Simulation method, device and equipment of quantum chip layout and storage medium

Abstract

The present disclosure provides a simulation method and device for a quantum chip layout, equipment and a storage medium, relates to the field of computers, in particular to the field of quantum computing and quantum simulation. The specific implementation scheme is as follows: pre-simulation is performed on a quantum chip layout to obtain a target mapping relationship corresponding to the quantum chip layout, wherein the target mapping relationship represents the correspondence between a quantum device and an eigenfrequency; the quantum device is one of a plurality of quantum devices included in the quantum chip layout; based on the target mapping relationship representing the quantum device and the eigenfrequency, and task information of a simulation task, a pre-simulation result is obtained, wherein the pre-simulation result includes simulation parameters required by the simulation task.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to the fields of quantum computing and quantum simulation technology. Background Technology

[0002] Similar to the development path of classical chips, expanding the number of qubits in superconducting quantum chips not only places higher demands on micro- and nano-fabrication processes, but also makes the simulation of superconducting quantum chips increasingly indispensable before formal fabrication. It should be noted that the simulation of superconducting quantum chips aims to characterize the chip's features as realistically as possible, enabling researchers to better predict chip performance during the design phase and reduce the material, human, and time costs of repeated experiments. Summary of the Invention

[0003] This disclosure provides a method, apparatus, device, and storage medium for simulating quantum chip layouts.

[0004] According to one aspect of this disclosure, a simulation method for quantum chip layout is provided, comprising:

[0005] A pre-simulation of the quantum chip layout is performed to obtain the target mapping relationship corresponding to the quantum chip layout, wherein the target mapping relationship characterizes the correspondence between quantum devices and intrinsic frequencies; the quantum device is one of multiple quantum devices included in the quantum chip layout;

[0006] Based on the target mapping relationship between the quantum device and the intrinsic frequency, and the task information of the simulation task, the pre-simulation results are obtained, wherein the pre-simulation results include the simulation parameters required by the simulation task.

[0007] According to another aspect of this disclosure, a simulation apparatus for a quantum chip layout is provided, comprising:

[0008] The processing unit is used to perform pre-simulation on the quantum chip layout to obtain the target mapping relationship corresponding to the quantum chip layout, wherein the target mapping relationship characterizes the correspondence between quantum devices and intrinsic frequencies; the quantum device is one of multiple quantum devices included in the quantum chip layout;

[0009] The result determination unit is used to obtain pre-simulation results based on the target mapping relationship between the quantum device and the intrinsic frequency, and the task information of the simulation task. The pre-simulation results include the simulation parameters required by the simulation task.

[0010] According to another aspect of this disclosure, a computing device is provided, comprising:

[0011] At least one quantum processing unit (QPU);

[0012] A memory, coupled to the at least one QPU and used to store executable instructions,

[0013] The instruction is executed by the at least one QPU to enable the at least one QPU to perform the method described above;

[0014] Or, including:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described above.

[0018] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions that, when executed by at least one quantum processing unit, cause the at least one quantum processing unit to perform the method described above.

[0019] Alternatively, the computer instructions may be used to cause the computer to perform the methods described above.

[0020] According to another aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by at least one quantum processing unit, implements the methods described above.

[0021] Alternatively, the computer program may implement the above-described method when executed by a processor.

[0022] In this way, the proposed solution obtains the target mapping relationship through pre-simulation, and then obtains the pre-simulation results based on the target mapping relationship and the task information of the simulation task, so as to obtain the simulation parameters required for the simulation task. This lays the foundation for the subsequent automated and efficient operation of the simulation task, and also lays the foundation for improving the efficiency of subsequent simulations. Moreover, the solution is simple and easy to implement, has a low threshold for use, and is practical.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0024] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0025] Figure 1This is a schematic diagram of the implementation flow of the simulation method for quantum chip layout according to embodiments of this disclosure. Figure 1 ;

[0026] Figure 2 This is a schematic diagram of the implementation flow of the simulation method for quantum chip layout according to embodiments of this disclosure. Figure 2 ;

[0027] Figure 3 This is a schematic diagram of a quantum chip layout including a qubit-coupler-qubit according to an embodiment of the present disclosure;

[0028] Figure 4 This is a schematic diagram of the implementation flow of the simulation method for quantum chip layout according to the embodiments of this disclosure in a specific embodiment. Figure 1 ;

[0029] Figure 5 This is a schematic diagram of the implementation flow of the simulation method for quantum chip layout according to the embodiments of this disclosure in a specific embodiment. Figure 2 ;

[0030] Figure 6 This is a schematic diagram of the quantum chip layout of a superconducting quantum chip according to an embodiment of the present disclosure;

[0031] Figure 7 This is a schematic diagram of the structure of a simulation device for a quantum chip layout according to an embodiment of this disclosure;

[0032] Figure 8 This is a block diagram of a computing device used to implement a simulation method for a quantum chip layout according to embodiments of the present disclosure. Detailed Implementation

[0033] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0034] In this document, the term "and / or" merely describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The term "at least one" in this document indicates any combination of at least two of a plurality of elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C. The terms "first" and "second" in this document refer to and distinguish between multiple similar technical terms, not to restrict the order or to limit there to only two. For example, "first feature" and "second feature" refer to two categories / two features; the first feature can be one or more, and the second feature can also be one or more.

[0035] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can still be practiced even without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0036] As a logical inevitable breakthrough in chip size beyond the limits of classical physics and a landmark technology of the post-Moore's Law era, quantum computing has garnered significant attention. Currently, quantum computing is developing rapidly at the application, algorithm, and hardware levels. It is worth noting that the realization of quantum algorithms and applications is highly dependent on the development and progress of quantum hardware. Several different technological solutions exist for quantum hardware implementation, such as superconducting quantum circuits, ion traps, and optical quantum systems. Benefiting from its excellent scalability and mature semiconductor manufacturing processes, superconducting quantum circuits are considered one of the most promising technological routes. Furthermore, in recent years, with the development of superconducting quantum computing technologies and micro / nano fabrication processes, the number of qubits integrated on superconducting quantum chips is increasing, leading to richer and more comprehensive chip structures.

[0037] Similar to the development path of classical chips, expanding the number of qubits in superconducting quantum chips not only places higher demands on micro- and nano-fabrication processes, but also makes the simulation of superconducting quantum chips increasingly indispensable before formal fabrication. It should be noted that the simulation of superconducting quantum chips aims to characterize the chip's features as realistically as possible, enabling researchers to better predict chip performance during the design phase and reduce the material, human, and time costs of repeated experiments.

[0038] In the design and simulation of quantum chip layouts, the simulation settings typically vary significantly depending on the specific simulation task. The simulation process requires setting simulation parameters (such as the simulation start frequency and the number of modes to be simulated). These parameters need to be set based on the number of quantum devices contained in the quantum chip layout (e.g., the layout of a superconducting quantum chip) and the distribution of their eigenmodes. For example, simulation parameters can be determined through pre-simulation of the quantum chip layout. This pre-simulation process involves: first, performing a rough pre-simulation of the quantum chip layout; second, based on the electromagnetic field energy distribution obtained from the pre-simulation, determining the types of all eigenmodes of the quantum chip layout within a certain frequency range. Here, eigenmode types are mainly categorized as the eigenmodes of the quantum devices, parasitic modes of the quantum devices, and frequency harmonics of the quantum devices. This pre-simulation process determines the approximate range of the eigenmodes corresponding to each quantum device. Based on this information and the specific simulation task, the required simulation parameters (i.e., the simulation parameters mentioned above) can be determined. Finally, subsequent high-precision simulations are performed based on the determined simulation parameters.

[0039] However, the industry primarily performs the aforementioned pre-simulation process manually, especially the identification and matching of intrinsic modes, which is mainly determined by observing the distribution of electromagnetic field energy. It is foreseeable that as the scale of quantum chips increases, this process will become cumbersome and inefficient. Therefore, there is an urgent need for a highly automated and practical method to improve the efficiency of the pre-simulation process.

[0040] Based on this, the present disclosure proposes a pre-simulation method for quantum chip layout to efficiently realize automated pre-simulation in quantum chip layout.

[0041] Specifically, Figure 1 This is a schematic diagram of the implementation flow of the simulation method for quantum chip layout according to embodiments of this disclosure. Figure 1 This method can be optionally applied to quantum computing devices that also have classical computing capabilities, or it can be applied to classical computing devices that also have quantum computing capabilities, or it can be directly applied to classical computing devices, such as personal computers, servers, server clusters and other electronic devices with classical computing capabilities, or it can be directly applied to quantum computers. This disclosure does not impose any restrictions on this method.

[0042] Furthermore, the method includes at least a portion of the following: (e.g.) Figure 1 As shown, it includes:

[0043] Step S101: Perform pre-simulation on the quantum chip layout to obtain the target mapping relationship corresponding to the quantum chip layout.

[0044] Here, the target mapping relationship characterizes the correspondence between quantum devices and intrinsic frequencies; the quantum device is one of the multiple quantum devices included in the quantum chip layout.

[0045] Step S102: Based on the target mapping relationship between the quantum device and the intrinsic frequency, and the task information of the simulation task, the pre-simulation results are obtained.

[0046] Here, the pre-simulation results include the simulation parameters required for the simulation task.

[0047] In this way, the proposed solution obtains the target mapping relationship through pre-simulation, and then obtains the pre-simulation results based on the target mapping relationship and the task information of the simulation task, so as to obtain the simulation parameters required for the simulation task. This lays the foundation for the subsequent automated and efficient operation of the simulation task, and also lays the foundation for improving the efficiency of subsequent simulations. Moreover, the solution is simple and easy to implement, has a low threshold for use, and is practical.

[0048] It should be noted that the pre-simulation of the quantum chip layout described in this disclosure is a step before executing the simulation task; that is, by pre-simulating the quantum chip layout, the simulation parameters required for subsequent simulation tasks can be obtained. In other words, the pre-simulation method can reasonably and automatically provide the required simulation parameters for subsequent simulation tasks. This can save a lot of manpower, material resources and computing resources, greatly improve the automation level of simulation task execution, and thus comprehensively improve the efficiency of simulation.

[0049] In one specific example, the quantum chip layout includes, but is not limited to, quantum bits, couplers, readout cavities, etc.

[0050] Furthermore, in a specific example, the quantum chip can be a superconducting quantum chip; here, the superconducting quantum chip refers to a quantum chip made of superconducting materials. For example, all components in the superconducting quantum chip (such as qubits, coupling devices, etc.) are made of superconducting materials. This allows the disclosed solution to be applied to superconducting quantum chips, enriching the application scenarios of the disclosed solution.

[0051] In a specific example of the scheme disclosed herein, the simulation parameters are at least one of the following: the target simulation start frequency, and the number of target frequencies required for the simulation task.

[0052] Here, the target simulation start frequency is less than or equal to the target minimum eigenfrequency, which is the minimum value among the eigenfrequencys of each of the at least two target quantum devices to be simulated in the simulation task.

[0053] Furthermore, the number of target frequencies is used to determine a plurality of target simulation frequencies starting from the target simulation start frequency, the plurality of target simulation frequencies including the intrinsic frequencies of the target quantum device to be simulated by the simulation task.

[0054] Thus, by obtaining the target simulation start frequency and / or the number of target frequencies required for the simulation task, this disclosed solution provides support for the effective operation of the simulation task in the future.

[0055] In a specific example of the scheme disclosed herein, the target simulation start frequency is less than or equal to the class minimum eigenfrequency corresponding to the target quantum device. The class minimum eigenfrequency corresponding to the target quantum device represents the minimum value of the eigenfrequency of each quantum device in the same device type as the target quantum device. The class minimum eigenfrequency corresponding to the target quantum device is less than or equal to the target minimum eigenfrequency.

[0056] In a specific example, the quantum chip layout may contain multiple types of quantum devices, such as qubits, couplers, readout cavities, etc., according to their functions. It is understood that in practical applications, quantum devices can also be classified in other ways. This disclosure does not impose specific restrictions on the classification of types.

[0057] Thus, this disclosed scheme further refines the value of the target simulation start frequency, thereby adapting to different simulation tasks and making it more versatile.

[0058] In a specific example of the scheme disclosed herein, the intrinsic frequencies in the target mapping relationship can be sorted first, and then the number of target frequencies can be obtained based on the sorted ordered sequence of targets; specifically, the method further includes:

[0059] The intrinsic frequencies contained in the target mapping relationship are sorted to obtain an ordered sequence of targets;

[0060] Furthermore, after obtaining the target ordered sequence, the number of target frequencies is obtained in the following manner:

[0061] Based on the task information from the simulation task and the position of the quantum device in the target ordered sequence, the number of target frequencies is obtained.

[0062] For example, for the current simulation task, the intrinsic frequencies of all target quantum devices required for the simulation task can be obtained, and then the number of target frequencies can be obtained based on the position of the minimum target intrinsic frequency in the target ordered sequence and the position of the maximum target intrinsic frequency in the target ordered sequence.

[0063] Here, the target minimum eigenfrequency is the minimum value among the eigenfrequencys of all target quantum devices to be simulated in the simulation task. Similarly, the target maximum eigenfrequency is the maximum value among the eigenfrequencys of all target quantum devices to be simulated in the simulation task. For specific examples, please refer to the relevant content of step S503 in Part III below, which will not be repeated here.

[0064] Thus, this disclosed solution provides a specific and general method for obtaining the number of target frequencies, thereby laying the foundation for effectively obtaining simulation parameters and running simulation tasks.

[0065] In a specific example of the disclosed scheme, the target frequency quantity is obtained from the above-mentioned task information based on the simulation task and the position of the quantum device in the target ordered sequence, including:

[0066] When the simulation task is to obtain the eigenfrequency of a qubit, determine the position of the class minimum eigenfrequency (e.g., the class minimum eigenfrequency of a qubit) of the target quantum device in the target ordered sequence, and determine the position of the class maximum eigenfrequency (e.g., the class maximum eigenfrequency of a qubit) of the target quantum device in the target ordered sequence.

[0067] The number of target frequencies is obtained based on the position of the minimum eigenfrequency of the target quantum device in the target ordered sequence and the position of the maximum eigenfrequency of the target quantum device in the target ordered sequence.

[0068] Here, the class maximum eigenfrequency corresponding to the target quantum device represents the maximum value of the eigenfrequency of each quantum device in the same device type as the target quantum device; for example, the class maximum eigenfrequency corresponding to a qubit represents the maximum value of the eigenfrequency of each qubit in the quantum chip layout.

[0069] For specific examples, please refer to the relevant description of Simulation Task 1 below, which will not be repeated here.

[0070] Thus, the present disclosure provides a feasible solution for obtaining the required number of target frequencies under a specific simulation task. This solution is simple, requires no complex calculations, saves time, further improves processing efficiency, and provides support for the effective operation of subsequent simulation tasks.

[0071] In a specific example of the scheme disclosed herein, obtaining the target frequency quantity based on the task information of the simulation task and the position of the quantum device in the target ordered sequence includes:

[0072] Given that the simulation task is to obtain the dispersion ratio of the quantum system formed by the qubit-coupler-qubit, and that the eigenfrequency of the qubit is less than the eigenfrequency of the coupler, determine the position of the class minimum eigenfrequency of the coupler in the target ordered sequence, and determine the position of the class maximum eigenfrequency of the qubit in the target ordered sequence.

[0073] The number of target frequencies is obtained based on the position of the minimum eigenfrequency of the coupler in the target ordered sequence and the position of the maximum eigenfrequency of the qubit in the target ordered sequence.

[0074] Here, it is understood that in this example, the plurality of quantum devices include at least one coupler and at least two qubits, and adjacent qubits are connected by the coupler.

[0075] Furthermore, the minimum eigenfrequency of the coupler is the minimum eigenfrequency among all couplers in the quantum chip layout; correspondingly, the maximum eigenfrequency of the qubit is the maximum eigenfrequency among all qubits in the quantum chip layout.

[0076] For specific examples, please refer to the relevant description of Simulation Task 2 below, which will not be repeated here.

[0077] Thus, the present disclosure provides a feasible solution for obtaining the required number of target frequencies under a specific simulation task. This solution is simple, requires no complex calculations, saves time, further improves processing efficiency, and provides support for the effective operation of subsequent simulation tasks.

[0078] In a specific example of the disclosed scheme, the target frequency quantity is obtained from the above-mentioned task information based on the simulation task and the position of the quantum device in the target ordered sequence, including:

[0079] Given that the simulation task is to obtain the coupling strength between the qubit and its corresponding readout cavity, determine the position of the class minimum eigenfrequency of the readout cavity in the target ordered sequence, and determine the position of the class minimum eigenfrequency of the qubit in the target ordered sequence.

[0080] The number of target frequencies is obtained based on the position of the class minimum eigenfrequency corresponding to the readout cavity in the target ordered sequence, and the position of the class minimum eigenfrequency corresponding to the qubit in the target ordered sequence.

[0081] Here, it can be understood that in this example, the plurality of quantum devices include at least one qubit and at least one readout cavity; for example, each qubit is provided with a readout cavity, so that the quantum correlation data of the qubit can be read through the readout cavity.

[0082] Furthermore, the class minimum eigenfrequency corresponding to the readout cavity is the minimum value among the eigenfrequencys of all readout cavities in the quantum chip layout; correspondingly, the class minimum eigenfrequency corresponding to the qubit is the minimum value among the eigenfrequencys of all qubits in the quantum chip layout.

[0083] For specific examples, please refer to the relevant description of simulation task three below, which will not be repeated here.

[0084] Thus, the present disclosure provides a feasible solution for obtaining the required number of target frequencies under a specific simulation task. This solution is simple, requires no complex calculations, saves time, further improves processing efficiency, and provides support for the effective operation of subsequent simulation tasks.

[0085] In a specific example of the scheme disclosed herein, Figure 2 This is a schematic diagram of the implementation flow of the simulation method for quantum chip layout according to embodiments of this disclosure. Figure 2 This method can be optionally applied to quantum computing devices that also possess classical computing capabilities, or it can be applied to classical computing devices that also possess quantum computing capabilities, or it can be directly applied to classical computing devices, such as personal computers, servers, server clusters, and other electronic devices with classical computing capabilities, or it can be directly applied to quantum computers. This disclosure does not impose any limitations on these applications. It is understood that the above... Figure 1 The methods shown can also be applied to this example, and the related content will not be elaborated further in this example.

[0086] Furthermore, the method includes at least a portion of the following: (e.g.) Figure 2 As shown, it includes:

[0087] Step S201: Perform pre-simulation on the quantum chip layout to obtain the target mapping relationship corresponding to the quantum chip layout.

[0088] Here, the target mapping relationship characterizes the correspondence between quantum devices and intrinsic frequencies; the quantum device is one of the multiple quantum devices included in the quantum chip layout.

[0089] Step S202: Based on the target mapping relationship between the quantum device and the intrinsic frequency, and the task information of the simulation task, the pre-simulation results are obtained.

[0090] Here, the pre-simulation results include the simulation parameters required for the simulation task.

[0091] Step S203: Input the simulation parameters and the task information of the simulation task into the preset simulation system to obtain the simulation results corresponding to the simulation task.

[0092] In this way, the disclosed solution automatically obtains the target mapping relationship through the pre-simulation process and determines the simulation parameters required for the simulation task. Then, the simulation parameters and the task information of the simulation task are used as inputs to obtain the simulation results. This greatly improves the automation level of the simulation task execution, saves a lot of manpower, material resources and computing resources, and thus comprehensively improves the simulation efficiency.

[0093] In a specific example of the scheme disclosed herein, the target mapping relationship can be obtained in the following manner. Specifically, the above-mentioned pre-simulation of the quantum chip layout to obtain the target mapping relationship corresponding to the quantum chip layout (i.e., step S101 or step S201) specifically includes:

[0094] Step S1011: Perform pre-simulation on the quantum chip layout to obtain information on multiple intrinsic modes.

[0095] In a specific example, the pre-simulation of the quantum chip layout to obtain multiple eigenmode information specifically includes: performing electromagnetic simulation on the quantum chip layout with the initial simulation frequency at a preset minimum value and the number of initial pre-simulation frequencies at a preset number, thereby obtaining multiple eigenmode information. This provides a specific initialization process to ensure that the pre-simulation can obtain the simulation parameters required for the simulation task. Furthermore, after initialization, electromagnetic simulation is used to implement the pre-simulation process, thus laying the foundation for automating the pre-simulation process and improving simulation efficiency.

[0096] Furthermore, it is understandable that in practical applications, the simulation accuracy can be set (for example, the minimum value can be preset to be within the range of 1%-5%), and then electromagnetic simulation of the quantum chip layout can be performed under the preset simulation accuracy. This improves the accuracy of the pre-simulation results and lays the foundation for efficiently completing the simulation task.

[0097] Step S1012: Based on multiple intrinsic mode information, obtain the intrinsic frequencies of each quantum device in the quantum chip layout.

[0098] In a specific example, each eigenmode information corresponds to an eigenmode. In this case, the eigenmode information of the quantum device can specifically include the eigenfrequency of the quantum device within its eigenmode. Thus, based on the eigenmode information of the quantum device, the eigenfrequency of the quantum device can be obtained.

[0099] Step S1013: Obtain the target mapping relationship based on the eigenfrequency of each quantum device.

[0100] Thus, this disclosure provides a specific scheme for obtaining the eigenfrequency of each quantum device through pre-simulation, and then obtaining the target mapping relationship. This lays the foundation for the subsequent automated and efficient operation of simulation tasks, and also lays the foundation for improving the efficiency of subsequent simulations. Moreover, this scheme is simple and easy to implement, has a low threshold for use, and is practical.

[0101] It should be noted that in practical applications, when designing quantum chip layouts, the eigenfrequency of all quantum devices (such as qubits) may be very close and concentrated at a certain frequency. In this case, the eigenfrequency of each qubit can be efficiently and accurately identified using the scheme disclosed herein.

[0102] In a specific example of the disclosed scheme, the intrinsic frequencies of each quantum device can also be obtained in the following manner. Specifically, the above-described method of obtaining the intrinsic frequencies of each quantum device in the quantum chip layout based on multiple intrinsic mode information (i.e., step S1011) specifically includes:

[0103] Based on multiple intrinsic mode information, the inductance energy ratio of the quantum device under different intrinsic modes is obtained; wherein, the intrinsic mode information in the multiple intrinsic mode information corresponds to an intrinsic mode;

[0104] Based on the proportion of inductor energy in different eigenmodes of the quantum device, the eigenfrequency of the quantum device is identified from the information of the multiple eigenmodes.

[0105] Thus, this disclosure provides a specific scheme for identifying the intrinsic frequencies of a quantum device from the multiple intrinsic mode information, and the process is automated. Compared with the existing method of manually matching quantum devices with intrinsic frequencies, this disclosure greatly shortens the matching time and improves efficiency, thereby improving the ease of execution and simulation efficiency of subsequent simulation tasks.

[0106] Furthermore, in a specific example, the identification of the eigenfrequency of the quantum device from the multiple eigenmode information based on the inductor energy ratio of the quantum device in different eigenmodes, as described above, specifically includes:

[0107] The target inductance energy percentage of the quantum device is determined from the inductance energy percentage of the quantum device in different intrinsic modes;

[0108] Based on the multiple intrinsic mode information, the intrinsic mode information corresponding to the target inductance energy ratio of the quantum device is determined;

[0109] The intrinsic frequency of the quantum device is obtained based on the intrinsic mode information corresponding to the target inductance energy ratio of the quantum device.

[0110] In other words, from the calculated inductance energy percentages of the quantum device under different eigenmodes, the required inductance energy percentage is selected as the target inductance energy percentage of the quantum device; then, the eigenmode information corresponding to the target inductance energy percentage of the quantum device is used as the eigenmode information of the quantum device to obtain the eigenfrequency of the quantum device. This achieves an automated matching process between the quantum device and its eigenmode information.

[0111] Thus, this disclosure provides a specific method for obtaining the intrinsic frequency of a quantum device, which is simple, easy to implement, and highly practical.

[0112] Furthermore, in a specific example, the target inductance energy percentage can be obtained in the following manner. Specifically, determining the target inductance energy percentage of the quantum device from the inductance energy percentages of the quantum device in different eigenmodes, as described above, specifically includes: selecting the maximum percentage from the inductance energy percentages corresponding to the quantum device in different eigenmodes, wherein the target inductance energy percentage is the maximum percentage.

[0113] In other words, among the calculated inductance energy percentages of the quantum device under different intrinsic modes, the inductance energy percentage with the largest value is selected as the target inductance energy percentage of the quantum device, so as to determine the intrinsic mode information of the quantum device.

[0114] Thus, this disclosure provides a specific scheme for obtaining the target inductance energy ratio of a quantum device. Moreover, the scheme is simple and easy to implement, has strong practicality, and is highly interpretable.

[0115] Furthermore, in a specific example, information based on multiple intrinsic modes can be obtained in the following manner. Specifically, the inductance energy ratio of the quantum device in different intrinsic modes, as described above, can be obtained based on multiple intrinsic mode information, including:

[0116] The inductance energy percentage p of quantum device n in intrinsic mode m among the plurality of quantum devices is obtained as follows: mn :

[0117] Based on information from multiple intrinsic modes, the inductance energy of intrinsic mode m stored in quantum device n is obtained. And obtain the total inductance energy stored in the intrinsic mode m. Wherein, the intrinsic mode information corresponding to intrinsic mode m is one of the plurality of intrinsic mode information;

[0118] The inductor energy stored in quantum device n based on the intrinsic mode m and the total inductance energy stored in the intrinsic mode m The inductance energy percentage p of quantum device n in intrinsic mode m is obtained. mn .

[0119] In other words, in this example, the inductance energy of eigenmode m stored in quantum device n is first obtained based on information from multiple eigenmodes. And obtain the total inductance energy stored in the intrinsic mode m. The inductor energy stored in quantum device n based on the intrinsic mode m and the total inductance energy stored in the intrinsic mode m For example, based on the ratio of the two, the inductance energy percentage p of quantum device n in eigenmode m can be obtained. mn In this way, the inductance energy ratio of quantum devices in different intrinsic modes can be obtained.

[0120] For example, the inductance energy percentage of quantum device n in its intrinsic mode m can be obtained by the following specific expression: p mn :

[0121]

[0122] Furthermore, based on the above formula, the inductor energy percentage of the quantum device in multiple eigenmodes can be obtained. For example, if simulation of the quantum chip layout yields M (positive integers greater than or equal to 2) eigenmodes, and each eigenmode corresponds to a single eigenmode, then the inductor energy percentage of the quantum device in the M eigenmodes can be obtained, which can be denoted as p. 1n ,p 2n ,…,p mn ,…,p Mn Thus, based on the energy percentage of the M inductors, the eigenmode information of quantum device n can be obtained, and consequently, the eigenfrequency of quantum device n can be obtained.

[0123] This provides a method for obtaining the inductance energy percentage p of quantum device n in its eigenmode m. mn The specific scheme is as follows: based on this scheme, the inductance energy ratio of quantum device n in different eigenmodes can be obtained, so as to obtain the eigenfrequency of quantum device n, and then the eigenfrequency of all quantum devices can be obtained, so as to finally obtain the target mapping relationship.

[0124] Furthermore, in a specific example, after obtaining the inductor energy percentage p... mn In the process, inductor energy can be obtained in the following ways. That is, based on the information from multiple intrinsic modes described above, the inductance energy of intrinsic mode m stored in quantum device n is obtained. Specifically, it includes:

[0125] Based on information from multiple intrinsic modes, the inductance value L of quantum device n is calculated. n And the voltage (i.e. peak voltage) V of the eigenmode m along the voltage integral line of the quantum device n in space is calculated. mn ;

[0126] Based on the inductance value L of quantum device n n The intrinsic mode m represents the voltage V along the voltage integral line of the quantum device n in space. mn and the eigenfrequency ω' corresponding to the eigenmode m m The inductance energy of intrinsic mode m stored in quantum device n is obtained.

[0127] It should be noted that the eigenmode information obtained from finite element electromagnetic simulation (e.g., high-frequency electromagnetic field simulation) of the quantum chip layout includes the eigenfrequency corresponding to the eigenmode. Furthermore, the eigenmode information corresponding to eigenmode m in the plurality of eigenmode information includes the eigenfrequency ω' corresponding to eigenmode m. m .

[0128] For example, in a specific instance, the intrinsic mode m is stored in the inductance energy of the quantum device n. It can be obtained through the following formula:

[0129]

[0130] in, This represents the magnetic flux of quantum device n in its intrinsic mode m.

[0131] Thus, this disclosure provides a method for obtaining the inductor energy of the intrinsic mode m stored in the quantum device n. This specific scheme provides quantifiable data support for the subsequent automated and rapid acquisition of intrinsic mode information of quantum devices. Moreover, the scheme is simple to implement, highly interpretable, and has strong practicality.

[0132] Furthermore, in a specific example, after obtaining the inductor energy percentage p... mn During the process, the inductance value L of the quantum device n can be obtained in the following way. n That is, based on the information from multiple intrinsic modes mentioned above, the inductance value L of the quantum device n is calculated. n ,include:

[0133] The correlation between the inductance energy ratio of quantum device n in different intrinsic modes was obtained;

[0134] Based on the correlation between the inductance energy ratios of quantum device n under different eigenmodes, the inductance value L of quantum device n is obtained.n .

[0135] For example, based on the normalization relation, the correlation between the inductance energy percentage of quantum device n in different eigenmodes can be specifically as follows:

[0136]

[0137] Here, for the M eigenmodes, m takes values ​​from 1 to M.

[0138] Furthermore, based on the above correlation, the inductance value L of the quantum device n can be obtained. n The specific expression is:

[0139]

[0140] Thus, this disclosed method provides a way to obtain the inductance value L of the quantum device n. n This specific scheme provides quantifiable data support for the subsequent automated and rapid acquisition of intrinsic mode information of quantum devices. Moreover, the scheme is simple to implement, highly interpretable, and has strong practicality.

[0141] Furthermore, in a specific example, after obtaining the inductor energy percentage p... mn During the process, the voltage V of the eigenmode m along the voltage integral line of the quantum device n in space can be obtained in the following way. mn That is, based on the information of multiple intrinsic modes mentioned above, the voltage V of intrinsic mode m along the voltage integral line of quantum device n in space is calculated. mn Specifically, it includes:

[0142] Based on the electric field intensity distribution information of intrinsic mode m in space, the voltage V of quantum device n along the voltage integral line in space is calculated. mn Among them, the intrinsic mode information corresponding to intrinsic mode m includes the electric field intensity distribution information of intrinsic mode m in space.

[0143] It should be noted that the eigenmode information obtained from finite element electromagnetic simulation (e.g., high-frequency electromagnetic field simulation) of the quantum chip layout may also include the electric field intensity distribution information of the eigenmode in space. Furthermore, the eigenmode information corresponding to eigenmode m among the multiple eigenmode information includes the electric field intensity distribution information of eigenmode m in space, such as the peak electric field intensity distribution.

[0144] For example, based on the peak electric field distribution of eigenmode m in space The voltage (i.e., peak voltage) V of the eigenmode m along the voltage integral line of the quantum device n in space can be obtained using the following formula.mn :

[0145]

[0146] here, This represents the voltage integral line vector of quantum device n in the quantum chip layout. For known terms, The length can be determined by the voltage integration line added during preprocessing, the voltage integration line vector. The direction can be determined based on the default positive direction of the coordinate system in which the quantum chip layout is located. The location vector represents the peak distribution of the electric field intensity.

[0147] Thus, this disclosure provides a method for obtaining the voltage V of the eigenmode m along the voltage integral line of the quantum device n in space. mn This specific scheme provides quantifiable data support for the subsequent automated and rapid acquisition of intrinsic mode information of quantum devices. Moreover, the scheme is simple to implement, highly interpretable, and has strong practicality.

[0148] Furthermore, in a specific example, after obtaining the inductor energy percentage p... mn During the process, the total inductance energy stored in the eigenmode m can be obtained in the following way. That is, based on the information from multiple intrinsic modes described above, the total inductance energy stored in intrinsic mode m is obtained. Specifically, it includes:

[0149] Based on the electric field intensity distribution information of eigenmode m in space, the average electric field energy stored in space by eigenmode m is obtained. Among them, the intrinsic mode information corresponding to intrinsic mode m includes the electric field intensity distribution information of intrinsic mode m in space;

[0150] The average electric field energy stored in space for the eigenmode m The total inductance energy stored in the intrinsic mode m

[0151] For example, the average electric field energy stored in space by the eigenmode m It can be obtained through the following formula:

[0152]

[0153] here, The dielectric tensor, v, represents the dielectric tensor at different locations in space. full The volume of the space is represented by the above quantities, all of which are known quantities.

[0154] Furthermore, the average electric field energy stored in space for the eigenmode m The total inductance energy stored in the intrinsic mode m That is to say

[0155] Thus, the present disclosure provides a method for obtaining the total inductance energy stored in the eigenmode m. This specific scheme provides quantifiable data support for the subsequent automated and rapid acquisition of intrinsic mode information of quantum devices. Moreover, the scheme is simple to implement, highly interpretable, and has strong practicality.

[0156] The following detailed explanation of this disclosure, with specific examples, further illustrates the proposed solution. Based on the inductance energy participation ratio (iEPR), this disclosure proposes an automated pre-simulation method for quantum chip layouts to determine the simulation parameters required for the simulation task. Specifically, by inputting the quantum chip layout to be simulated and the task type, the pre-simulation can be automatically completed, and the simulation parameters for that task (such as the target simulation start frequency and the number of target frequencies required for the simulation task) can be output. Furthermore, experimental verification shows that this disclosure method can significantly improve the pre-simulation efficiency of quantum chip layouts, providing important guidance for the automated design of quantum chips (such as superconducting quantum chips).

[0157] The following section uses a superconducting quantum chip as an example and elaborates on the present invention in five parts. The first part mainly introduces the quantum chip layout of the superconducting quantum chip and the simulation task. The second part describes the intrinsic frequency identification method based on iEPR. The third part describes the specific process of pre-simulation of the present invention. The fourth part provides a detailed explanation of the present invention in conjunction with different simulation tasks. The fifth part demonstrates the application to verify the effectiveness of the present invention.

[0158] Part 1: Quantum Chip Layout and Simulation Task Types

[0159] Similar to classical chips, a complete quantum chip layout for superconducting quantum chips needs to be designed before formal production and fabrication, and simulation verification is required before tape-out. To ensure that the simulation results meet design expectations, appropriate simulation parameters need to be set for different simulation tasks. Understandably, the accuracy of simulation parameter settings is crucial for the completion of simulation tasks, especially as the scale of superconducting quantum chips increases, existing manual methods will require significantly more time. Therefore, highly automated and practical methods are needed to accurately and quickly determine the simulation parameters for the quantum chip layout.

[0160] It should be noted that the quantum chip layout of this disclosure includes multiple quantum devices, which include, but are not limited to, qubits, couplers, readout resonant cavities (i.e., readout cavities), filters, etc.

[0161] The following is a brief explanation of the quantum chip layout of a superconducting quantum chip. This layout includes all the core quantum devices, control lines, readout lines, etc., of the superconducting quantum chip. In practical applications, one of the most important quantum devices in a superconducting quantum chip is the qubit (qubit). A qubit is typically composed of a coplanar capacitor and a Josephson junction. In practice, a substrate (usually silicon or sapphire) is selected, an aluminum film is deposited on the substrate, and different shapes are etched into the aluminum film to form the capacitance of the qubit. Finally, a nonlinear device, such as a Josephson junction, is placed between the two substrates and the aluminum film. For example, in one example, such as... Figure 3 The diagram shown is a schematic of the layout of a quantum chip consisting of a qubit-coupler-qubit configuration. This quantum chip layout includes:

[0162] A qubit (Qubit) 301, for example, has a cross-shaped structure;

[0163] Coupler 302, for example, is a rectangular structure;

[0164] Reading cavity 303, for example, is a triangular or serpentine structure;

[0165] The qubits 301 are connected to each other via couplers 302, and each qubit 301 is also connected to a corresponding readout cavity 303. The readout cavity 303 is connected to a readout port, which is then connected to a readout line 304 to facilitate the reading of quantum correlation data of the qubits 301 through the readout cavity 303.

[0166] Furthermore, in a specific example, such as Figure 1 As shown, both the quantum bit 301 and the coupler 302 are equipped with Josephson junctions 305. For example, a Josephson junction 305 can be placed below a cross-shaped structure or below a rectangular structure. Here, in actual electromagnetic simulations, the Josephson junction can be represented by its equivalent inductance.

[0167] Furthermore, when designing and verifying the layout of a quantum chip, a series of simulation tasks are typically set up based on the characteristics of the quantum device. The simulation tasks of the scheme disclosed herein include, but are not limited to, one of the following:

[0168] Simulation Task 1: Simulate the eigenmodes of the qubit, that is, simulate the eigenfrequency of the qubit; the eigenmodes are electromagnetic properties of the qubit itself.

[0169] Simulation Task 2: Simulate and obtain the cut-off point between qubits in the "qubit-coupler-qubit" system, that is, the coupling strength between qubits in the "qubit-coupler-qubit" system. Here, for the "qubit-coupler-qubit" system, when the coupling strength (e.g., the equivalent coupling strength) between two qubits is 0, the inductance value or frequency value of the coupler at this time can be used as the cut-off point between qubits in the "qubit-coupler-qubit" system.

[0170] It should be noted that in simulation task 2, the two qubits in the "qubit-coupler-qubit" are adjacent, and the two adjacent qubits are connected by a coupler.

[0171] Simulation Task 3: Simulate the coupling strength of the "qubit-reading cavity (QR)" to ensure that the reading cavity can accurately read the quantum data information of the qubit. There is usually a certain coupling strength between two quantum devices. For the qubit-reading cavity, under the combined effect of other quantum devices on the qubit and the coupler in the qubit-reading cavity, the coupling strength between the qubit and the reading cavity (such as the equivalent coupling strength) determines the reading rate of the reading cavity and the fidelity of the qubit. Therefore, the coupling strength between the qubit and the reading cavity needs to be within a suitable range, which is the significance of simulating the coupling strength between the qubit and the reading cavity.

[0172] It should be noted that in simulation task 3, "qubit-reading cavity (QR)" refers to the qubit and its corresponding reading cavity. Therefore, simulation task 3 aims to simulate the coupling strength between the qubit and its corresponding reading cavity.

[0173] Simulation Task 4: Simulate and obtain the dispersion ratio of the quantum system formed by "qubit-coupler-qubit". This dispersion ratio is used to measure whether dispersion coupling between qubits in the quantum system formed by qubit-coupler is realized.

[0174] This dispersion ratio, denoted as β, satisfies the following relationship:

[0175]

[0176] Here, g qc ω represents the coupling strength between the qubit and the coupler in the "qubit-coupler-qubit" model; c ω represents the eigenfrequency of the coupler. qThis represents the eigenfrequency of the qubit. In a specific simulation, the dispersion ratio can be calculated by obtaining the coupling strength between the qubit and the coupler at the opening point (such as the critical point when the coupling strength between the qubit and the coupler is greater than a preset threshold), as well as the eigenfrequency of the qubit and the eigenfrequency of the coupler.

[0177] For example, for "qubit Q1-coupler-qubit Q2", the dispersion ratio of "qubit-coupler-qubit" can be specifically defined as follows:

[0178]

[0179] Here, g q1c This represents the coupling strength between qubit Q1 and the coupler. Furthermore, in a specific simulation process, the dispersion ratio can be calculated by simulating the coupling strength between qubit Q1 and the coupler at the opening point (e.g., the critical point where the coupling strength between qubit Q1 and the coupler is greater than a preset threshold), as well as the eigenfrequency of qubit Q1 and the eigenfrequency of the coupler.

[0180] It should be noted that in simulation task 4, the two qubits in the "qubit-coupler-qubit" are adjacent, and the two adjacent qubits are connected by a coupler.

[0181] In addition, it should be noted that the simulation tasks described above are merely illustrative examples, and other simulation tasks may exist in actual applications, which are not limited in this disclosure.

[0182] Part Two: Pattern Recognition Method Based on iEPR, Program One

[0183] The program takes as input a quantum chip layout of a superconducting quantum chip and outputs a target mapping relationship corresponding to the quantum chip layout. This target mapping relationship characterizes the correspondence between quantum devices and intrinsic frequencies in the quantum chip layout.

[0184] like Figure 4 As shown, the specific steps of procedure one include:

[0185] Step S401: Input the quantum chip layout.

[0186] Step S402: Perform finite element electromagnetic simulation (e.g., high-frequency electromagnetic field simulation) to obtain multiple eigenmode information; wherein, the eigenmode information includes the eigenfrequency corresponding to the eigenmode and the electric field intensity distribution information corresponding to the eigenmode; for example, the obtained eigenmode information includes:

[0187] (1) The eigenfrequency ω' corresponding to the eigenmode m m ;

[0188] (2) Information on the electric field intensity distribution of the intrinsic mode m in space, such as the peak electric field intensity distribution. here, The location vector representing the peak distribution of the electric field intensity; the value of M is related to the total amount of intrinsic mode information.

[0189] Step S403: Calculate the iEPR of the quantum devices in different intrinsic modes among multiple quantum devices.

[0190] Specifically, the iEPR of quantum device n under different eigenmodes m in a plurality of quantum devices will be used as an example for detailed explanation; specifically, the iEPR of quantum device n under eigenmode m can be denoted as p. mn At this time, p mn It can be defined as:

[0191]

[0192] Furthermore, p can be calculated based on the following steps. mn Specifically, it includes:

[0193] Step S4031: Calculate the quantum device n along the intrinsic mode m.

[0194] The voltage V across the voltage integration line mn The specific expression is as follows:

[0195]

[0196] in, This represents the voltage integral line vector of quantum device n in the quantum chip layout. For known terms, The length can be determined by the voltage integration line added during preprocessing, the voltage integration line vector. The direction can be determined based on the default positive direction of the coordinate system in which the quantum chip layout is located.

[0197] Step S4032: Calculate the average inductance energy of eigenmode m on quantum device n in space, and use it as the inductance energy of eigenmode m on quantum device n in space. The specific expression is as follows:

[0198]

[0199] in, L n Let be the inductance value of quantum device n, and be an unknown term.

[0200] Step S4033: Calculate the average electric field energy stored in space for the eigenmode m (which can be denoted as...). The total inductance energy stored in the intrinsic mode m is expressed as follows:

[0201]

[0202] in, This represents the electric field energy of the eigenmode m in space. The dielectric tensor, v, represents the dielectric tensor at different locations in space. full The volume of the space is represented by the above quantities, all of which are known quantities.

[0203] Step S4034: Based on the normalization relation, the following formula exists:

[0204]

[0205] Step S4035: Obtain the inductance value L of quantum device n n ,Right now:

[0206]

[0207] Step S4036: Obtain p mn This leads to the iEPR of quantum device n under different eigenmodes. Here, p mn This can be specifically expressed as:

[0208]

[0209] In this way, the iEPR of quantum device n in different eigenmodes can be obtained.

[0210] Step S404: Based on the iEPR of the quantum device in different intrinsic modes, obtain the target iEPR of the quantum device, for example, obtain the maximum iEPR of the quantum device.

[0211] For example, let p be the maximum iEPR of quantum device n. sn ,but:

[0212]

[0213] Understandably, according to iEPR theory, in a superconducting quantum chip containing multiple quantum devices, for quantum device n, the following relationship exists:

[0214] p nn >p mm

[0215] That is, the proportion of inductance energy of quantum device n in its own eigenmode n is greater than the proportion of inductance energy of quantum device n in eigenmode m. Therefore, the proportion of inductance energy of quantum device n in different eigenmodes m can be calculated, and then the eigenfrequency of quantum device n can be obtained based on the eigenmode corresponding to the maximum value of the inductance energy proportion. For example, the eigenfrequency of the eigenmode corresponding to the maximum value can be directly used as the eigenfrequency of quantum device n to complete the identification and matching between quantum device and eigenmode.

[0216] Step S405: Based on the maximum iEPR of the quantum device, obtain the eigenfrequency of the quantum device; based on the eigenfrequency of each quantum device, obtain the target mapping relationship.

[0217] Step S406: Output the target mapping relationship.

[0218] Part Three: The specific steps for implementing the pre-simulation method, namely Program Two.

[0219] Program 2 is a pre-simulation method, which is also the main program of this disclosed solution.

[0220] The input for this example is: a quantum chip layout containing N quantum devices, and task information for the simulation task; the output is: the simulation parameters required for the simulation task.

[0221] like Figure 5 As shown, the implementation steps of this main program specifically include:

[0222] Step S501: Input the quantum chip layout containing N quantum devices, and the task information for the simulation task.

[0223] In a specific example, you can input a quantum chip layout, the identification information of quantum devices in the quantum chip layout, and the task information of the simulation task.

[0224] Step S502: Call program one to obtain the target mapping relationship corresponding to the quantum chip layout, wherein the target mapping relationship is the correspondence between quantum devices and intrinsic frequencies.

[0225] Specifically: initialization; for example, setting the initial simulation frequency to a preset minimum value, that is, a preset lowest frequency, and setting the number of initial frequencies for pre-simulation to a preset number, such as a preset maximum value (e.g., 20). After initialization, using program one to perform finite element electromagnetic simulation at a preset simulation accuracy to obtain multiple eigenmode information, and then obtain the target mapping relationship.

[0226] In practical applications, the intrinsic frequencies in the obtained intrinsic mode information can also be numbered according to the following rules, such as: freq[1],freq[2],…,freq[M]. Where M is a positive integer greater than or equal to 2, and its value is related to the total number of intrinsic mode information.

[0227] Furthermore, before numbering, the intrinsic frequencies in the obtained intrinsic mode information can be sorted and numbered to obtain the target ordered sequence, such as {freq[1],freq[2],…,freq[m],…freq[M]}. Here, m represents the position of the intrinsic frequency freq[m] in the target ordered sequence, i.e., the mth position.

[0228] It should be noted that the simulation accuracy is an empirical value, for example, within the range of 1%-5%. Understandably, in practical applications, the simulation accuracy should not be too high, as this will result in excessively long simulation times. Moreover, since this main program is a pre-simulation process, it does not require very high simulation accuracy. At the same time, the simulation accuracy should not be too low either, as this may result in incorrect pre-simulation results or pre-simulation results that are too far from the actual results.

[0229] Furthermore, in one example, the target mapping relationship can be represented as follows:

[0230] The correspondence between the identification information of quantum devices and their intrinsic frequencies in the target ordered sequence. For example, the intrinsic frequency can be specifically represented as freq[n i ], where n i The intrinsic frequencies freq[n] represent the frequencies of the eigenvalues. i The position of freq[n] in the target ordered sequence, i.e., the intrinsic frequency freq[n] i [The nth position in the target ordered sequence] i Bit.

[0231] At this point, the target mapping relationship can be represented by the following table:

[0232] Table 1

[0233]

[0234] Step S503: Based on the target mapping relationship and the task information of the input simulation task, obtain the simulation parameters of the simulation task.

[0235] For example, based on the correspondence between quantum devices and their intrinsic frequencies, the frequency distribution of the intrinsic frequencies of the target quantum devices to be simulated in the simulation task can be determined. Then, based on the frequency distribution, the target simulation start frequency required for the simulation task and the number of target frequencies to be simulated in the simulation task can be determined.

[0236] Here, it is understandable that, since different simulation tasks target different quantum devices, the frequency distribution of the intrinsic frequencies corresponding to different simulation tasks will also be different.

[0237] For example, for the current simulation task, the eigenfrequency of all target quantum devices required for the simulation task can be obtained, such as:

[0238] freq[n1], freq[n2], ..., freq[n i ...;

[0239] Furthermore, the minimum eigenfrequency of the target can be recorded as: Let modeNumMin be the position of the target's minimum intrinsic frequency in the ordered sequence of targets; similarly, let modeNumMin be the position of the target's maximum intrinsic frequency. Let modeNumMax be the position of the target's maximum intrinsic frequency in the ordered sequence of targets. Further, let freq be the starting frequency of the target simulation. * At this time, the target simulation start frequency freq * The eigenfrequency freqMin needs to be less than or equal to the minimum intrinsic frequency. This ensures that the intrinsic frequencies of all target quantum devices to be simulated are greater than or equal to the target simulation start frequency. Furthermore, the number of target frequencies can be obtained based on modeNumMax and modeNumMin. For example, the number of target frequencies = modeNumMax - modeNumMin + 1. In this way, the number of target frequencies required for the simulation task is obtained automatically, which provides support for the effective operation of the simulation task in the future.

[0240] Step S504: Output the simulation parameters required for the simulation task, such as the target simulation start frequency and the number of target frequencies.

[0241] Furthermore, simulation parameters and task information can be transferred to the simulation software through automated programs to automate the execution of simulation tasks.

[0242] Part Four: Parameter Output Rules Based on Pre-simulation Methods and Specific Simulation Tasks

[0243] Based on the pattern recognition method described in Part II, the distribution information of the intrinsic frequencies of each quantum device in the quantum chip layout can be obtained. This section mainly introduces how to use this distribution information and combine it with simulation tasks to automatically output simulation parameters.

[0244] Simulation Task 1: Simulate and obtain the eigenfrequency of each qubit.

[0245] The target quantum device to be simulated in this simulation task one is all the qubits in the quantum chip layout.

[0246] The first step is to input the layout of a quantum chip containing N1 qubits, as well as the task information for simulation task one. N1 is a positive integer greater than or equal to 1.

[0247] The second step involves using program one to obtain M eigenmode information, and then obtaining the eigenfrequency of each qubit.

[0248] Here, each of the M eigenmode information corresponds to an eigenmode. Furthermore, each eigenmode information contains its corresponding eigenfrequency; that is, a total of M eigenfrequencys are obtained. These M eigenfrequencys are sorted in ascending order and numbered after sorting to obtain a target ordered sequence, such as {freq[1],freq[2],…,freq[m],…freq[M]}. At this point, the eigenfrequency of the obtained qubit can be represented as freq[n1],freq[n2],…,freq[n],… i ], ..., here n i It is a positive integer greater than or equal to 1 and less than or equal to N1.

[0249] In practical applications, when designing a quantum chip layout, the eigenfrequency of all qubits may be very close and concentrated at a certain frequency (e.g., 6.6 GHz). In this case, the eigenfrequency of each qubit can be effectively identified using Program 1.

[0250] The third step is to determine the eigenfrequency of each qubit, such as freq[n]. i The class minimum eigenfrequency corresponding to the qubit is obtained, which can be denoted as freqQLeast. At the same time, the position of the class minimum eigenfrequency freqQLeast corresponding to the qubit in the target ordered sequence is obtained, which can be denoted as numQLeast. And, the class maximum eigenfrequency corresponding to the qubit is obtained, which can be denoted as freqQMost. At the same time, the position of the class maximum eigenfrequency freqQMost corresponding to the qubit in the target ordered sequence is obtained, which can be denoted as numQMost.

[0251] The fourth step is to obtain the target simulation starting frequency freq based on the class minimum eigenfrequency freqQLeast corresponding to the qubit. * Furthermore, based on the position numQLeast of the class minimum eigenfrequency corresponding to the qubit in the target ordered sequence and the position numQMost of the class maximum eigenfrequency corresponding to the qubit in the target ordered sequence, the number of target frequencies required for simulation task one is obtained.

[0252] Specifically, in one example, the target simulation start frequency freq * Less than or equal to the class minimum eigenfrequency freqQLeast corresponding to the qubit. Or, in one example, the target simulation start frequency freq. * It can be located between the class minimum eigenfrequency freqQLeast and the eigenfrequency in the target ordered sequence that is smaller than the class minimum eigenfrequency freqQLeast. Further, the target simulation starting frequency freq... * Between the class's minimum intrinsic frequency freqQLeast and the intrinsic frequency freq[freqQLeast-1], that is:

[0253] freq[numQLeast-1]≤target simulation start frequency freq * ≤freqQLeast

[0254] It should be noted that if numQLeast is the first digit in the target ordered sequence, then freq[numQLeast-1] can be a preset frequency value.

[0255] Furthermore, in one example, the number of target frequencies = numQMost - numQLeast + 1.

[0256] Simulation Task 2: Simulate and obtain the dispersion ratio of the quantum system formed by the qubit-coupler-qubit.

[0257] The target quantum device to be simulated in the second simulation task is two qubits (which can be called target qubits) and a coupler (which can be called target couplers) in the qubit-coupler-qubit layout of the quantum chip.

[0258] Here, the two qubits in the qubit-coupler-qubit are adjacent qubits and are connected by a coupler.

[0259] The first step is to input the quantum chip layout and the task information for simulation task two.

[0260] It should be noted that the quantum chip layout includes at least two qubits and a coupler for connecting the two qubits; it is understood that the quantum chip layout may include other quantum devices in addition to the aforementioned quantum devices, and this disclosure does not impose any restrictions on them.

[0261] The second step involves using program one to obtain M eigenmode information, and then obtaining the eigenfrequency of the quantum device in the quantum chip layout. Here, the target quantum device is any one of the quantum devices in the quantum bit-coupler-qubit to be simulated. In other words, this step can simulate the eigenfrequency of the quantum bit in the quantum bit-coupler-qubit to be simulated, as well as the eigenfrequency of the coupler.

[0262] The third step is to obtain the target simulation starting frequency freq based on the eigenfrequency of each target quantum device. * And the number of target frequencies.

[0263] For example, the eigenfrequencys of the target quantum device in the qubit-coupler-qubit simulation are freq[n1], freq[n2], and freq[n3]. At this point, the minimum eigenfrequency freqMin and its position modeNumMin in the target ordered sequence can be obtained; and the maximum eigenfrequency freqMax and its position modeNumMax in the target ordered sequence can also be obtained. The target simulation starting frequency freq can then be obtained in a manner similar to that used in simulation task one. * For example, freq[modeNumMin-1] ≤ target simulation start frequency freq * ≤freqMin, for details please refer to the relevant description of simulation task one above, which will not be repeated in this example. Correspondingly, the number of target frequencies = modeNumMax - modeNumMin + 1.

[0264] Alternatively, in another specific example, if the eigenfrequency of the qubit is less than the eigenfrequency of the coupler, then the target simulation starting frequency freq is... * It only needs to be less than or equal to the smallest eigenfrequency among the eigenfrequencys of the qubits in the qubit-coupler-qubit sequence. Understandably, the target simulation starting frequency freq in this example can also be obtained by referring to simulation task one. * This will not be elaborated upon here.

[0265] It should be noted that the relationship between the intrinsic frequency of the qubit and the intrinsic frequency of the coupler may differ depending on the design of the quantum chip layout, and this disclosed scheme does not impose any restrictions on this.

[0266] Furthermore, assuming that the eigenfrequency of the qubit is less than the eigenfrequency of the coupler, we can obtain the class minimum eigenfrequency among the eigenfrequencys of each coupler in the quantum chip layout, and thus the position of the class minimum eigenfrequency of the coupler in the target ordered sequence, which can be denoted as numCoupLeast; and obtain the class maximum eigenfrequency among the eigenfrequencys of each qubit in the quantum chip layout, and thus the position of the class maximum eigenfrequency of the qubit in the target ordered sequence, which can be denoted as numQMost. Thus, based on numCoupLeast and numQMost, we can obtain the number of target frequencies, for example, the number of target frequencies = numCoupLeast - numQMost + 2.

[0267] It should be noted that, in this case, before performing the simulation, it is necessary to adjust the inductance values ​​of other quantum devices besides the target quantum device, such as other qubits or other couplers, and adjust them to be large enough (i.e., much larger than the maximum eigenfrequency of the device type to which the quantum device belongs; in actual scenarios, this can be adjusted to a preset value, which is an empirical value), so as to ensure that the simulation task is completed using as few target frequencies as possible, thereby maximizing the simulation efficiency.

[0268] Simulation Task 3: Simulate the coupling strength between the qubit and the readout cavity.

[0269] The target quantum device to be simulated in this simulation task is the quantum bit whose coupling strength is to be solved (which can be called the target quantum bit), as well as all the readout cavities in the quantum chip layout. In the actual scenario, the frequency of the readout cavities cannot be adjusted, so if we want to simulate the coupling strength between the quantum bit and the readout cavity, the simulation needs to include all the readout cavities in the quantum chip layout.

[0270] The first step is to input the quantum chip layout and the task information for simulation task three.

[0271] The second step involves using Program 1 to obtain M eigenmode information, and then obtaining the eigenfrequency of each qubit (including the eigenfrequency of the target quantum device) and the eigenfrequency of each readout cavity.

[0272] The third step is to obtain the target simulation starting frequency freq based on the eigenfrequency of the qubit and the eigenfrequency of each readout cavity. * And the number of target frequencies.

[0273] Here, if the intrinsic frequency of the readout cavity is less than the intrinsic frequency of the qubit, then the target simulation starting frequency freq *It only needs to be less than or equal to the smallest eigenfrequency among the eigenfrequencys of each readout cavity, or a method similar to simulation task one can be used to obtain freq[freqCpwLeast-1]≤target simulation starting frequencyfreq. * ≤freq[freqCpwLeast]. Here, freqCpwLeast is the position of the class minimum eigenfrequency corresponding to each readout cavity in the target ordered sequence. For details, please refer to the relevant description in Simulation Task 1 above; this example will not repeat it.

[0274] It should be noted that the relationship between the intrinsic frequency of the readout cavity and the intrinsic frequency of the qubit may differ depending on the design of the quantum chip layout, and this disclosed scheme does not impose any restrictions on this.

[0275] Furthermore, assuming the intrinsic frequency of the readout cavity is less than the intrinsic frequency of the qubit, we can obtain the class minimum intrinsic frequency (FMI) among the intrinsic frequencies of each readout cavity in the quantum chip layout, denoted as freqCpwLeast, and the position of the corresponding FMI of the readout cavity, denoted as numCpwLeast, in the target ordered sequence; and the class minimum intrinsic frequency (FMI) among the intrinsic frequencies of each qubit in the quantum chip layout, denoted as freqQLeast, and the position of the corresponding FMI of the qubit, denoted as numQLeast, in the target ordered sequence. Thus, the number of target frequencies can be obtained based on numCpwLeast and numQLeast, for example, the number of target frequencies = numQLeast - numCpwLeast + 1.

[0276] Simulation Task 4: Simulate and obtain the breakpoints between adjacent qubits.

[0277] The target quantum device to be simulated in simulation task four is two adjacent qubits.

[0278] The target frequency for this simulation task can be calculated using the method described in Simulation Task 1 above, or it can be directly set to 2.

[0279] It should be noted that in simulation task four, it is also necessary to adjust the inductance values ​​of other qubits besides the target quantum device and adjust them to be large enough (i.e., much larger than the maximum eigenfrequency of the device type to which the quantum device belongs) in order to ensure that the simulation task is completed using as few target frequencies as possible, thereby maximizing the simulation efficiency.

[0280] Furthermore, the target simulation start frequency freq in simulation task four *The method for determining the target simulation start frequency is the same as that for determining the target simulation start frequency in Task 1. Please refer to the relevant content of Simulation Task 1, which will not be repeated here.

[0281] Thus, the target simulation start frequency freq required for the simulation task can be obtained automatically using the scheme disclosed herein. * And the number of target frequencies.

[0282] Part Five, Application Demonstration, to verify the effectiveness of this disclosed solution.

[0283] (I) Quantum Chip Layout

[0284] like Figure 6 The diagram shown is a schematic representation of the quantum chip layout of a superconducting quantum chip, specifically including:

[0285] The cross-shaped structure represents a qubit. This quantum chip layout contains four qubits: qubit Q1, qubit Q2, qubit Q3, and qubit Q4. Each qubit also has a Josephson junction.

[0286] The double-arrow-shaped structure represents a coupler. There are three couplers in this quantum chip layout: coupler C12, coupler C23, and coupler C34. Here, coupler C12 is used to connect qubit Q1 and qubit Q2, coupler C23 is used to connect qubit Q2 and qubit Q3, and coupler C34 is used to connect qubit Q3 and qubit Q4.

[0287] The long straight wire with grooves represents the readout cavity, which is set on each qubit, namely readout cavity 1 for qubit Q1, readout cavity 2 for qubit Q2, readout cavity 3 for qubit Q3, and readout cavity 4 for qubit Q4.

[0288] It should be noted that, Figure 6 The white portion represents the air zone, and the gray portion represents the grounding metal plate.

[0289] (II) Verification Task: Verification of Pre-simulation Process

[0290] Step 1: Input as follows Figure 6 The diagram shows the layout of the quantum chip, as well as the task information for the simulation mission.

[0291] Step 2: Using Program 1, obtain the target mapping relationship characterizing the quantum device and its intrinsic frequency.

[0292] Step 2.1: Initialization, i.e., setting the initial simulation frequency to 4GHz, the number of initial frequencies for pre-simulation to 20, and the simulation accuracy to 5%. Using program one, perform finite element electromagnetic simulation to obtain 20 intrinsic mode information. Each of these 20 intrinsic mode information corresponds to an intrinsic frequency, i.e., 20 intrinsic frequencies. Sort these 20 intrinsic frequencies in ascending order and number them to obtain the target ordered sequence, which is shown in Table 2 below.

[0293] Table 2

[0294] Intrinsic frequency (GHz) Position of intrinsic frequencies in the target ordered sequence 5.13561 1 5.17621 2 5.20564 3 5.36325 4 6.56906 5 6.58265 6 6.63162 7 6.67022 8 7.85016 9 7.98242 10 8.26006 11 10.0009 12 10.1371 13 10.2956 14 10.7322 15 11.1479 16 11.7231 17 12.5857 18 13.1047 19 13.1056 20

[0295] Step 2.2: Traverse each quantum device in the quantum chip layout. For example, traverse the identification information of the quantum devices to obtain the eigenfrequency of each quantum device in the quantum chip layout. Then, as shown in Table 3, obtain the target mapping relationship that characterizes the correspondence between quantum devices and eigenfrequency.

[0296] It should be noted that in practical applications, the intrinsic frequencies can also be numerically processed to meet the processing accuracy requirements. As shown in Table 3, the intrinsic frequencies in this example are accurate to the percentile. In practical applications, the values ​​can be set according to actual needs, and this disclosure does not impose any restrictions on this.

[0297] Furthermore, as shown in the last column of Table 3, the position of the intrinsic frequencies of each quantum device in the target ordered sequence is also given, which facilitates the subsequent determination of the number of target frequencies.

[0298] Table 3

[0299]

[0300] Step 3: Based on the target mapping relationship shown in Table 3 and the task information of the simulation task, obtain the simulation parameters for different simulation tasks.

[0301] Specifically, based on the analysis of Table 3, the distribution of the intrinsic frequencies of quantum devices is as follows: for example, the intrinsic frequencies of the readout cavity (denoted as freqCPW) are concentrated around 5 GHz, the intrinsic frequencies of the qubits (denoted as freqQubit) are concentrated around 6.6 GHz, and the intrinsic frequencies of the couplers (denoted as freqCoupler) are concentrated around 10 GHz. Based on this, it can be seen that the current quantum chip layout exhibits the following relationship: freqCPW <freqQubit<freqCoupler。

[0302] It should be noted that the magnitude relationship between the intrinsic frequencies of these three quantum devices may differ in different design schemes, and this disclosure does not impose any restrictions on this.

[0303] For simulation task one (simulating the eigenfrequency of the qubit), the simulation parameters for simulation task one can be obtained in the following way, specifically including:

[0304] The target quantum device to be simulated in the first simulation task consists of four qubits, namely qubits Q1 to Q4. As shown in Table 3, the class minimum eigenfrequency freqQLeast = 6.57 GHz is obtained, and the position of this class minimum eigenfrequency freqQLeast in the target ordered sequence is obtained, i.e., numQLeast = 5; and the class maximum eigenfrequency freqQMost = 6.67 GHz is obtained, and the position of this class maximum eigenfrequency in the target ordered sequence is obtained, i.e., numQMost = 8.

[0305] At this point, the target simulation start frequency freq * This can be obtained based on the following relationship:

[0306] freq[numQLeast-1] <freq * ≤freq[numQLeast]

[0307] like Figure 3 It can be seen that 5.36 <freq * ≤6.57, for example, a possible value is the average of the two, 6.1GHz.

[0308] Furthermore, the number of target frequencies = numQMost - numQLeast + 1 = 8 - 5 + 1 = 4, and this simulation parameter meets the design requirements.

[0309] For simulation task two (simulating the dispersion ratio of the quantum system formed by the qubit-coupler-qubit), the simulation parameters for simulation task two can be obtained in the following way, specifically including:

[0310] The target quantum device to be simulated in the second simulation task is two qubits and a coupler in a quantum system formed by a qubit-coupler-qubit.

[0311] As shown in Table 3, the eigenfrequencys of the qubits in the quantum chip layout simulated in this example are all lower than the eigenfrequencys of the couplers. Therefore, the target simulation starting frequency freq in simulation task two is... * It only needs to be less than or equal to the smallest eigenfrequency among all the eigenfrequencys of the qubits. In this case, the target simulation starting frequency is freq. * The value of can be determined in a similar way to that of simulation task one, for example, the target simulation starting frequency freq. * =6.1GHz.

[0312] Furthermore, during the simulation of the dispersion ratio, the following operation can be performed: adjust the inductance values ​​of other quantum devices besides the target quantum device to a sufficiently large value to ensure that as few target frequencies as possible are used. Based on this, the number of target frequencies can be calculated as follows: based on the position of the class minimum eigenfrequency (i.e., 10.00) of the coupler in the target ordered sequence, numCoupLeast = 12, and the position of the class maximum eigenfrequency (i.e., 6.67) of the qubit in the target ordered sequence, numQMost = 8.

[0313] Based on this, the dispersion ratio of any quantum system formed by a qubit-coupler-qubit in the simulated quantum chip layout can be obtained using the simulation parameters obtained in this example.

[0314] For simulation task three (simulating the coupling strength between the qubit and the readout cavity), the simulation parameters for simulation task three can be obtained in the following way, specifically including:

[0315] The target quantum device to be simulated in simulation task three is a single qubit (which can be called the target qubit) and all readout cavities. It should be noted that each simulation must include all readout cavities and the qubit whose coupling strength needs to be solved.

[0316] As shown in Table 3, the eigenfrequencys of the readout cavity are all lower than the eigenfrequencys of the qubits. Therefore, the target simulation starting frequency freq * The value of can be less than or equal to the minimum eigenfrequency among all the eigenfrequencys of the readout cavities.

[0317] Therefore, as shown in Table 3, the minimum eigenfrequency of the readout cavity is obtained, freqCpwLeast = 5.14, and its position in the target ordered sequence is numCpwLeast = 1; the minimum eigenfrequency of the qubit is obtained, freqQLeast = 6.57, and its position in the target ordered sequence is numQLeast = 5. At this point, the target frequency data is obtained as numQLeast - numCpwLeast + 1 = 5.

[0318] In this example, the target simulation start frequency freq can also be obtained in the following way. * ,Right now:

[0319] freq[numCpwLeast-1] <freq *≤freq[numCpwLeast];

[0320] Here, since numCpwLeast = 1, which is the first value in Table 2, freq[numCpwLeast-1] can be a preset frequency value, such as 4GHz. <freq * ≤5.14GHz, at this point, the target simulation start frequency freq * Specifically, it can be the average of 4 and 5.14, which is 4.62 GHz.

[0321] The simulation parameters described above meet the design requirements.

[0322] For simulation task four (simulating the turn-off points between adjacent qubits), the simulation parameters can be obtained in the following way, specifically including:

[0323] The target quantum device to be simulated in simulation task four consists of two adjacent qubits. Since the target quantum device to be simulated consists of two adjacent qubits, the number of target frequencies can be specifically 2. Further, the target simulation starting frequency freq... * Specifically, it can be 6.1GHz.

[0324] Here, in simulation task four, it is also necessary to adjust the inductance values ​​of other qubits besides the target quantum device and make them large enough. At this time, the number of target frequencies is 2. In other words, a smaller number of target frequencies can be used to complete the simulation task, thereby maximizing the simulation efficiency.

[0325] In summary, this disclosure provides application examples of automated pre-simulation to verify its effectiveness. Furthermore, this disclosure can help superconducting quantum chip designers quickly and accurately generate the required simulation parameters, thereby accelerating the quantum chip design process.

[0326] Based on this, the disclosed solution has the following advantages:

[0327] First, high efficiency. This disclosed solution can automate the pre-simulation process and obtain simulation parameters, thus comprehensively improving simulation efficiency.

[0328] Second, automation. From inputting the quantum chip layout and basic information to outputting simulation parameters, the entire process of this disclosed solution requires no human intervention, greatly improving the automation level of simulation verification, reducing errors that may occur during human operation, and also facilitating integration with subsequent automated simulation verification steps.

[0329] Third, standardization. This disclosed solution automatically determines the eigenfrequency assignment based on quantifiable iEPR values ​​and is applicable to various simulation tasks, making the entire process more standardized. It effectively solves the tediousness of manual identification and effectively reduces the error probability. For example, when a device resonates, the electromagnetic field distribution is almost uniform, and it is difficult to identify the quantum device to which the eigenfrequency belongs based solely on the field distribution diagram. However, the quantized iEPR based on this disclosed solution can perform eigenfrequency matching very accurately.

[0330] This disclosure also provides a simulation device for quantum chip layout, such as... Figure 7 As shown, it includes:

[0331] Processing unit 701 is used to perform pre-simulation of the quantum chip layout to obtain the target mapping relationship corresponding to the quantum chip layout, wherein the target mapping relationship characterizes the correspondence between quantum devices and intrinsic frequencies; the quantum device is one of the multiple quantum devices included in the quantum chip layout;

[0332] The result determination unit 702 is used to obtain pre-simulation results based on the target mapping relationship between the quantum device and the intrinsic frequency, and the task information of the simulation task, wherein the pre-simulation results include the simulation parameters required by the simulation task.

[0333] In a specific example of the scheme disclosed herein, the simulation parameter is at least one of the following: the target simulation start frequency, the number of target frequencies required for the simulation task;

[0334] Wherein, the target simulation start frequency is less than or equal to the target minimum eigenfrequency, and the target minimum eigenfrequency is the minimum value among the eigenfrequency of each of the at least two target quantum devices to be simulated in the simulation task;

[0335] The number of target frequencies is used to determine a plurality of target simulation frequencies starting from the target simulation start frequency, the plurality of target simulation frequencies including the intrinsic frequencies of the target quantum device to be simulated by the simulation task.

[0336] In a specific example of the scheme disclosed herein, the target simulation start frequency is less than or equal to the class minimum eigenfrequency corresponding to the target quantum device. The class minimum eigenfrequency corresponding to the target quantum device represents the minimum value of the eigenfrequency of each quantum device in the same device type as the target quantum device. The class minimum eigenfrequency corresponding to the target quantum device is less than or equal to the target minimum eigenfrequency.

[0337] In a specific example of the disclosed solution, the result determining unit 702 is further configured to:

[0338] The intrinsic frequencies contained in the target mapping relationship are sorted to obtain an ordered sequence of targets;

[0339] The result determination unit 702 is further configured to obtain the target frequency quantity in the following manner: based on the task information of the simulation task and the position of the quantum device in the target ordered sequence, the target frequency quantity is obtained.

[0340] In a specific example of the disclosed solution, the result determination unit 702 is specifically used for:

[0341] When the simulation task is to obtain the eigenfrequency of a qubit, determine the position of the class minimum eigenfrequency corresponding to the target quantum device in the target ordered sequence, and determine the position of the class maximum eigenfrequency corresponding to the target quantum device in the target ordered sequence; wherein, the class maximum eigenfrequency corresponding to the target quantum device represents the maximum value of the eigenfrequency of each quantum device of the same device type as the target quantum device;

[0342] The number of target frequencies is obtained based on the position of the minimum eigenfrequency of the target quantum device in the target ordered sequence and the position of the maximum eigenfrequency of the target quantum device in the target ordered sequence.

[0343] In a specific example of the disclosed solution, the result determination unit 702 is specifically used for:

[0344] In a simulation task where the goal is to obtain the dispersion ratio of a quantum system formed by a qubit-coupler-qubit array, and given that the eigenfrequency of a qubit is less than the eigenfrequency of a coupler, the position of the class minimum eigenfrequency corresponding to the coupler in the target ordered sequence, and the position of the class maximum eigenfrequency corresponding to the qubit in the target ordered sequence, are determined. The plurality of quantum devices includes at least one coupler and at least two qubits, with adjacent qubits connected by couplers. The class maximum eigenfrequency corresponding to a qubit represents the maximum eigenfrequency of each quantum device among all qubits in the quantum chip layout.

[0345] The number of target frequencies is obtained based on the position of the minimum eigenfrequency of the coupler in the target ordered sequence and the position of the maximum eigenfrequency of the qubit in the target ordered sequence.

[0346] In a specific example of the disclosed solution, the result determination unit 702 is specifically used for:

[0347] In a simulation task where the goal is to obtain the coupling strength between a qubit and its corresponding readout cavity, the position of the class minimum eigenfrequency corresponding to the readout cavity in the target ordered sequence is determined, as well as the position of the class minimum eigenfrequency corresponding to the qubit in the target ordered sequence is determined; wherein, the plurality of quantum devices includes at least one qubit and at least one readout cavity;

[0348] The number of target frequencies is obtained based on the position of the class minimum eigenfrequency corresponding to the readout cavity in the target ordered sequence, and the position of the class minimum eigenfrequency corresponding to the qubit in the target ordered sequence.

[0349] In a specific example of the disclosed scheme, it further includes: a target simulation unit, wherein,

[0350] The target simulation unit is used to input the simulation parameters and the task information of the simulation task into a preset simulation system to obtain the simulation result corresponding to the simulation task.

[0351] In a specific example of the scheme disclosed herein, the processing unit 701 is specifically used for:

[0352] Pre-simulation of the quantum chip layout yielded information on multiple intrinsic modes;

[0353] Based on multiple intrinsic mode information, the intrinsic frequencies of each quantum device in the quantum chip layout are obtained;

[0354] The target mapping relationship is obtained based on the eigenfrequency of each quantum device.

[0355] In a specific example of the scheme disclosed herein, the processing unit 701 is specifically used for:

[0356] With the initial simulation frequency set to a preset minimum and the number of initial frequencies for pre-simulation set to a preset number, electromagnetic simulation is performed on the quantum chip layout to obtain information on multiple intrinsic modes.

[0357] In a specific example of the scheme disclosed herein, the processing unit 701 is specifically used for:

[0358] Based on multiple intrinsic mode information, the inductance energy ratio of the quantum device under different intrinsic modes is obtained; wherein, the intrinsic mode information in the multiple intrinsic mode information corresponds to an intrinsic mode;

[0359] Based on the proportion of inductor energy in different eigenmodes of the quantum device, the eigenfrequency of the quantum device is identified from the information of the multiple eigenmodes.

[0360] In a specific example of the scheme disclosed herein, the processing unit 701 is specifically used for:

[0361] The target inductance energy percentage of the quantum device is determined from the inductance energy percentage of the quantum device in different intrinsic modes;

[0362] Based on the multiple intrinsic mode information, the intrinsic mode information corresponding to the target inductance energy ratio of the quantum device is determined;

[0363] The intrinsic frequency of the quantum device is obtained based on the intrinsic mode information corresponding to the target inductance energy ratio of the quantum device.

[0364] In a specific example of the scheme disclosed herein, the processing unit 701 is specifically used to: select the maximum proportion from the inductance energy proportions corresponding to different intrinsic modes of the quantum device, wherein the target inductance energy proportion is the maximum proportion.

[0365] In a specific example of the scheme disclosed herein, the processing unit 701 is specifically used for:

[0366] The inductance energy percentage p of quantum device n in intrinsic mode m among the plurality of quantum devices is obtained as follows: mn :

[0367] Based on information from multiple intrinsic modes, the inductance energy of intrinsic mode m stored in quantum device n is obtained. And obtain the total inductance energy stored in the intrinsic mode m. Wherein, the intrinsic mode information corresponding to intrinsic mode m is one of the plurality of intrinsic mode information;

[0368] The inductor energy stored in quantum device n based on the intrinsic mode m and the total inductance energy stored in the intrinsic mode m The inductance energy percentage p of quantum device n in intrinsic mode m is obtained. mn .

[0369] In a specific example of the scheme disclosed herein, the processing unit 701 is specifically used for:

[0370] Based on information from multiple intrinsic modes, the inductance value L of quantum device n is calculated. n And the voltage V of the intrinsic mode m along the voltage integral line of the quantum device n in space is calculated. mn ;

[0371] Based on the inductance value L of quantum device n n The intrinsic mode m represents the voltage V along the voltage integral line of the quantum device n in space. mn and the eigenfrequency ω' corresponding to the eigenmode m m The inductance energy of intrinsic mode m stored in quantum device n is obtained.

[0372] Among them, the eigenmode information corresponding to eigenmode m includes the eigenfrequency ω' corresponding to eigenmode m. m .

[0373] In a specific example of the scheme disclosed herein, the processing unit 701 is specifically used for:

[0374] The correlation between the inductance energy ratio of quantum device n in different intrinsic modes was obtained;

[0375] Based on the correlation between the inductance energy ratios of quantum device n under different eigenmodes, the inductance value L of quantum device n is obtained. n .

[0376] In a specific example of the scheme disclosed herein, the processing unit 701 is specifically used for:

[0377] Based on the electric field intensity distribution information of intrinsic mode m in space, the voltage V of quantum device n along the voltage integral line in space is calculated. mn ;

[0378] Among them, the intrinsic mode information corresponding to intrinsic mode m includes the electric field intensity distribution information of intrinsic mode m in space.

[0379] In a specific example of the scheme disclosed herein, the processing unit 701 is specifically used for:

[0380] Based on the electric field intensity distribution information of eigenmode m in space, the average electric field energy stored in space by eigenmode m is obtained. Among them, the intrinsic mode information corresponding to intrinsic mode m includes the electric field intensity distribution information of intrinsic mode m in space;

[0381] The average electric field energy stored in space for the eigenmode m The total inductance energy stored in the intrinsic mode m

[0382] In a specific example of the scheme disclosed herein, the quantum chip layout is a chip layout of a superconducting quantum chip.

[0383] For a description of the specific functions and examples of each unit of the apparatus in this disclosure embodiment, please refer to the relevant descriptions of the corresponding steps in the above method embodiments, which will not be repeated here.

[0384] This disclosure also provides a non-transitory computer-readable storage medium storing computer instructions that, when executed by at least one quantum processing unit, cause the at least one quantum processing unit to perform the method described above using a quantum computing device.

[0385] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the methods described above for use in classical computing devices.

[0386] Alternatively, the computer program, when executed by at least one quantum processing unit, implements the method applied to a quantum computing device.

[0387] This disclosure also provides a quantum computing device, the quantum computing device comprising:

[0388] At least one quantum processing unit;

[0389] A memory, coupled to the at least one QPU and used to store executable instructions,

[0390] The instructions are executed by the at least one quantum processing unit to enable the at least one quantum processing unit to perform the method applied to the quantum computing device.

[0391] It is understood that the quantum processing unit (QPU) used in the present disclosure may also be referred to as a quantum processor or quantum chip, and may involve a physical chip comprising multiple qubits interconnected in a specific manner.

[0392] Furthermore, it is understood that the qubit described in this disclosure can refer to the basic information unit of a quantum computing device. The qubit is contained within the QPU and extends the concept of the classical digital bit.

[0393] According to embodiments of this disclosure, this disclosure also provides a computing device, a readable storage medium, and a computer program product.

[0394] Figure 8 A schematic block diagram of an example computing device 800 that can be used to implement embodiments of the present disclosure is shown. The computing device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The computing device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0395] like Figure 8As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0396] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0397] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the simulation method for quantum chip layout. For example, in some embodiments, the simulation method for quantum chip layout can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the simulation method for quantum chip layout described above can be performed. Alternatively, in other embodiments, computing unit 801 may be configured to perform a simulation method of quantum chip layout by any other suitable means (e.g., by means of firmware).

[0398] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0399] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0400] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0401] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0402] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0403] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0404] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0405] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A simulation method for quantum chip layout, comprising: Pre-simulation of the quantum chip layout yields the target mapping relationship corresponding to the quantum chip layout, including: Electromagnetic simulation of the quantum chip layout yielded information on multiple intrinsic modes. Based on multiple intrinsic mode information, the intrinsic frequencies of each quantum device in the quantum chip layout are obtained; The target mapping relationship is obtained based on the eigenfrequency of each quantum device; wherein, the target mapping relationship characterizes the correspondence between quantum devices and eigenfrequency; the quantum device is one of the multiple quantum devices included in the quantum chip layout; Based on the target mapping relationship between the quantum device and its intrinsic frequency, and the task information of the simulation task, the pre-simulation results are obtained, including: The intrinsic frequencies contained in the target mapping relationship are sorted to obtain an ordered sequence of targets; Based on the correspondence between quantum devices and intrinsic frequencies characterized by the target mapping relationship, the frequency distribution of the intrinsic frequencies of the target quantum devices indicated by the task information is determined. Based on the frequency distribution, the simulation parameters required for the simulation task are determined in the target ordered sequence; the pre-simulation results include the simulation parameters required for the simulation task. Among them, the simulation parameters are at least one of the following: the target simulation start frequency, the number of target frequencies required for the simulation task; The target simulation start frequency is less than or equal to the target minimum eigenfrequency, which is the minimum value among the eigenfrequencys of each of the at least two target quantum devices to be simulated in the simulation task. The number of target frequencies is used to determine multiple target simulation frequencies starting from the target simulation start frequency, which include the eigenfrequency of the target quantum device to be simulated in the simulation task.

2. The method according to claim 1, wherein, The target simulation start frequency is less than or equal to the class minimum eigenfrequency corresponding to the target quantum device. The class minimum eigenfrequency corresponding to the target quantum device represents the minimum eigenfrequency of each quantum device in the same device type as the target quantum device. The class minimum eigenfrequency corresponding to the target quantum device is less than or equal to the target minimum eigenfrequency.

3. The method according to claim 2, wherein, The number of target frequencies is obtained in the following way: Based on the task information from the simulation task and the position of the quantum device in the target ordered sequence, the number of target frequencies is obtained.

4. The method according to claim 3, wherein, The target frequency quantity is obtained from the task information based on the simulation task and the position of the quantum device in the target ordered sequence, including: When the simulation task is to obtain the eigenfrequency of a qubit, determine the position of the class minimum eigenfrequency corresponding to the target quantum device in the target ordered sequence, and determine the position of the class maximum eigenfrequency corresponding to the target quantum device in the target ordered sequence; wherein, the class maximum eigenfrequency corresponding to the target quantum device represents the maximum value of the eigenfrequency of each quantum device of the same device type as the target quantum device; The number of target frequencies is obtained based on the position of the minimum eigenfrequency of the target quantum device in the target ordered sequence and the position of the maximum eigenfrequency of the target quantum device in the target ordered sequence.

5. The method according to claim 3, wherein, The target frequency quantity is obtained from the task information based on the simulation task and the position of the quantum device in the target ordered sequence, including: In a simulation task where the goal is to obtain the dispersion ratio of a quantum system formed by a qubit-coupler-qubit array, and given that the eigenfrequency of a qubit is less than the eigenfrequency of a coupler, the position of the class minimum eigenfrequency corresponding to the coupler in the target ordered sequence, and the position of the class maximum eigenfrequency corresponding to the qubit in the target ordered sequence, are determined. The plurality of quantum devices includes at least one coupler and at least two qubits, with adjacent qubits connected by couplers. The class maximum eigenfrequency corresponding to a qubit represents the maximum eigenfrequency of each quantum device among all qubits in the quantum chip layout. The number of target frequencies is obtained based on the position of the minimum eigenfrequency of the coupler in the target ordered sequence and the position of the maximum eigenfrequency of the qubit in the target ordered sequence.

6. The method according to claim 3, wherein, The target frequency quantity is obtained from the task information based on the simulation task and the position of the quantum device in the target ordered sequence, including: In a simulation task where the goal is to obtain the coupling strength between a qubit and its corresponding readout cavity, the position of the class minimum eigenfrequency corresponding to the readout cavity in the target ordered sequence is determined, as well as the position of the class minimum eigenfrequency corresponding to the qubit in the target ordered sequence is determined; wherein, the plurality of quantum devices includes at least one qubit and at least one readout cavity; The number of target frequencies is obtained based on the position of the class minimum eigenfrequency corresponding to the readout cavity in the target ordered sequence, and the position of the class minimum eigenfrequency corresponding to the qubit in the target ordered sequence.

7. The method according to any one of claims 1-6, further comprising: The simulation parameters and the task information of the simulation task are input into a preset simulation system to obtain the simulation results corresponding to the simulation task.

8. The method according to any one of claims 1-6, wherein, The electromagnetic simulation of the quantum chip layout yields multiple intrinsic mode information, including: With the initial simulation frequency set to a preset minimum and the number of initial frequencies for pre-simulation set to a preset number, electromagnetic simulation is performed on the quantum chip layout to obtain information on multiple intrinsic modes.

9. The method according to any one of claims 1-6, wherein, The process of obtaining the eigenfrequency of each quantum device in the quantum chip layout based on multiple eigenmode information includes: Based on multiple intrinsic mode information, the inductance energy ratio of the quantum device under different intrinsic modes is obtained; wherein, the intrinsic mode information in the multiple intrinsic mode information corresponds to an intrinsic mode; Based on the proportion of inductor energy in different eigenmodes of the quantum device, the eigenfrequency of the quantum device is identified from the information of the multiple eigenmodes.

10. The method according to claim 9, wherein, The method of identifying the eigenfrequency of a quantum device from multiple eigenmodes based on the proportion of inductor energy in different eigenmodes includes: The target inductance energy percentage of the quantum device is determined from the inductance energy percentage of the quantum device in different intrinsic modes; Based on the multiple intrinsic mode information, the intrinsic mode information corresponding to the target inductance energy ratio of the quantum device is determined; The intrinsic frequency of the quantum device is obtained based on the intrinsic mode information corresponding to the target inductance energy ratio of the quantum device.

11. The method according to claim 10, wherein, Determining the target inductance energy percentage of the quantum device from the inductance energy percentages in different intrinsic modes includes: The maximum proportion is selected from the proportions of inductance energy corresponding to different intrinsic modes of the quantum device, wherein the target inductance energy proportion is the maximum proportion.

12. The method according to claim 9, wherein, The method of obtaining the inductance energy ratio of a quantum device in different intrinsic modes based on multiple eigenmode information includes: The inductance energy percentage of quantum device n in intrinsic mode m among the multiple quantum devices is obtained as follows: : Based on information from multiple intrinsic modes, the inductance energy of intrinsic mode m stored in quantum device n is obtained. And obtain the total inductance energy stored in the intrinsic mode m. Wherein, the intrinsic mode information corresponding to intrinsic mode m is one of the plurality of intrinsic mode information; The inductor energy stored in quantum device n based on the intrinsic mode m And the total inductance energy stored in the intrinsic mode m The inductance energy ratio of quantum device n in intrinsic mode m was obtained. .

13. The method according to claim 12, wherein, The inductance energy of intrinsic mode m stored in quantum device n is obtained based on information from multiple intrinsic modes. ,include: Based on information from multiple intrinsic modes, the inductance value of quantum device n is calculated. And the voltage of the intrinsic mode m along the voltage integral line of the quantum device n in space is calculated. ; Inductance value based on quantum device n The voltage of intrinsic mode m along the voltage integral line of quantum device n in space. and the intrinsic frequencies corresponding to the intrinsic mode m The inductance energy of the intrinsic mode m stored in the quantum device n is obtained. ; Among them, the eigenmode information corresponding to eigenmode m includes the eigenfrequency corresponding to eigenmode m. .

14. The method according to claim 13, wherein, The inductance value of quantum device n is calculated based on multiple intrinsic mode information. ,include: The correlation between the inductance energy ratio of quantum device n in different intrinsic modes was obtained; Based on the correlation between the inductance energy ratio of quantum device n in different eigenmodes, the inductance value of quantum device n is obtained. .

15. The method according to claim 13, wherein, Based on information from multiple intrinsic modes, the voltage of intrinsic mode m along the voltage integral line of quantum device n in space is calculated. ,include: Based on the electric field intensity distribution information of intrinsic mode m in space, the voltage of quantum device n along the voltage integration line in space is calculated. ; Among them, the intrinsic mode information corresponding to intrinsic mode m includes the electric field intensity distribution information of intrinsic mode m in space.

16. The method according to claim 12, wherein, Based on information from multiple intrinsic modes, the total inductance energy stored in intrinsic mode m is obtained. ,include: Based on the electric field intensity distribution information of eigenmode m in space, the average electric field energy stored in space by eigenmode m is obtained. Among them, the intrinsic mode information corresponding to intrinsic mode m includes the electric field intensity distribution information of intrinsic mode m in space; The average electric field energy stored in space for the eigenmode m The total inductance energy stored in the intrinsic mode m .

17. The method according to any one of claims 1-6, wherein, The quantum chip layout is a chip layout of a superconducting quantum chip.

18. A simulation device for quantum chip layout, comprising: A processing unit is used to perform pre-simulation of a quantum chip layout to obtain a target mapping relationship corresponding to the quantum chip layout, wherein the target mapping relationship characterizes the correspondence between quantum devices and intrinsic frequencies; the quantum device is one of multiple quantum devices included in the quantum chip layout; The result determination unit is used to obtain pre-simulation results based on the target mapping relationship between the quantum device and the intrinsic frequency, as well as the task information of the simulation task. Specifically, the processing unit is used to perform electromagnetic simulation on the quantum chip layout to obtain multiple intrinsic mode information; based on the multiple intrinsic mode information, to obtain the intrinsic frequency of each quantum device in the quantum chip layout; and based on the intrinsic frequency of each quantum device, to obtain the target mapping relationship. Specifically, the result determination unit is used to sort the intrinsic frequencies included in the target mapping relationship to obtain a target ordered sequence; based on the correspondence between quantum devices and intrinsic frequencies represented by the target mapping relationship, determine the frequency distribution of the intrinsic frequencies of the target quantum devices indicated by the task information; determine the simulation parameters required for the simulation task in the target ordered sequence based on the frequency distribution; the pre-simulation results include the simulation parameters required for the simulation task; wherein the simulation parameters are at least one of the following: the target simulation starting frequency, the number of target frequencies required for the simulation task; the target simulation starting frequency is less than or equal to the target minimum intrinsic frequency, the target minimum intrinsic frequency is the minimum value among the intrinsic frequencies of each of the at least two target quantum devices required for the simulation task; the number of target frequencies is used to determine multiple target simulation frequencies starting from the target simulation starting frequency, the multiple target simulation frequencies including the intrinsic frequencies of the target quantum devices required for the simulation task.

19. The apparatus according to claim 18, wherein, The target simulation start frequency is less than or equal to the class minimum eigenfrequency corresponding to the target quantum device. The class minimum eigenfrequency corresponding to the target quantum device represents the minimum eigenfrequency of each quantum device in the same device type as the target quantum device. The class minimum eigenfrequency corresponding to the target quantum device is less than or equal to the target minimum eigenfrequency.

20. The apparatus according to claim 19, wherein, The result determination unit is also used to obtain the target frequency quantity in the following manner: based on the task information of the simulation task and the position of the quantum device in the target ordered sequence, the target frequency quantity is obtained.

21. The apparatus according to claim 20, wherein, The result determination unit is specifically used for: When the simulation task is to obtain the eigenfrequency of a qubit, determine the position of the class minimum eigenfrequency corresponding to the target quantum device in the target ordered sequence, and determine the position of the class maximum eigenfrequency corresponding to the target quantum device in the target ordered sequence; wherein, the class maximum eigenfrequency corresponding to the target quantum device represents the maximum value of the eigenfrequency of each quantum device of the same device type as the target quantum device; The number of target frequencies is obtained based on the position of the minimum eigenfrequency of the target quantum device in the target ordered sequence and the position of the maximum eigenfrequency of the target quantum device in the target ordered sequence.

22. The apparatus according to claim 20, wherein, The result determination unit is specifically used for: In a simulation task where the goal is to obtain the dispersion ratio of a quantum system formed by a qubit-coupler-qubit array, and given that the eigenfrequency of a qubit is less than the eigenfrequency of a coupler, the position of the class minimum eigenfrequency corresponding to the coupler in the target ordered sequence, and the position of the class maximum eigenfrequency corresponding to the qubit in the target ordered sequence, are determined. The plurality of quantum devices includes at least one coupler and at least two qubits, with adjacent qubits connected by couplers. The class maximum eigenfrequency corresponding to a qubit represents the maximum eigenfrequency of each quantum device among all qubits in the quantum chip layout. The number of target frequencies is obtained based on the position of the minimum eigenfrequency of the coupler in the target ordered sequence and the position of the maximum eigenfrequency of the qubit in the target ordered sequence.

23. The apparatus according to claim 20, wherein, The result determination unit is specifically used for: In a simulation task where the goal is to obtain the coupling strength between a qubit and its corresponding readout cavity, the position of the class minimum eigenfrequency corresponding to the readout cavity in the target ordered sequence is determined, as well as the position of the class minimum eigenfrequency corresponding to the qubit in the target ordered sequence is determined; wherein, the plurality of quantum devices includes at least one qubit and at least one readout cavity; The number of target frequencies is obtained based on the position of the class minimum eigenfrequency corresponding to the readout cavity in the target ordered sequence, and the position of the class minimum eigenfrequency corresponding to the qubit in the target ordered sequence.

24. The apparatus according to any one of claims 18-23, further comprising: Target simulation unit, wherein, The target simulation unit is used to input the simulation parameters and the task information of the simulation task into a preset simulation system to obtain the simulation result corresponding to the simulation task.

25. The apparatus according to any one of claims 18-23, wherein, The processing unit is specifically used for: With the initial simulation frequency set to a preset minimum and the number of initial frequencies for pre-simulation set to a preset number, electromagnetic simulation is performed on the quantum chip layout to obtain information on multiple intrinsic modes.

26. The apparatus according to any one of claims 18-23, wherein, The processing unit is specifically used for: Based on multiple intrinsic mode information, the inductance energy ratio of the quantum device under different intrinsic modes is obtained; wherein, the intrinsic mode information in the multiple intrinsic mode information corresponds to an intrinsic mode; Based on the proportion of inductor energy in different eigenmodes of the quantum device, the eigenfrequency of the quantum device is identified from the information of the multiple eigenmodes.

27. The apparatus according to claim 26, wherein, The processing unit is specifically used for: The target inductance energy percentage of the quantum device is determined from the inductance energy percentage of the quantum device in different intrinsic modes; Based on the multiple intrinsic mode information, the intrinsic mode information corresponding to the target inductance energy ratio of the quantum device is determined; The intrinsic frequency of the quantum device is obtained based on the intrinsic mode information corresponding to the target inductance energy ratio of the quantum device.

28. The apparatus according to claim 27, wherein, The processing unit is specifically used to: select the maximum proportion from the inductance energy proportions corresponding to different intrinsic modes of the quantum device, wherein the target inductance energy proportion is the maximum proportion.

29. The apparatus according to claim 26, wherein, The processing unit is specifically used for: The inductance energy percentage of quantum device n in intrinsic mode m among the multiple quantum devices is obtained as follows: : Based on information from multiple intrinsic modes, the inductance energy of intrinsic mode m stored in quantum device n is obtained. And obtain the total inductance energy stored in the intrinsic mode m. Wherein, the intrinsic mode information corresponding to intrinsic mode m is one of the plurality of intrinsic mode information; The inductor energy stored in quantum device n based on the intrinsic mode m And the total inductance energy stored in the intrinsic mode m The inductance energy ratio of quantum device n in intrinsic mode m was obtained. .

30. The apparatus according to claim 29, wherein, The processing unit is specifically used for: Based on information from multiple intrinsic modes, the inductance value of quantum device n is calculated. And the voltage of the intrinsic mode m along the voltage integral line of the quantum device n in space is calculated. ; Inductance value based on quantum device n The voltage of intrinsic mode m along the voltage integral line of quantum device n in space. and the intrinsic frequencies corresponding to the intrinsic mode m The inductance energy of the intrinsic mode m stored in the quantum device n is obtained. ; Among them, the eigenmode information corresponding to eigenmode m includes the eigenfrequency corresponding to eigenmode m. .

31. The apparatus according to claim 30, wherein, The processing unit is specifically used for: The correlation between the inductance energy ratio of quantum device n in different intrinsic modes was obtained; Based on the correlation between the inductance energy ratio of quantum device n in different eigenmodes, the inductance value of quantum device n is obtained. .

32. The apparatus according to claim 30, wherein, The processing unit is specifically used for: Based on the electric field intensity distribution information of intrinsic mode m in space, the voltage of quantum device n along the voltage integration line in space is calculated. ; Among them, the intrinsic mode information corresponding to intrinsic mode m includes the electric field intensity distribution information of intrinsic mode m in space.

33. The apparatus according to claim 29, wherein, The processing unit is specifically used for: Based on the electric field intensity distribution information of eigenmode m in space, the average electric field energy stored in space by eigenmode m is obtained. Among them, the intrinsic mode information corresponding to intrinsic mode m includes the electric field intensity distribution information of intrinsic mode m in space; The average electric field energy stored in space for the eigenmode m The total inductance energy stored in the intrinsic mode m .

34. The apparatus according to any one of claims 18-23, wherein, The quantum chip layout is a chip layout of a superconducting quantum chip.

35. A computing device, comprising: At least one quantum processing unit (QPU); A memory, coupled to the at least one QPU and used to store executable instructions, The instructions are executed by the at least one QPU to enable the at least one QPU to perform the method of any one of claims 1 to 17; Or, including: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-17.

36. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, When at least one quantum processing unit is executed, the computer instructions cause the at least one quantum processing unit to perform the method according to any one of claims 1 to 17; Alternatively, the computer instructions are used to cause the computer to perform the method according to any one of claims 1-17.

37. A computer program product comprising a computer program that, when executed by at least one quantum processing unit, implements the method according to any one of claims 1-17; Alternatively, the computer program, when executed by a processor, implements the method according to any one of claims 1-17.

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