Quantum hardware resource virtualization method and apparatus, and computer device and storage medium

Abstract

A quantum hardware resource virtualization method and apparatus, and an electronic device and a storage medium. The method comprises: acquiring a target quantum bit unit topology used for a target quantum computing task (310); acquiring a quantum bit unit network in a quantum hardware resource (320); in the quantum bit unit network, acquiring a plurality of candidate quantum bit unit sub-networks meeting the target quantum bit unit topology (330); on the basis of self-operation fidelity of quantum bit units in the candidate quantum bit unit sub-networks and mutual operation fidelity between adjacent quantum bit units, determining the overall operation fidelity of the candidate quantum bit unit sub-networks (340); and on the basis of the overall operation fidelity, determining a target quantum bit unit sub-network to serve as a virtualization resource used for the target quantum computing task (350). The method increases the utilization rate of a virtualization resource, and also ensures a relatively high operation accuracy. The method can be applied to various scenarios, such as quantum computing and superconducting quantum computing.

Description

Quantum hardware resource virtualization method, device, computer equipment and storage medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on August 30, 2023, with application number 2023111056033 and invention name “Quantum hardware resource virtualization method, device, electronic device and storage medium”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present disclosure relates to the field of quantum technology, and in particular to a method, device, electronic device, and storage medium for virtualizing quantum hardware resources. Background Art

[0003] In quantum hardware, quantum bits are stored in qubit units, the basic unit for performing quantum computations. When performing multiple tasks simultaneously, similar to classical computing, the quantum chip often needs to be partitioned into multiple virtual machines or run multiple processes simultaneously to execute multiple tasks. Quantum computing differs from classical computing in that the operations themselves are often inseparable from the underlying qubit network that supports them. Arbitrarily virtualizing the resulting qubit network structure may not be sufficient to support the desired quantum computations, so the underlying quantum hardware resources are often not arbitrarily virtualized.

[0004] Related technologies for quantum hardware resource virtualization typically employ a simple physical partitioning approach. For example, the quantum hardware resources are split into two: virtualized quantum hardware resource A and virtualized quantum hardware resource B. When executing Task 1, virtualized quantum hardware resource A is activated, while when executing Task 2, virtualized quantum hardware resource B is activated. This simple partitioning approach can waste quantum hardware resources and may not guarantee that the allocated virtualized quantum hardware resources can accurately execute quantum tasks.

[0005] The disadvantages of related technologies are that the resource utilization rate of quantum hardware resources is low and the calculation accuracy is difficult to guarantee.

[0006] Summary of the Invention

[0007] The embodiments of the present disclosure provide a quantum hardware resource virtualization method, apparatus, electronic device, and storage medium, which improve the utilization rate of virtualized resources and ensure high computational accuracy.

[0008] According to one aspect of the present disclosure, a method for virtualizing quantum hardware resources is provided, comprising:

[0009] Obtaining a target quantum bit unit topology for a target quantum computing task;

[0010] Obtaining the quantum bit unit network in quantum hardware resources;

[0011] In the qubit unit network, obtaining a plurality of candidate qubit unit subnetworks that conform to the target qubit unit topology;

[0012] Determining the overall operational fidelity of each candidate qubit unit subnetwork in the plurality of candidate qubit unit subnetworks based on the self-operational fidelity of each qubit unit in the candidate qubit unit subnetwork and the mutual operational fidelity between adjacent qubit units;

[0013] Based on the overall operational fidelity of the plurality of candidate qubit unit subnetworks, a target qubit unit subnetwork is determined, and the target qubit unit subnetwork serves as a virtualized resource for the target quantum computing task.

[0014] According to one aspect of the present disclosure, a device for virtualizing quantum hardware resources is provided, comprising:

[0015] A first acquisition unit is used to acquire a target quantum bit unit topology for a target quantum computing task;

[0016] A second acquisition unit is used to acquire a quantum bit unit network in the quantum hardware resources;

[0017] A third acquisition unit is configured to acquire, in the qubit unit network, a plurality of candidate qubit unit sub-networks that conform to the target qubit unit topology;

[0018] a first determining unit, configured to determine the overall operational fidelity of each candidate qubit unit subnetwork in the plurality of candidate qubit unit subnetworks based on the self-operation fidelity of each qubit unit in each candidate qubit unit subnetwork and the mutual operational fidelity between adjacent qubit units;

[0019] A second determining unit is configured to determine a target quantum bit unit subnetwork based on the overall operational fidelity of the plurality of candidate quantum bit unit subnetworks, wherein the target quantum bit unit subnetwork serves as a virtualized resource for the target quantum computing task.

[0020] Optionally, the first determining unit is specifically configured to:

[0021] Obtaining a first weight of the self-operation fidelity and a second weight of the mutual operation fidelity;

[0022] Based on the first weight and the second weight, a weighted sum is performed on the self-operation fidelity and the mutual operation fidelity to obtain the overall operation fidelity.

[0023] Optionally, the first determining unit is further configured to:

[0024] Obtaining a task type of the target quantum computing task;

[0025] Based on the task type, a preset mapping relationship between the task type and the weight is searched to obtain the first weight and the second weight.

[0026] Optionally, the target qubit unit topology is a universal qubit unit topology; and the first acquisition unit is specifically configured to:

[0027] Acquire a plurality of sample quantum computing tasks, wherein the plurality of sample quantum computing tasks include the target quantum computing task;

[0028] Obtaining a plurality of sample quantum bit unit topologies for a plurality of the sample quantum computing tasks;

[0029] The union of multiple sample quantum bit unit topologies is taken to obtain the universal quantum bit unit topology.

[0030] Optionally, the second acquiring unit is specifically configured to:

[0031] Acquire original quantum bit units in a quantum hardware resource and a first connection relationship between the original quantum bit units;

[0032] Obtain occupied quantum bit units;

[0033] Obtaining a second connection relationship between the occupied qubit units and between the occupied qubit unit and an adjacent qubit unit;

[0034] The occupied quantum bit unit is removed from the original quantum bit unit, and the second connection relationship is removed from the first connection relationship to obtain the quantum bit unit network.

[0035] Optionally, the target qubit unit topology includes a first number of qubit units; and the third acquisition unit is specifically configured to:

[0036] Traversing the qubit unit network to obtain a plurality of screened qubit unit sub-networks, wherein the screened qubit unit sub-networks include the first number of connected qubit units;

[0037] The third connection relationship between the qubit units in the target qubit unit topology is compared with the fourth connection relationship between the qubit units in each of the screened qubit unit subnetworks to determine a plurality of candidate qubit unit subnetworks from the plurality of screened qubit unit subnetworks.

[0038] Optionally, the second determining unit is specifically configured to:

[0039] Determining an overall failure rate of each candidate qubit unit subnetwork based on the failure rate of each qubit unit in each candidate qubit unit subnetwork;

[0040] A target qubit unit subnetwork is determined based on the overall operational fidelity and the overall failure rate of a plurality of the candidate qubit unit subnetworks.

[0041] Optionally, the second determining unit is specifically configured to:

[0042] Obtaining the importance of the target quantum computing task;

[0043] determining a fidelity level corresponding to the importance;

[0044] The candidate quantum bit unit sub-network whose overall operation fidelity belongs to the fidelity level among the multiple candidate quantum bit unit sub-networks is determined as the target quantum bit unit sub-network.

[0045] Optionally, the number of the target quantum computing tasks is a second number, and the number of the candidate quantum bit unit subnetworks is a third number, wherein the second number is smaller than the third number; and the second determining unit is specifically configured to:

[0046] sorting the third number of candidate quantum bit unit sub-networks in descending order according to the overall operation fidelity;

[0047] The first second number of candidate quantum bit unit sub-networks in the sorting are determined as the second number of target quantum bit unit sub-networks for the second number of target quantum computing tasks.

[0048] Optionally, the target quantum computing task is a second number of the associated target quantum computing tasks, and the candidate quantum bit unit subnetwork is a third number of the candidate quantum bit unit subnetwork, wherein the second number is smaller than the third number;

[0049] Optionally, the second determining unit is specifically configured to:

[0050] Traversing the third number of candidate qubit unit sub-networks to obtain a plurality of candidate sub-network combinations, each candidate sub-network combination in the plurality of candidate sub-network combinations including the second number of candidate qubit unit sub-networks;

[0051] Determining the sum of the overall operational fidelity of a second number of the candidate qubit unit sub-networks in each candidate sub-network combination;

[0052] determining a concentration of a second number of the candidate quantum bit unit sub-networks in each candidate sub-network combination;

[0053] Based on the sum of the overall operational fidelity and the concentration of the plurality of candidate sub-network combinations, a target sub-network combination is determined from the plurality of candidate sub-network combinations, the target sub-network combination serving as the second number of target quantum bit unit sub-networks for the second number of target quantum computing tasks.

[0054] Optionally, the second determining unit is further configured to:

[0055] Determining the center point distances between each of the second number of candidate quantum bit unit sub-networks in the candidate sub-network combination;

[0056] The reciprocal of the average distance of the center point distances is determined as the concentration degree of the second number of the candidate quantum bit unit sub-networks.

[0057] According to one aspect of the present disclosure, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned quantum hardware resource virtualization method when executing the computer program.

[0058] According to one aspect of the present disclosure, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the method for virtualizing quantum hardware resources as described above is implemented.

[0059] According to one aspect of the present disclosure, a computer program product is provided. The computer program product includes a computer program. The computer program is read and executed by a processor of a computer device, so that the computer device performs the quantum hardware resource virtualization method as described above.

[0060] In the disclosed embodiments, when quantum hardware resources are required to perform a quantum computing task, a target qubit unit topology is first determined based on the quantum computing task. Then, multiple candidate qubit unit subnetworks that conform to the target qubit unit topology are obtained from the qubit unit network in the quantum hardware resource. By obtaining multiple candidate qubit unit subnetworks that conform to the target qubit unit topology from the qubit unit network in the quantum hardware resource, any one of the multiple candidate qubit unit subnetworks can be used as a network for performing the quantum computing task. The topology of the candidate qubit unit subnetwork is relatively reasonable, improving the adaptability of the virtualized quantum hardware resources to the quantum computing task and thereby improving the utilization of the quantum hardware resources. Then, based on the self-computational fidelity of each qubit unit in each candidate qubit unit subnetwork and the mutual computational fidelity between adjacent qubit units, the overall computational fidelity of each candidate qubit unit subnetwork is determined. Based on the overall computational fidelity of the multiple candidate qubit unit subnetworks, the target qubit unit subnetwork is determined as the virtualized resource for the target quantum computing task. In this way, the target qubit unit subnetwork has a high overall operational fidelity, ensuring the accuracy of the target qubit unit subnetwork in executing quantum computing tasks. In summary, the disclosed embodiments can improve the adaptability of virtualized quantum hardware resources to quantum computing tasks, increase quantum hardware resource utilization, and ensure high accuracy of quantum computing tasks.

[0061] Other features and advantages of the present disclosure will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present disclosure. The purposes and other advantages of the present disclosure can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The accompanying drawings are used to provide a further understanding of the technical solution of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the technical solution of the present disclosure and do not constitute a limitation to the technical solution of the present disclosure.

[0063] FIG1 is an architecture diagram of a quantum computing system to which a quantum hardware resource virtualization method according to an embodiment of the present disclosure is applied;

[0064] 2A-2D are schematic diagrams of embodiments of the present disclosure applied in a quantum hardware resource virtualization scenario;

[0065] FIG3 is a flowchart of a method for quantum hardware resource virtualization according to an embodiment of the present disclosure;

[0066] 4A-4D are schematic diagrams of step 310 in FIG. 3 regarding the target quantum bit unit topology;

[0067] FIG5 is a flowchart of step 310 in FIG3 regarding the target qubit unit topology being a universal qubit unit topology;

[0068] FIG6 is a schematic diagram of a specific implementation of summarizing a universal quantum bit unit topology from multiple sample quantum computing tasks;

[0069] FIG7 is a flowchart of step 320 in FIG3 regarding obtaining a quantum bit unit network;

[0070] 8A-8C are schematic diagrams of a specific implementation of obtaining a quantum bit unit network;

[0071] FIG9 is a flowchart of step 330 in FIG3 regarding obtaining multiple candidate quantum bit unit sub-networks;

[0072] FIG10 is a schematic diagram of a specific implementation of obtaining multiple candidate quantum bit unit sub-networks;

[0073] FIG11 is a flow chart of step 340 in FIG3 regarding obtaining overall operation fidelity;

[0074] FIG12 is a schematic diagram of a specific implementation of obtaining overall operation fidelity;

[0075] FIG13 is a flow chart of step 350 in FIG3 , which uses the overall failure rate to determine the target quantum bit unit sub-network;

[0076] FIG14 is a schematic diagram showing a specific implementation of determining a target quantum bit unit subnetwork using an overall failure rate;

[0077] FIG15 is a flowchart of step 350 in FIG3 using importance to determine the target qubit unit subnetwork;

[0078] FIG16 is a schematic diagram showing a specific implementation of determining a target quantum bit unit subnetwork using importance;

[0079] FIG17 is a flowchart of step 350 in FIG3 for determining a target quantum bit unit subnetwork for a second number of target quantum computing tasks;

[0080] FIG18 is a schematic diagram of a specific implementation of determining a target quantum bit unit subnetwork for a second number of target quantum computing tasks;

[0081] FIG19 is a flowchart of step 350 in FIG3 for determining a target qubit unit subnetwork for an associated second number of target quantum computing tasks;

[0082] 20A-20C are schematic diagrams of a specific implementation of determining a target quantum bit unit sub-network for an associated second number of target quantum computing tasks;

[0083] FIG21 is a diagram showing the detailed implementation of the quantum hardware resource virtualization method according to an embodiment of the present disclosure;

[0084] FIG22 is a module diagram of a quantum hardware resource virtualization device according to an embodiment of the present disclosure;

[0085] FIG23 is a structural diagram of a terminal for executing the typesetting processing method shown in FIG3 according to an embodiment of the present disclosure;

[0086] FIG24 is a structural diagram of a server for executing the typesetting processing method shown in FIG3 according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not intended to limit the present disclosure.

[0088] Before further explaining the embodiments of the present disclosure in detail, the nouns and terms involved in the embodiments of the present disclosure are explained. The nouns and terms involved in the embodiments of the present disclosure are subject to the following interpretations:

[0089] Quantum computing: Quantum computing is a computing method designed using the principles of quantum mechanics. Its key difference from traditional computing is that it uses quantum bits (qubits) rather than traditional binary bits. Qubits have distinct properties from traditional binary bits, the most important of which is their ability to simultaneously exist in multiple states. Because qubits can represent two states simultaneously, they carry more information. Using the same number of qubits, more data operations can be performed than with classical computing. Therefore, quantum computing significantly increases computer processing speed.

[0090] Quantum: The quantum in "quantum computing" refers to the smallest discrete unit a system can use to calculate output.

[0091] A qubit is the fundamental unit of information in quantum computing. While its role in quantum computing is similar to that of a bit in traditional computing, its behavior differs significantly. While a classical bit is binary and can only store a 0 or 1, a qubit can store a superposition of all possible states—a state of |0> and a state of |1>—thus significantly increasing computer processing speed.

[0092] Quantum bit unit: A storage unit in quantum hardware resources used to hold quantum bits involved in calculations.

[0093] System architecture and scenario description of the application of the embodiments of the present disclosure

[0094] Figure 1 is a diagram of the architecture of a quantum computing system used in a quantum hardware resource virtualization method according to an embodiment of the present disclosure. The quantum computing system includes a browser, a client, a quantum application, a quantum operating system, and quantum hardware resources.

[0095] Browsers and clients are executable programs used to generate quantum computing tasks. They typically run on terminals, which include desktop computers, laptops, tablets, PDAs (personal digital assistants), mobile phones, and in-vehicle terminals.

[0096] Quantum applications are deployed on quantum operating systems. The application's API receives quantum computing tasks. The task distribution system distributes these tasks to the quantum operating system. The quantum simulator within the application simulates the quantum computations involved.

[0097] The quantum operating system (Qubit Operating System, QOS) is generally deployed in the cloud. The quantum operating system is generally installed on quantum hardware resources. The quantum operating system includes an intermediate representation, a quantum just-in-time compiler, a quantum cabin, resource allocation, isolation security, and resource checking. The intermediate representation serves as a common interface between quantum applications and the quantum operating system and can serve as an abstract representation of quantum computing tasks. The quantum just-in-time compiler (JIT) includes a task acquisition model and a topology compilation model. The task scheduling model is used to perform task scheduling. The topology compilation model is used to compile scheduled tasks to obtain the quantum bit unit topology. The quantum cabin includes a checker and a database. The checker is used to check quantum computing tasks. The database is used to store data for quantum computing tasks, such as the quantum bit unit topology corresponding to the quantum computing task. Resource allocation is used to allocate virtualized resources for quantum computing tasks. Isolation security is used to isolate and secure data between the quantum operating system and quantum hardware resources. Resource checking is used to check quantum hardware resources for quantum computing tasks.

[0098] Quantum hardware resources are the physical space where quantum computing takes place. Quantum hardware resources are typically deployed in the cloud. As shown in Figure 1, quantum hardware resources include Quantum Processing Unit 1 (QPU1) and Quantum Processing Unit 2 (QPU2). Quantum processing units are physically independent hardware blocks within the quantum hardware resource. Those skilled in the art will appreciate that Quantum Processing Unit 1 (QPU1) and Quantum Processing Unit 2 (QPU2) are merely examples, and the number of quantum processing units may vary.

[0099] When a browser or client generates a task requiring quantum computing, it is first dispatched to the quantum operating system's task scheduling model via the quantum application's task distribution system. The quantum simulator within the quantum application can simulate the quantum computing of the task. The task is then scheduled by the task scheduling model within the quantum operating system. This task is then compiled using the topology compilation model to generate a qubit unit topology suitable for executing the task. Quantum hardware resources contain a large number of qubit units. Adjacent qubit units are connected via radio frequency beams, forming a network of qubit units. Quantum hardware resources are virtualized to create virtualized resources for quantum computing tasks.

[0100] The embodiments of the present disclosure can be applied in various scenarios, such as the quantum hardware resource virtualization scenario shown in FIG. 2A to FIG. 2D .

[0101] As shown in Figure 2A, quantum computing tasks A and B require virtualized resources, so the quantum hardware resources are virtualized. The quantum hardware resources include 12 qubit units. Qubit unit 1 is connected to qubit unit 2. Qubit unit 2 is connected to qubit unit 3. Qubit unit 3 is connected to qubit unit 4. Qubit unit 4 is connected to qubit unit 5. Qubit unit 5 is connected to qubit unit 6. Qubit unit 6 is connected to qubit unit 7. Qubit unit 7 is connected to qubit unit 8. Qubit unit 8 is connected to qubit unit 9. Qubit unit 9 is connected to qubit unit 10. Qubit unit 10 is connected to qubit unit 11. Qubit unit 11 is connected to qubit unit 12. Qubit unit 1 is connected to qubit unit 12. Qubit unit 2 is connected to qubit unit 11. There is a connection relationship between qubit unit 3 and qubit unit 10. There is a connection relationship between qubit unit 4 and qubit unit 9. There is a connection relationship between qubit unit 5 and qubit unit 8.

[0102] The qubit unit topology for quantum computing task A consists of four qubit units connected end-to-end, while the qubit unit topology for quantum computing task B consists of three qubit units connected in a single chain. The quantum hardware resources are partitioned based on the qubit unit topologies of these two tasks. The two dashed lines in Figure 2A divide the overall quantum hardware resources into virtualized resource z1, virtualized resource z2, and virtualized resource z3. Virtualized resource z1, virtualized resource z2, and virtualized resource z3 are shown in Figure 2B.

[0103] In Figure 2B , virtualized resource z1 includes qubit unit 1, qubit unit 2, qubit unit 11, and qubit unit 12, for a total of four qubit units. Virtualized resource z2 includes qubit unit 3, qubit unit 4, qubit unit 9, and qubit unit 10, for a total of four qubit units. Virtualized resource z3 includes qubit unit 5, qubit unit 6, qubit unit 7, and qubit unit 8, for a total of four qubit units.

[0104] The difference between FIG2B and FIG2A is that there is no connection between qubit unit 2 and qubit unit 3. There is no connection between qubit unit 4 and qubit unit 5. There is no connection between qubit unit 8 and qubit unit 9. There is no connection between qubit unit 10 and qubit unit 11.

[0105] Assuming the computational fidelity of virtualized resource z1 is 0.9, the computational fidelity of virtualized resource z2 is 0.8, and the computational fidelity of virtualized resource z3 is 0.5, virtualized resource z1 is determined to process quantum computing task A, and virtualized resource z2 is determined to process quantum computing task B. This ensures the computational fidelity of the virtualized resources in processing quantum computing tasks.

[0106] Running quantum computing task A on virtualized resource z1 is shown in Figure 2C, and running quantum computing task B on virtualized resource z2 is shown in Figure 2D. It should be noted that virtualized resource z3 can be used for other quantum computing tasks.

[0107] In Figure 2C, when virtualized resource z1 processes quantum computing task A, quantum computing task A occupies qubit unit 1, qubit unit 2, qubit unit 11, and qubit unit 12. It can be seen that virtualized resource z1 is fully occupied, and resource utilization is high.

[0108] In Figure 2D , when virtualized resource z2 processes quantum computing task B, task B occupies qubit units 4, 5, and 9, leaving qubit unit 8. In this case, only one qubit unit is idle, indicating high resource utilization.

[0109] In summary, the disclosed embodiments fully consider the qubit unit topology of quantum computing tasks when virtualizing quantum hardware resources, improving the adaptability of virtualized quantum hardware resources to quantum computing tasks and thereby increasing resource utilization. Furthermore, the computational fidelity of each partitioned virtualized resource is considered, resulting in higher computational accuracy for the virtualized quantum hardware resources used for quantum computing tasks.

[0110] General description of the embodiments of the present disclosure

[0111] According to one embodiment of the present disclosure, a method for virtualizing quantum hardware resources is provided.

[0112] The quantum hardware resource virtualization method of the disclosed embodiment can be executed on a server, or partially on a server and partially on a terminal.

[0113] As shown in FIG3 , according to one embodiment of the present disclosure, quantum hardware resource virtualization includes:

[0114] Step 310: Obtain a target quantum bit unit topology for a target quantum computing task;

[0115] Step 320: Obtain a quantum bit unit network in the quantum hardware resources;

[0116] Step 330: In the qubit unit network, obtain multiple candidate qubit unit sub-networks that meet the target qubit unit topology;

[0117] Step 340: Determine the overall computational fidelity of each candidate qubit unit subnetwork based on the self-computational fidelity of each qubit unit in each candidate qubit unit subnetwork and the mutual computational fidelity between adjacent qubit units in the plurality of candidate qubit unit subnetworks.

[0118] Step 350: Determine a target quantum bit unit subnetwork based on the overall computational fidelity of the plurality of candidate quantum bit unit subnetworks, with the target quantum bit unit subnetwork serving as a virtualized resource for the target quantum computing task.

[0119] The target qubit unit topology for a target quantum computing task and the qubit unit network in the quantum hardware resources are separately obtained, and then multiple candidate qubit unit subnetworks that conform to the target qubit unit topology are obtained from the qubit unit network in the quantum hardware resources. By obtaining multiple candidate qubit unit subnetworks that conform to the target qubit unit topology from the qubit unit network in the quantum hardware resources, any one of the multiple candidate qubit unit subnetworks can be used as a network for performing quantum computing tasks, and the topological structure of the candidate qubit unit subnetwork is relatively reasonable, thereby improving the adaptability of the virtualized quantum hardware resources to quantum computing tasks and thereby increasing quantum hardware resource utilization. The overall computational fidelity of each candidate qubit unit subnetwork is then determined based on the self-computational fidelity of each qubit unit in each candidate qubit unit subnetwork and the mutual computational fidelity between adjacent qubit units. Based on the overall computational fidelity of the multiple candidate qubit unit subnetworks, a target qubit unit subnetwork is determined as the virtualized resource for the target quantum computing task. In this way, the target qubit unit subnetwork has a high overall operational fidelity, ensuring the accuracy of the target qubit unit subnetwork in executing quantum computing tasks. In summary, the disclosed embodiments can improve the adaptability of virtualized quantum hardware resources to quantum computing tasks, increase quantum hardware resource utilization, and ensure high accuracy of quantum computing tasks.

[0120] The following is a detailed description of steps 310-330.

[0121] Detailed description of step 310

[0122] In step 310, a target qubit unit topology for a target quantum computing task is obtained. A target quantum computing task is a task that can trigger the quantum hardware resource virtualization service. The target quantum computing task includes multiple quantum operations. The target qubit unit topology refers to the network structure composed of multiple qubit units.

[0123] Because the multiple quantum computations in the target quantum computing task can be converted into gate operations on each qubit unit and gate operations between adjacent pairs of qubit units, a target qubit unit topology is obtained based on the network structure formed by each qubit unit and the adjacent pairs of qubit units. The target qubit unit topology includes the multiple qubit units and the connection relationships between the multiple qubit units.

[0124] It should be noted that quantum bits can represent two states at the same time, such as the "dead" and "alive" state of "Schrödinger's cat", but before the box is opened, "Schrödinger's cat" is in a superposition of the two states of "dead" and "alive". Since quantum bits can represent two states at the same time and carry more information, more data operations can be completed with the same number of quantum bit units than classical calculations. The state of a quantum bit is a superposition of two states |0> and |1>, with probabilities of α and β respectively. Before the quantum bit is observed, α and β are uncertain values ​​that change randomly, but both satisfy the "α 2 +β 2 =1"; when the quantum bit is observed, the values ​​of α and β are determined. Each quantum bit can use a variety of different gate operations.

[0125] Gate operation is also called quantum gate operation or quantum logic gate operation. The more typical gate operations are I, X, Y, and H. The gate operation here is not completed through the gate devices in classical computing, but is completed by applying different radio frequency modulation signals to the quantum bit unit. The radio frequency modulation signal has signals with different components, and each component signal has three variables: amplitude, frequency, and phase. By adjusting the three variables of different components, various gate operations can be completed. For example, the X gate operation is to interchange the probabilities of states |0> and |1>, that is, the probability of state |0> in the quantum bit unit is changed from α to β by adjusting the three variables of different components of the radio frequency signal; the probability of state |1> in the quantum bit unit is changed from β to α by adjusting the three variables of different components of the radio frequency signal. The H gate operation is to change the probability of state |0> from α to |1>. The probability of state |1> changes from β to

[0126] In one embodiment, step 310 includes:

[0127] Obtain quantum computing tasks for task distribution systems in quantum applications;

[0128] Calling the task scheduling model in the quantum operating system to schedule the target quantum computing task to obtain the scheduled target quantum computing task, where the target quantum computing task is any task in the quantum computing task;

[0129] The topology compilation model is called to perform topology compilation on the scheduled target quantum computing task to obtain the target quantum bit unit topology.

[0130] Since quantum applications and quantum operating systems have been described in the system architecture above, they will not be repeated here.

[0131] The benefit of this embodiment is that it fully utilizes the cooperation between quantum applications and quantum operating systems to improve the flexibility and accuracy of obtaining the target quantum bit unit topology.

[0132] The target quantum bit unit topology is described in detail below in conjunction with Figures 4A-4B.

[0133] In one example, as shown in FIG4A , the target qubit unit topology M1 of the target quantum computing task R1 includes four qubit units. Specifically, the target quantum computing task R1 includes seven quantum computations. These seven quantum computations can be converted into gate operations of qubit unit 1, gate operations of qubit unit 2, gate operations of qubit unit 3, gate operations of qubit unit 4, gate operations between qubit unit 1 and qubit unit 2, gate operations between qubit unit 2 and qubit unit 3, and gate operations between qubit unit 3 and qubit unit 4. Then, in the target qubit unit topology M1, there is a connection relationship between qubit unit 1 and qubit unit 2, a connection relationship between qubit unit 2 and qubit unit 3, and a connection relationship between qubit unit 3 and qubit unit 4.

[0134] In another example, as shown in FIG4B , the target qubit unit topology M2 for the target quantum computing task R2 includes three qubit units. In the target qubit unit topology M2 , there is a connection relationship between qubit unit 1 and qubit unit 2, and there is a connection relationship between qubit unit 1 and qubit unit 3.

[0135] In another example, as shown in Figure 4C, the target qubit unit topology M3 of the target quantum computing task R3 includes two qubit units. In the target qubit unit topology M3, there is a connection relationship between qubit unit 1 and qubit unit 2.

[0136] In another example, as shown in Figure 4D, the target qubit unit topology M4 for target quantum computing task R4 includes four qubit units. In target qubit unit topology M4, there is a connection relationship between qubit unit 1 and qubit unit 2, a connection relationship between qubit unit 2 and qubit unit 3, a connection relationship between qubit unit 3 and qubit unit 4, and a connection relationship between qubit unit 1 and qubit unit 4.

[0137] It should be noted that, as can be seen from the target qubit unit topology shown in Figures 4A-4D, the number of qubit units and the connection relationships between qubit units required for different target quantum computing tasks are generally different. It can be seen that the target qubit unit topology for a single target quantum computing task is non-universal and cannot be universally applied to other target quantum computing tasks. To address this, in one embodiment, it is proposed to determine a universal qubit unit network structure topology that is generally applicable to most tasks based on the form of the qubit unit network structure typically occupied by quantum computing tasks.

[0138] 5 , in a specific implementation of this embodiment, the target qubit unit topology is a universal qubit unit topology, and step 310 includes:

[0139] Step 510: Acquire multiple sample quantum computing tasks, where the multiple sample quantum computing tasks include a target quantum computing task;

[0140] Step 520: Obtain multiple sample quantum bit unit topologies for multiple sample quantum computing tasks;

[0141] Step 530: Take the union of multiple sample quantum bit unit topologies to obtain a universal quantum bit unit topology.

[0142] The following is a detailed description of steps 510-530.

[0143] In step 510, the sample quantum computing task is similar to the target quantum computing task, also including multiple quantum computations and corresponding qubit unit topology. However, the target quantum computing task is used for practical applications of quantum hardware resource virtualization, while the sample quantum computing task is used to generalize the universal qubit unit topology. As shown in Figure 6, there are four sample quantum computing tasks: sample quantum computing task Y1, sample quantum computing task Y2, sample quantum computing task Y3, and sample quantum computing task Y4. Sample quantum computing task Y1 is the target quantum computing task.

[0144] In step 520, a sample qubit unit topology for a sample quantum computing task can be obtained by calling the topology compilation model. Step 520 is similar to the process of obtaining the target qubit unit topology for a target quantum computing task described above and will not be repeated here.

[0145] In step 530, the multiple sample qubit unit topologies differ, effectively in terms of the number of qubit units and the connections between them. Therefore, the union of these sample qubit unit topologies is taken to obtain a universal qubit unit topology that includes all qubit units and all their connections.

[0146] In one example, as shown in Figure 6, a universal qubit unit topology is obtained by taking the union of the sample qubit unit topologies of four sample quantum computing tasks. Specifically, the sample qubit unit topologies of sample quantum computing tasks Y1, Y2, Y3, and Y4 indicate that the sample qubit units involved include sample qubit unit 1, sample qubit unit 2, sample qubit unit 3, and sample qubit unit 4, for a total of four sample qubit units. It can also be seen that a connection exists between sample qubit unit 1 and sample qubit unit 2, between sample qubit unit 2 and sample qubit unit 3, and between sample qubit unit 3 and sample qubit unit 4. Ultimately, the universal qubit unit topology shown on the right side of Figure 6 is obtained. In actual use, if the qubit unit topology of a target quantum computing task is the same as that of any of sample quantum computing tasks Y1, Y2, Y3, or Y4, then the universal qubit unit topology is applicable to computing that target quantum computing task.

[0147] The benefit of this embodiment is that a universal quantum bit unit topology can be summarized based on multiple sample quantum computing tasks. The universal quantum bit unit topology is applicable to different target quantum computing tasks, thereby improving the rate and accuracy of obtaining the target quantum bit unit topology.

[0148] Detailed description of step 320

[0149] In step 320, a qubit unit network in the quantum hardware resources is obtained. A qubit unit network is a network composed of multiple qubit units that can be used as a virtualized resource for quantum computing tasks. The qubit unit network includes original qubit units and first connections between original qubit units. In practice, original qubit units in the qubit unit network may be occupied by other quantum computing tasks, and occupied original qubit units cannot participate in processing the target quantum computing task. Therefore, in one embodiment, occupied qubit units should be deleted from the qubit unit network.

[0150] 7 , in the specific implementation of this embodiment, step 320 includes:

[0151] Step 710: Obtain original quantum bit units in the quantum hardware resource and a first connection relationship between the original quantum bit units;

[0152] Step 720: Obtain occupied quantum bit units;

[0153] Step 730: Obtain a second connection relationship between occupied qubit units and between an occupied qubit unit and an adjacent qubit unit;

[0154] Step 740: Remove occupied qubit units from the original qubit units, remove the second connection relationship from the first connection relationship, and obtain a qubit unit network, wherein the qubit units included in the qubit unit network are not occupied.

[0155] In step 710, as shown in FIG8A , the original qubit units in the quantum hardware resource include {qubit unit 1, qubit unit 2, qubit unit 3, qubit unit 4, qubit unit 5, qubit unit 6, qubit unit 7, qubit unit 8, qubit unit 9, qubit unit 10, qubit unit 11, qubit unit 12}, a total of 12 original qubit units. The first connection relationship between the original qubit units includes {first connection relationship L 1，2 , the first connection relationship L 2，3 , the first connection relationship L 3，4 , the first connection relationship L 4，5 , the first connection relationship L 5，6 , the first connection relationship L 6，7 , the first connection relationship L 7，8 , the first connection relationship L 8，9 , the first connection relationship L 9，10 , the first connection relationship L 10,11 , the first connection relationship L 11,12 , the first connection relationship L 1，12 , the first connection relationship L 2，11 , the first connection relationship L 3,10 , the first connection relationship L 4,9 , the first connection relationship L 5,8}, a total of 16 first connection relationships.

[0156] Continuing with FIG8A , the first connection relationship corresponding to the quantum bit unit 1 includes the first connection relationship L 1，2 , the first connection relationship L 1，12 The first connection relationship corresponding to the quantum bit unit 2 includes the first connection relationship L 1，2 , the first connection relationship L 2，3 , the first connection relationship L 2，11 The first connection relationship corresponding to the quantum bit unit 3 includes the first connection relationship L 2，3 , the first connection relationship L 3，4 , the first connection relationship L 3，10 The first connection relationship corresponding to the quantum bit unit 4 includes the first connection relationship L 3，4, the first connection relationship L 4，5 , the first connection relationship L 4，9 The first connection relationship corresponding to the quantum bit unit 5 includes the first connection relationship L 4，5 , the first connection relationship L 5，6 , the first connection relationship L 5，8 The first connection relationship corresponding to the quantum bit unit 6 includes the first connection relationship L 5，6 , the first connection relationship L 6，7 The first connection relationship corresponding to the quantum bit unit 7 includes the first connection relationship L 6，7 , the first connection relationship L 7，8 The first connection relationship corresponding to the quantum bit unit 8 includes the first connection relationship L 5，8 , the first connection relationship L 7，8 , the first connection relationship L 8，9 The first connection relationship corresponding to the quantum bit unit 9 includes the first connection relationship L 4，9 , the first connection relationship L 8，9 , the first connection relationship L 9，10 The first connection relationship corresponding to the quantum bit unit 10 includes the first connection relationship L 3，10 , the first connection relationship L 9，10 , the first connection relationship L 10，11 The first connection relationship corresponding to the quantum bit unit 11 includes the first connection relationship L 2，11 , the first connection relationship L 10，11 , the first connection relationship L 11，12 The first connection relationship corresponding to the quantum bit unit 12 includes the first connection relationship L1, 12, the first connection relationship L 11，12 .

[0157] In step 720, occupied qubit units refer to original qubit units that are occupied by other tasks or damaged. For example, as shown in FIG8B , among the 12 original qubit units of the quantum hardware resource, qubit unit 2 and qubit unit 10 are occupied qubit units.

[0158] In step 730, if an original qubit unit of the quantum hardware resource is an occupied qubit unit, it not only indicates that the original qubit unit is unavailable, but also the connection relationship connected to the original qubit unit is unavailable. Therefore, it is necessary to obtain the second connection relationship between the occupied qubit units and between the occupied qubit unit and the adjacent qubit unit. For example, as shown in FIG8B , the second connection relationship corresponding to qubit unit 2 includes the second connection relationship L1,2, the second connection relationship L 2，3 , the second connection relationship L 2，11The second connection relationship corresponding to the quantum bit unit 10 includes the second connection relationship L 3，10 , the second connection relationship L 9，10 , the second connection relationship L 10， 11 .

[0159] In step 740, the occupied qubit units are removed from the original qubit units, and the second connection relationship is removed from the first connection relationship to obtain a qubit unit network. For example, as shown in FIG8C , qubit unit 2 and qubit unit 10 are removed from the 12 original qubit units, and the original qubit units obtained include {qubit unit 1, qubit unit 3, qubit unit 4, qubit unit 5, qubit unit 6, qubit unit 7, qubit unit 8, qubit unit 9, qubit unit 11, qubit unit 12}, a total of 10 original qubit units. Then L is removed from the 16 first connection relationships. 1，2 , L 2，3 , L 2，11 , L 3，10 , L 9，10 , L 10，11 , the first connection relationship includes {first connection relationship L 3，4 , the first connection relationship L 4，5 , the first connection relationship L 5，6 , the first connection relationship L 6，7 , the first connection relationship L 7，8 , the first connection relationship L 8，9 , the first connection relationship L 11,12 , the first connection relationship L 1，12 , the first connection relationship L 4,9 , the first connection relationship L 5,8}, a total of 10 first connection relationships.

[0160] The benefit of this embodiment is that by removing the occupied qubit units and the connection relationships related to the occupied qubit units from the original qubit units in the quantum hardware resources and the first connection relationships between the original qubit units, the possibility of unavailable qubit units in the qubit unit network is reduced, thereby improving the availability of the qubit unit network.

[0161] Detailed description of step 330

[0162] In step 330, a plurality of candidate qubit unit subnetworks that conform to the target qubit unit topology are obtained from the qubit unit network. The candidate qubit unit subnetwork is a subset of the qubit unit network. In practice, if a candidate qubit unit subnetwork includes the qubit units in the target qubit unit topology and the connection relationship between the qubit units, it indicates that the candidate qubit unit conforms to the target qubit unit topology. Therefore, in one embodiment, assuming that the target qubit unit topology includes a first number of qubit units, the qubit unit network is screened based on the first number of qubit units, and then a candidate qubit unit subnetwork whose connection relationship is the same as the connection relationship between the qubit units in the target qubit unit topology is found from the screened qubit unit subnetwork.

[0163] 9 , in a specific implementation of this embodiment, the target qubit unit topology includes a first number of qubit units, and step 330 includes:

[0164] Step 910: traverse the qubit unit network to obtain a plurality of screened qubit unit sub-networks, wherein the screened qubit unit sub-networks include a first number of connected qubit units;

[0165] Step 920: Compare the third connection relationship between the qubit units in the target qubit unit topology with the fourth connection relationship between the qubit units in each screened qubit unit subnetwork to determine multiple candidate qubit unit subnetworks from the multiple screened qubit unit subnetworks.

[0166] In step 910, the screened qubit unit subnetwork is a subset of the qubit unit network and includes a first number of qubit units. As shown in Figure 10, it is assumed that the target qubit unit topology includes 4 qubit units and the qubit unit network includes 12 qubit units. After traversal, the obtained screened qubit unit subnetwork includes 4 connected qubit units. Figure 10 shows 5 screened qubit unit subnetworks (the part circled by the dotted box in the matrix in Figure 10), but in fact there are other screened qubit unit subnetworks. For example, qubit unit 1, qubit unit 2, qubit unit 3, and qubit unit 4 in the qubit unit network can also form a screened qubit unit subnetwork.

[0167] In step 920, the selected qubit unit subnetwork has the same topology as the target qubit unit in terms of the number of qubit units, but not necessarily the same connection relationship. Therefore, it is necessary to compare the third connection relationship with the fourth connection relationship, and select the selected qubit unit subnetwork with the same connection relationship as the candidate qubit unit subnetwork.

[0168] For example, as shown in Figure 10, five candidate qubit unit subnetworks can be determined from multiple screened qubit unit subnetworks. These five candidate qubit unit subnetworks include candidate qubit unit subnetwork W1, candidate qubit unit subnetwork W2, candidate qubit unit subnetwork W3, candidate qubit unit subnetwork W4, and candidate qubit unit subnetwork W5. Among them, candidate qubit unit subnetwork W1 includes qubit unit 1, qubit unit 2, qubit unit 11, and qubit unit 12. Candidate qubit unit subnetwork W2 includes qubit unit 2, qubit unit 3, qubit unit 10, and qubit unit 11. Candidate qubit unit subnetwork W3 includes qubit unit 3, qubit unit 4, qubit unit 9, and qubit unit 10. Candidate qubit unit subnetwork W4 includes qubit unit 4, qubit unit 5, qubit unit 8, and qubit unit 9. Candidate qubit unit subnetwork W5 includes qubit unit 5, qubit unit 6, qubit unit 7, and qubit unit 8.

[0169] It should be noted that the multiple candidate qubit unit subnetworks are distinct but may overlap. For example, the overlap between W1 and W2 includes qubit unit 2 and qubit unit 11. The overlap between W2 and W3 includes qubit unit 3 and qubit unit 10. The overlap between W3 and W4 includes qubit unit 4 and qubit unit 9. The overlap between W4 and W5 includes qubit unit 5 and qubit unit 8.

[0170] The benefit of the embodiment of steps 910-920 is that since the screened qubit unit subnetwork has fewer qubit units than the qubit unit network, a qubit unit subnetwork whose connection relationship conforms to the third connection relationship is found from the screened qubit unit subnetwork as a candidate qubit unit subnetwork, and the third connection relationship is the connection relationship between qubit units in the target qubit unit topology, thereby improving the efficiency of determining the candidate qubit unit subnetwork.

[0171] Detailed description of step 340

[0172] In step 340, the overall operation fidelity of each candidate qubit unit subnetwork is determined based on the self-operation fidelity of each qubit unit in each candidate qubit unit subnetwork and the mutual operation fidelity between adjacent qubit units.

[0173] It should be noted that when a quantum bit unit performs a quantum computing task, it is generally expected that the gate operation will achieve the expected result. The expected result refers to the theoretical gate operation result, which can also be called the expected gate operation result. However, due to various reasons, the results generally have deviations. The degree of closeness between the actual gate operation result (that is, the observed result of the state of the quantum bit after the gate operation) and the expected gate operation result is the fidelity. The fidelity of the gate operation represents the fidelity of the execution task. The fidelity of the gate operation is manifested in the embodiment of the present disclosure as self-operation fidelity and mutual operation fidelity.

[0174] Self-operation fidelity refers to the degree of closeness between the first actual operation result after a qubit unit performs a gate operation independently and the first expected operation result. The first expected operation result is the theoretical operation result of the qubit unit performing a gate operation independently. The higher the closeness, the higher the self-operation fidelity. For example, if the first actual operation result is 0.9 and the first expected operation result is 0.8, the self-operation fidelity is (0.8 - |0.8 - 0.9|) / 0.8 = 7 / 8.

[0175] Mutual fidelity refers to the degree of closeness between the second actual result of a gate operation between qubit units and the second expected result. The second expected result is the theoretical result of a gate operation between qubit units. The higher the degree of closeness, the higher the mutual fidelity. For example, if the second actual result is 0.7 and the second expected result is 0.9, the mutual fidelity is (0.9 - |0.9 - 0.7|) / 0.9 = 7 / 9.

[0176] In one embodiment, the overall operation fidelity can be determined by calculating the sum of the self-operation fidelity and the mutual operation fidelity. For example, the candidate quantum bit unit sub-network includes N quantum bit units. The self-operation fidelity of each quantum bit unit includes {N1, N2, ... N n-1 ,N n}, the mutual operation fidelity between adjacent quantum bit units includes {M1,M2,…M n-1 ,M n}. The overall operational fidelity is Q, where (N1+N2+…+N n-1 +N n +M1+M2+…+M n-1 +M n)=Q. The advantage of using and calculating the overall operation fidelity is that the influence of the self-operation fidelity and the mutual operation fidelity on determining the overall operation fidelity can be equally reflected.

[0177] For example, a candidate qubit unit subnetwork includes four qubit units. The self-operation fidelity of each qubit unit is {0.9, 0.8, 0.7, 0.8}, and the mutual operation fidelity between adjacent qubit units is {0.6, 0.4, 0.7, 0.6}. The overall operation fidelity is (0.9 + 0.8 + 0.7 + 0.8 + 0.6 + 0.4 + 0.7 + 0.6) = 5.5.

[0178] In one embodiment, the overall operation fidelity may be determined by calculating a weighted sum of the self-operation fidelity and the mutual operation fidelity.

[0179] 11 , in the specific implementation of this embodiment, step 340 includes:

[0180] Step 1110: Obtain a first weight of the self-operation fidelity and a second weight of the mutual operation fidelity;

[0181] Step 1120: Based on the first weight and the second weight, perform a weighted sum of the self-operation fidelity and the mutual operation fidelity to obtain the overall operation fidelity.

[0182] In step 1110, the first weight and the second weight can be set in advance. For example, the first weight is set to A, such as 0.9, and the second weight is set to B, such as 1.1. In other words, regardless of the quantum computing task, the fixed first weight and second weight can be used to calculate the overall computational fidelity.

[0183] In one embodiment, step 1110 includes:

[0184] Get the task type of the target quantum computing task;

[0185] Based on the task type, a preset mapping relationship between the task type and the weight is searched to obtain a first weight and a second weight.

[0186] Specifically, the task type refers to the type to which the target quantum computing task belongs in the preset type set. For example, the type set includes the first type, the second type, and the third type. In one embodiment, the task type of the target quantum computing task can be obtained by a classification model or other type determination method. The mapping relationship between task type and weight is shown in Table 1. When the task type is the first type, the corresponding first weight is A1 and the second weight is A2; when the task type is the second type, the corresponding first weight is B1 and the second weight is B2; when the task type is the third type, the corresponding first weight is C1 and the second weight is C2. The weight values ​​in Table 1 can be set based on experience or other reasonable calculations.

[0187] For example, in Table 1 above, A1 is 0.6, A2 is 1.4, B1 is 0.8, B2 is 1.2, C1 is 0.9, and C2 is 1.1. Assuming the task type is type 1, the first weight is 0.6 and the second weight is 1.4. Assuming the task type is type 2, the first weight is 0.8 and the second weight is 1.2. Assuming the task type is type 3, the first weight is 0.9 and the second weight is 1.1.

[0188] Table 1 Task type and weight mapping relationship table

[0189] After the first weight and the second weight are obtained in step 1110 , in step 1120 , a weighted sum is performed on the self-operation fidelity and the mutual operation fidelity based on the first weight and the second weight to obtain the overall operation fidelity.

[0190] The advantage of the embodiment of steps 1110 - 1120 is that different weights can be set for the self-operation fidelity and the mutual operation fidelity, thereby increasing the flexibility of determining the overall operation fidelity.

[0191] In one example, the quantum hardware resources include 12 qubit units. Among them, candidate qubit unit subnetwork W1 includes qubit unit 1, qubit unit 2, qubit unit 11, and qubit unit 12; candidate qubit unit subnetwork W2 includes qubit unit 2, qubit unit 3, qubit unit 10, and qubit unit 11; candidate qubit unit subnetwork W3 includes qubit unit 3, qubit unit 4, qubit unit 9, and qubit unit 10; candidate qubit unit subnetwork W4 includes qubit unit 4, qubit unit 5, qubit unit 8, and qubit unit 9; and candidate qubit unit subnetwork W5 includes qubit unit 5, qubit unit 6, qubit unit 7, and qubit unit 8.

[0192] The self-operation fidelity of qubit unit 1 is Z1. The self-operation fidelity of qubit unit 2 is Z2. The self-operation fidelity of qubit unit 3 is Z3. The self-operation fidelity of qubit unit 4 is Z4. The self-operation fidelity of qubit unit 5 is Z5. The self-operation fidelity of qubit unit 6 is Z6. The self-operation fidelity of qubit unit 7 is Z7. The self-operation fidelity of qubit unit 8 is Z8. The self-operation fidelity of qubit unit 9 is Z9. The self-operation fidelity of qubit unit 10 is Z 10 The self-operation fidelity of quantum bit unit 11 is Z 11 The self-operation fidelity of quantum bit unit 12 is Z 12 .

[0193] The mutual operation fidelity between qubit units 1 and 2 is H 1,2 The mutual operation fidelity between quantum bit units 2 and 3 is H 2,3 The mutual operation fidelity between qubit units 3 and 4 is H 3,4 The mutual operation fidelity between quantum bit units 4 and 5 is H 4,5 The mutual operation fidelity between quantum bit units 5 and 6 is H 5,6 The mutual operation fidelity between quantum bit units 6 and 7 is H 6,7 The mutual operation fidelity between quantum bit units 7 and 8 is H 7,8 The mutual operation fidelity between quantum bit units 8 and 9 is H 8,9 The mutual operation fidelity between quantum bit units 9 and 10 is H 9,10 The mutual operation fidelity between quantum bit units 10 and 11 is H 10,11 The mutual operation fidelity between quantum bit units 11 and 12 is H 11,12 The mutual operation fidelity between quantum bit units 1 and 12 is H 1,12 The mutual operation fidelity between quantum bit units 2 and 11 is H 2,12 The mutual operation fidelity between quantum bit units 3 and 10 is H 3,10 The mutual operation fidelity between quantum bit units 4 and 9 is H 4,9 The mutual operation fidelity between quantum bit units 5 and 8 is H 5,8 .

[0194] Assume that the first weight is B1 in the above Table 1, and the second weight is B2.

[0195] The overall operational fidelity of the candidate quantum bit unit sub-network W1 is B1*(Z1+Z2+Z 11 +Z 12 )+B2*(H 1,2 +H2,11 +H 11,12 +H 1,12 )=Q1.

[0196] The overall operational fidelity of the candidate quantum bit unit sub-network W2 is B1*(Z2+Z3+Z 10 +Z 11 )+B2*(H 2,3 +H 3,10 +H 10,11 +H 2,11 )=Q2.

[0197] The overall operational fidelity of the candidate quantum bit unit sub-network W3 is B1*(Z3+Z4+Z9+Z 10 )+B2*(H 3,4 +H 4,9 +H 9,10 +H 3,10 )=Q3.

[0198] The overall computational fidelity of the candidate quantum bit unit subnetwork W4 is B1*(Z4+Z5+Z8+Z9)+B2*(H 4,5 +H 5,8 +H 8,9 +H 4,9 )=Q4.

[0199] The overall operational fidelity of the candidate quantum bit unit sub-network W5 is B1*(Z5+Z6+Z7+Z8)+B2*(H 5,6 +H 6,7 +H 7,8 +H 5,8 )=Q5.

[0200] For example, as shown in FIG12 , the quantum hardware resource includes 12 qubit units. The self-operation fidelity Z1 of qubit unit 1 is 0.9. The self-operation fidelity Z2 of qubit unit 2 is 0.8. The self-operation fidelity Z3 of qubit unit 3 is 0.4. The self-operation fidelity Z4 of qubit unit 4 is 0.7. The self-operation fidelity Z5 of qubit unit 5 is 0.5. The self-operation fidelity Z6 of qubit unit 6 is 0.6. The self-operation fidelity Z7 of qubit unit 7 is 0.8. The self-operation fidelity Z8 of qubit unit 8 is 0.5. The self-operation fidelity Z9 of qubit unit 9 is 0.6. The self-operation fidelity Z10 of qubit unit 10 is 0.8. 10 The self-operation fidelity Z of the quantum bit unit 11 is 0.8. 11 The self-operation fidelity Z of the quantum bit unit 12 is 0.4. 12 is 0.8.

[0201] Continuing with Figure 12, the mutual operation fidelity H between quantum bit units 1 and 2 is 1,2 The mutual operation fidelity H between quantum bit units 2 and 3 is 0.6. 2,3 The mutual operation fidelity H between quantum bit units 3 and 4 is 0.7. 3,4 The mutual operation fidelity H between quantum bit units 4 and 5 is 0.9. 4,5 The mutual operation fidelity H between quantum bit units 5 and 6 is 0.4. 5,6 The mutual operation fidelity H between quantum bit units 6 and 7 is 0.4. 6,7 The mutual operation fidelity H between quantum bit units 7 and 8 is 0.8. 7,8 The mutual operation fidelity H between quantum bit units 8 and 9 is 0.9. 8,9 The mutual operation fidelity H between quantum bit units 9 and 10 is 0.8. 9,10 The mutual operation fidelity H between quantum bit units 10 and 11 is 0.7. 10,11 The mutual operation fidelity H between quantum bit units 11 and 12 is 0.4. 11,12 The mutual operation fidelity H between quantum bit units 1 and 12 is 0.7. 1,12 The mutual operation fidelity H between quantum bit units 2 and 11 is 0.4. 2,11 The mutual operation fidelity H between quantum bit units 3 and 10 is 0.5. 3,10 The mutual operation fidelity H between quantum bit units 4 and 9 is 0.3. 4,9 The mutual operation fidelity H between quantum bit units 5 and 8 is 0.9. 5,8 is 0.7.

[0202] Assume that the first weight B1 is 0.8 and the second weight B2 is 1.2. Continue as shown in Figure 12;

[0203] The overall computational fidelity of the candidate quantum bit unit subnetwork W1 is 0.8*(0.9+0.8+0.4+0.8)+1.2*(0.6+0.5+0.7+0.4)=4.96;

[0204] The overall computational fidelity of the candidate qubit unit subnetwork W2 is 0.8*(0.8+0.4+0.8+0.4)+1.2*(0.7+0.3+0.4+0.5)=4.2;

[0205] The overall computational fidelity of the candidate quantum bit unit subnetwork W3 is 0.8*(0.4+0.7+0.6+0.8)+1.2*(0.9+0.9+0.7+0.3)=5.39;

[0206] The overall computational fidelity of candidate qubit unit subnetwork W4 is 0.8*(0.7+0.5+0.5+0.6)+1.2*(0.4+0.7+0.8+0.9)=5.2;

[0207] The overall operational fidelity of the candidate quantum bit unit subnetwork W5 is 0.8*(0.5+0.6+0.8+0.5)+1.2*(0.4+0.8+0.9+0.7)=5.28.

[0208] Detailed description of step 350

[0209] In step 350 , a target qubit unit subnetwork is determined based on the overall computational fidelity of the plurality of candidate qubit unit subnetworks, with the target qubit unit subnetwork serving as a virtualized resource for the target quantum computing task.

[0210] In one embodiment, multiple overall computational fidelities may be sorted by size, and the candidate qubit unit subnetwork corresponding to the highest overall computational fidelity from the sorted results may be selected as the target qubit unit subnetwork. Alternatively, multiple overall computational fidelities may be compared with a fidelity threshold, and the candidate qubit unit subnetwork with an overall computational fidelity greater than the fidelity threshold may be selected as the target qubit subnetwork.

[0211] The benefit of this embodiment is that the target qubit unit sub-network is determined only based on the overall operation fidelity, which can improve the efficiency of determining the target qubit unit sub-network.

[0212] It should be noted that overall computational fidelity refers to the computational accuracy achieved when the candidate qubit unit subnetwork is fault-free. However, some candidate qubit unit subnetworks are prone to failures. If a candidate qubit unit subnetwork has a high failure rate, it should not be considered a target qubit unit subnetwork, even if its overall computational fidelity is high.

[0213] 13 , in the specific implementation of this embodiment, step 350 includes:

[0214] Step 1310: Determine the overall failure rate of each candidate qubit unit subnetwork based on the failure rate of each qubit unit in each candidate qubit unit subnetwork;

[0215] Step 1320: Determine a target qubit unit subnetwork based on the overall operational fidelity and overall failure rate of multiple candidate qubit unit subnetworks.

[0216] The benefit of this embodiment is that the target quantum bit unit subnetwork is determined by the overall operational fidelity of multiple candidate quantum bit unit subnetworks and the overall failure rate of multiple candidate quantum bit unit subnetworks. Taking into account the overall failure rate and overall operational fidelity of the candidate quantum bit unit subnetworks, the stability of the determined target quantum bit unit subnetwork can be improved, thereby avoiding the overall failure rate of the target quantum bit unit subnetwork being high, which affects the execution of the target quantum computing task.

[0217] In step 1310, each candidate qubit unit subnetwork includes multiple qubit units. Each qubit unit has a corresponding failure rate. The failure rates of each qubit unit are calculated to obtain the overall failure rate. For example, if the candidate qubit unit subnetwork includes four qubit units with failure rates of 0.01, 0.03, 0.02, and 0.01, the overall failure rate is (0.1 + 0.3 + 0.2 + 0.1) / 4 = 0.0175.

[0218] After the overall failure rate is determined in step 1310 , in step 1320 , a target qubit unit subnetwork is determined based on the overall operational fidelity and overall failure rate of the plurality of candidate qubit unit subnetworks.

[0219] For example, as shown in Figure 14, there are five candidate qubit unit subnetworks. Candidate qubit unit subnetwork W1 has an overall computational fidelity of 4.96 and an overall failure rate of 0.02. Candidate qubit unit subnetwork W2 has an overall computational fidelity of 4.2 and an overall failure rate of 0.05. Candidate qubit unit subnetwork W3 has an overall computational fidelity of 5.39 and an overall failure rate of 0.09. Candidate qubit unit subnetwork W4 has an overall computational fidelity of 5.2 and an overall failure rate of 0.03. Candidate qubit unit subnetwork W5 has an overall computational fidelity of 5.28 and an overall failure rate of 0.01. In this example, W3 has the highest overall computational fidelity, but its overall failure rate is 0.09, which is too high to be a suitable target qubit unit subnetwork. Although W5's overall computational fidelity is slightly lower than W3's, its overall failure rate is 0.01. After comprehensively considering the overall operational fidelity and overall failure rate of multiple candidate quantum bit unit sub-networks, W5 is selected as the target quantum bit unit sub-network.

[0220] In one embodiment, step 1320 includes:

[0221] determining a first score based on the overall operational fidelity of each candidate qubit unit subnetwork;

[0222] determining a second score based on the overall failure rate of each candidate qubit unit subnetwork;

[0223] determining a total score for each candidate qubit unit subnetwork based on the first score and the second score;

[0224] Based on the total scores of the multiple candidate qubit unit sub-networks, a target qubit unit sub-network is determined.

[0225] Specifically, based on the overall operational fidelity of each candidate quantum bit unit sub-network, the first score is determined. This can be done by looking up a comparison table between the overall operational fidelity range and the first score, or by using a formula method.

[0226] (1) The overall operation fidelity range and the first score comparison table lists the corresponding relationship between the overall operation fidelity range and the first score. First, the overall operation fidelity range is obtained based on the overall operation fidelity. Then, the overall operation fidelity range and the first score comparison table are searched based on this to obtain the first score. The following Table 2 is an example of the overall operation fidelity range and the first score comparison table:

[0227] Table 2 Correspondence between the overall operation fidelity range and the first score

[0228] For example, if the overall operation fidelity is 0.8, then the overall operation fidelity range is 0.7-0.9. By looking up Table 2, the corresponding first score is 90.

[0229] The above method of searching the comparison table between the overall operation fidelity range and the first score has the advantages of being simple and easy to implement with low processing overhead.

[0230] (2) When using the formula method, the first score can be set to be proportional to the overall operation fidelity, for example: F1 = K1 * G1 Formula 1

[0231] Here, F1 represents the first score, G1 represents the overall computational fidelity, and K1 is a preset constant that can be set as needed. For example, if K1 = 112.5 and G1 = 0.8 is substituted, the first score F1 = 90.

[0232] The advantages of the above method of determining the first score by using a formula are high accuracy, and the formula can be adjusted as needed, which is highly flexible.

[0233] Based on the overall failure rate of the candidate quantum bit unit sub-network, the second score is determined. This can be done by looking up a comparison table between the overall failure rate range and the second score, or by using a formula method.

[0234] (1) The overall failure rate range and second score comparison table lists the corresponding relationship between the overall failure rate range and the second score. First, the overall failure rate range is obtained based on the overall failure rate. Then, the overall failure rate range and second score comparison table are searched based on this to obtain the second score. Table 3 below is an example of an overall failure rate range and second score comparison table:

[0235] Table 3 Correspondence between the overall failure rate range and the second score

[0236] For example, if the overall failure rate is 0.02, then the overall failure rate range is 0.01-0.03. By looking up Table 3, the corresponding second score is 90.

[0237] The above method of searching the comparison table between the overall failure rate range and the second score has the advantages of being simple and easy to implement with low processing overhead.

[0238] (2) When the formula method is used, the second score can be set to be inversely proportional to the overall failure rate, for example: F2 = K2*G2+b Formula 2

[0239] Where F2 represents the second score, G2 represents the overall failure rate, K2 and b are preset constants that can be set according to actual needs. For example, if K2 = -100 and b = 92, substituting G2 = 0.02, the second score F2 = 90.

[0240] The advantages of the above method of determining the second score by using a formula are high accuracy, and the formula can be adjusted as needed, which is highly flexible.

[0241] Based on the first score and the second score, the total score of the candidate quantum bit unit sub-network is determined, which can be done by calculating the average or weighted average of the first score and the second score.

[0242] When calculating the average of the first and second scores as the total score, for example, if the first score is 90 and the second score is 80, the total score is (90+80) / 2=85. The advantage of using the average to calculate the total score is that it can equally reflect the impact of the overall operation fidelity and the overall failure rate on determining the total score.

[0243] When calculating the total score as the weighted average of the first and second scores, for example, if the weights for overall operational fidelity and overall failure rate are set to 0.6 and 0.4, respectively, and the first score is 90 and the second score is 80, the total score is 90 × 0.6 + 80 × 0.4 = 86. The advantage of using a weighted average to calculate the total score is that different weights can be set for overall operational fidelity and overall failure rate, providing greater flexibility in determining the total score.

[0244] A target qubit unit subnetwork is determined based on the total scores of multiple candidate qubit unit subnetworks. The multiple total scores can be sorted by size, and the candidate qubit unit subnetwork corresponding to the largest total score from the sorted results is selected as the target qubit unit subnetwork. Alternatively, the multiple total scores are compared with a first threshold, and one of the multiple candidate qubit unit subnetworks with a total score greater than the first threshold is selected as the target qubit subnetwork.

[0245] The benefit of this embodiment is that the total score is determined based on the first score of the overall operational fidelity and the second score of the overall failure rate, so that the target quantum bit unit network is determined based on the total score, thereby improving the determination accuracy.

[0246] It should be noted that the above embodiment determines the target qubit unit network based on overall computational fidelity and overall failure rate, with a focus on ensuring the stability of the target qubit unit network. In one embodiment, to ensure that the target qubit unit network can meet the computational accuracy requirements of the target quantum computing task, it is necessary to determine the target qubit unit subnetwork based on the importance of the target quantum computing task.

[0247] 15 , in the specific implementation of this embodiment, step 350 includes:

[0248] Step 1510: Obtain the importance of the target quantum computing task;

[0249] Step 1520: Determine the fidelity level corresponding to the importance.

[0250] Step 1530: Determine the candidate quantum bit unit subnetwork whose overall computational fidelity belongs to the fidelity level among the multiple candidate quantum bit unit subnetworks as the target quantum bit unit subnetwork.

[0251] In step 1510, the importance indicates the importance of the target quantum computing task. For example, the more mutual operations a target quantum computing task contains, the higher its importance. The importance of the target quantum computing task can be automatically annotated when the target quantum computing task is generated, thereby obtaining the importance of the target quantum computing task. Alternatively, when determining the target quantum bit unit subnetwork for the target quantum computing task, the target quantum computing task is parsed, and the importance is determined based on the parsed quantum computations. This embodiment does not specifically limit the method for obtaining the importance.

[0252] At step 1520, the fidelity level corresponding to the importance is determined. This can be done by searching a preset importance-level mapping relationship to obtain the fidelity level. The mapping relationship between importance and fidelity levels is shown in Table 4. Assuming an importance of 0.9-1, the fidelity levels obtained are 5-10. Assuming an importance of 0.7-0.9, the fidelity levels obtained are 4-5. Assuming an importance of 0.5-0.7, the fidelity levels obtained are 1-4.

[0253] Table 4. Mapping relationship between importance and fidelity level

[0254] In step 1530, a candidate qubit unit subnetwork whose overall computational fidelity falls within the fidelity level among the multiple candidate qubit unit subnetworks is determined as a target qubit unit subnetwork. For example, as shown in FIG16 , the overall computational fidelity of candidate qubit unit subnetwork W1 is 4.96. The overall computational fidelity of candidate qubit unit subnetwork W2 is 4.2. The overall computational fidelity of candidate qubit unit subnetwork W3 is 5.39. The overall computational fidelity of candidate qubit unit subnetwork W4 is 5.2. The overall computational fidelity of candidate qubit unit subnetwork W5 is 5.28. Assuming the importance of the target quantum computing task is 0.9, looking up Table 4 yields a fidelity level of 5-10. The overall operational fidelity belonging to the fidelity level 5-10 is 5.39, 5.2, and 5.28, so any one of the candidate quantum bit unit subnetwork W3, the candidate quantum bit unit subnetwork W4, and the candidate quantum bit unit subnetwork W5 can be used as the target quantum bit unit subnetwork.

[0255] The benefit of the embodiment of steps 1510-1530 is that the target quantum bit unit network is determined by using the task type of the target quantum computing task, so that the overall operational fidelity of the target quantum bit unit is linked to the task type, thereby improving the correlation between the target quantum computing task and the target quantum bit unit network.

[0256] It should be noted that both embodiments of step 350 above are for determining a target qubit unit subnetwork for a target quantum computing task. However, in one embodiment, there are multiple target quantum computing tasks, and multiple target qubit unit subnetworks need to be determined simultaneously.

[0257] 17 , in a specific implementation of this embodiment, the number of target quantum computing tasks is the second number, the number of candidate quantum bit unit subnetworks is the third number, and the second number is less than the third number. Then, step 350 includes:

[0258] Step 1710: sort the third number of candidate quantum bit unit sub-networks in descending order according to overall computational fidelity;

[0259] Step 1720: Determine the top second number of candidate quantum bit unit sub-networks in the sort as the second number of target quantum bit unit sub-networks for the second number of target quantum computing tasks.

[0260] In one example, as shown in FIG18 , the second number is 3, specifically including target quantum computing task R1, target quantum computing task R2, and target quantum computing task R3. The third number is 5, specifically including candidate quantum bit unit subnetwork W1, candidate quantum bit unit subnetwork W2, candidate quantum bit unit subnetwork W3, candidate quantum bit unit subnetwork W4, and candidate quantum bit unit subnetwork W5. After being sorted in descending order according to overall computational fidelity, the order is {W3, W5, W4, W1, W2}. The first three candidate quantum bit unit subnetworks in the order, namely W3, W5, and W4, are determined as the three target quantum bit unit subnetworks for target quantum computing task R1, target quantum computing task R2, and target quantum computing task R3. For example, candidate quantum bit unit subnetwork W3 is used for target quantum computing task R1, candidate quantum bit unit subnetwork W5 is used for target quantum computing task R2, and candidate quantum bit unit subnetwork W4 is used for target quantum computing task R3.

[0261] The advantage of the embodiment of steps 1710-1720 is that a second number of target quantum bit unit sub-networks can be determined for a second number of target quantum computing tasks at the same time, thereby improving the determination efficiency.

[0262] It should be noted that, in step 1710, the third number of candidate quantum bit unit subnetworks may also be sorted in ascending order according to the overall computational fidelity; then, in step 1720, the second number of candidate quantum bit unit subnetworks in the sorting are determined as the second number of target quantum bit unit subnetworks for the second number of target quantum computing tasks.

[0263] It should be noted that the second number of target qubit unit subnetworks may be relatively dispersed across the quantum hardware resources. In this case, while this approach may be applicable if the second number of target quantum computing tasks are uncorrelated, if the second number of target quantum computing tasks are correlated, it will be difficult to achieve significantly higher processing efficiency with dispersed target qubit unit subnetworks. Therefore, in one embodiment, it is necessary to combine candidate qubit unit subnetworks and determine the second number of target qubit unit subnetworks based on the concentration of the combined networks.

[0264] 19 , in a specific implementation of this embodiment, the target quantum computing task is the associated second number of target quantum computing tasks, the candidate qubit unit subnetwork is the third number of candidate qubit unit subnetworks, and the second number is less than the third number. Step 350 includes:

[0265] Step 1910: traverse the third number of candidate qubit unit sub-networks to obtain a plurality of candidate sub-network combinations, where the candidate sub-network combinations include the second number of candidate qubit unit sub-networks.

[0266] Step 1920: Determine the sum of the overall operational fidelity of the second number of candidate qubit unit sub-networks in the candidate sub-network combination;

[0267] Step 1930: Determine the concentration of a second number of candidate qubit unit sub-networks in the candidate sub-network combination;

[0268] Step 1940: Based on the sum and concentration of the multiple candidate sub-network combinations, determine a target sub-network combination from the multiple candidate sub-network combinations as a second number of target quantum bit unit sub-networks for the second number of target quantum computing tasks.

[0269] In step 1910, as shown in FIG20A , the second number is 3 and the third number is 5. From the five candidate qubit unit subnetworks W1, W2, W3, W4, and W5, 10 candidate subnetwork combinations are obtained, each of which contains three candidate qubit unit subnetworks. The candidate subnetwork combinations are {(W1, W2, W3), (W1, W2, W4), (W1, W2, W5), (W1, W3, W4), (W1, W3, W5), (W1, W4, W5), (W2, W3, W4), (W2, W3, W5), (W2, W4, W5), (W3, W4, W5)}.

[0270] At step 1920, as shown in FIG20B , the sum of the overall computational fidelity of (W1, W2, W3) is: 4.96 + 4.2 + 5.39 = 15.55. The sum of the overall computational fidelity of (W1, W2, W4) is: 4.96 + 4.2 + 5.2 = 14.36. The sum of the overall computational fidelity of (W1, W2, W5) is: 4.96 + 4.2 + 5.28 = 14.44. The sum of the overall computational fidelity of (W1, W3, W4) is: 4.96 + 5.39 + 5.2 = 15.55. The sum of the overall computational fidelity of (W1, W3, W5) is: 4.96 + 5.39 + 5.28 = 15.63. The sum of the overall computational fidelity of (W1, W4, W5): 4.96 + 5.2 + 5.28 = 15.44. The sum of the overall computational fidelity of (W2, W3, W4): 4.2 + 5.39 + 5.2 = 14.79. The sum of the overall computational fidelity of (W2, W3, W5): 4.2 + 4.39 + 5.28 = 13.87. The sum of the overall computational fidelity of (W2, W4, W5): 4.2 + 5.2 + 5.28 = 14.68. The sum of the overall computational fidelity of (W3, W4, W5): 5.39 + 5.2 + 5.28 = 15.87.

[0271] At step 1930, the concentration of the second number of candidate qubit unit sub-networks in the candidate sub-network combination is determined. The concentration indicates the degree of concentration of the distances between each pair of candidate qubit unit sub-networks in the candidate sub-network combination.

[0272] In one embodiment, step 1930 includes:

[0273] Determining the center point distances between each of the second number of candidate quantum bit unit sub-networks in the candidate sub-network combination;

[0274] The reciprocal of the average distance of the center point distance is determined as the concentration of the second number of candidate quantum bit unit sub-networks.

[0275] For example, as shown in FIG20C , a horizontal rectangular coordinate system is established, and the center point distances between each of the three candidate quantum bit unit sub-networks in the candidate sub-network combination are obtained from the horizontal rectangular coordinate system. Specifically, for the candidate sub-network combination (W1, W2, W3), the center point distance between W1 and W2 = 2, the center point distance between W1 and W3 = 4, and the center point distance between W2 and W3 = 2. For the candidate sub-network combination (W1, W2, W4), the center point distance between W1 and W2 = 2, the center point distance between W1 and W4 = 6, and the center point distance between W2 and W4 = 4. For the candidate sub-network combination (W1, W2, W5), the center point distance between W1 and W2 = 2, the center point distance between W1 and W5 = 8, and the center point distance between W2 and W5 = 6. For the candidate sub-network combination (W1, W3, W4), the center point distance between W1 and W3 = 4, the center point distance between W1 and W4 = 6, and the center point distance between W3 and W4 = 2. For the candidate sub-network combination (W1, W3, W5), the center point distance between W1 and W3 = 4, the center point distance between W1 and W5 = 8, and the center point distance between W3 and W5 = 4. For the candidate sub-network combination (W1, W4, W5), the center point distance between W1 and W4 = 6, the center point distance between W1 and W5 = 8, and the center point distance between W4 and W5 = 2. For the candidate sub-network combination (W2, W3, W4), the center point distance between W2 and W3 = 2, the center point distance between W2 and W4 = 4, and the center point distance between W3 and W4 = 2. For the candidate sub-network combination (W2, W3, W5), the center point distance between W2 and W3 = 2, the center point distance between W2 and W5 = 6, and the center point distance between W3 and W5 = 4. For the candidate sub-network combination (W2, W4, W5), the center point distance between W2 and W4 = 4, the center point distance between W2 and W5 = 6, and the center point distance between W4 and W5 = 2. For the candidate sub-network combination (W3, W4, W5), the center point distance between W3 and W4 = 2, the center point distance between W3 and W5 = 4, and the center point distance between W4 and W5 = 2.

[0276] Continuing with Figure 20C , the concentration of (W1, W2, W3) is: 1 / (2+4+2) = 1 / 8. The concentration of (W1, W2, W4) is: 1 / (2+6+4) = 1 / 12. The concentration of (W1, W2, W5) is: 1 / (2+8+6) = 1 / 16. The concentration of (W1, W3, W4) is: 1 / (4+6+2) = 1 / 12. The concentration of (W1, W3, W5) is: 1 / (4+8+4) = 1 / 16. The concentration of (W1, W4, W5) is: 1 / (6+8+2) = 1 / 16. The concentration of (W2, W3, W4) is: 1 / (2+4+2) = 1 / 8. The concentration of (W2, W3, W5) is: 1 / (2+6+4) = 1 / 12. The concentration of (W2, W4, W5) is: 1 / (4+6+2) = 1 / 12. The concentration of (W3, W4, W5) is: 1 / (2+4+2) = 1 / 8.

[0277] After determining the sum and concentration of the multiple candidate sub-network combinations, in step 1940, based on the sum and concentration of the multiple candidate sub-network combinations, a target sub-network combination is determined from the multiple candidate sub-network combinations as the second number of target quantum bit unit sub-networks for the second number of target quantum computing tasks.

[0278] In one embodiment, step 1940 includes:

[0279] determining a third score based on the sum of each candidate sub-network combination;

[0280] determining a fourth score based on the concentration of each candidate sub-network combination;

[0281] Determine a total score for each candidate sub-network combination based on the third score and the fourth score;

[0282] Based on the total scores of multiple candidate sub-network combinations, the target sub-network combination is determined.

[0283] In this embodiment, the higher the sum of a candidate subnetwork combination, the higher the third score. The higher the concentration of a candidate subnetwork combination, the higher the fourth score. The total score of a candidate subnetwork combination can be determined by calculating the average or weighted average of the third score and the fourth score. Based on the total scores of multiple candidate subnetwork combinations, the target quantum bit unit subnetwork is determined. The total scores of multiple candidate subnetwork combinations can be sorted by size, and the candidate subnetwork combination corresponding to the largest total score from the sorting results is selected as the target quantum bit unit subnetwork. Alternatively, the multiple total scores are compared with the second threshold, and one of the multiple candidate subnetwork combinations whose total scores are greater than the second threshold is selected as the target subnetwork combination.

[0284] The benefit of this embodiment of steps 1910-1940 is that the accuracy of determining the second number of target quantum ratio unit sub-networks for the associated second number of target quantum computing tasks is improved based on the sum of the overall operational fidelity and the concentration.

[0285] Implementation details of the quantum hardware resource virtualization method of the disclosed embodiment

[0286] 21 , the implementation details of the quantum hardware resource virtualization method according to the embodiment of the present disclosure will be described in detail.

[0287] In step 2110, a plurality of sample quantum computing tasks are obtained, wherein the plurality of sample quantum computing tasks include a target quantum computing task; a plurality of sample qubit unit topologies for the plurality of sample quantum computing tasks are obtained; a union of the plurality of sample qubit unit topologies is taken to obtain a universal qubit unit topology, and the universal qubit unit topology is used as the target qubit unit topology;

[0288] In step 2120, original qubit units in the quantum hardware resource and a first connection relationship between the original qubit units are obtained; occupied qubit units are obtained; second connection relationships between the occupied qubit units and between the occupied qubit units and adjacent qubit units are obtained; the occupied qubit units are removed from the original qubit units, and the second connection relationship is removed from the first connection relationship to obtain a qubit unit network;

[0289] In step 2130, the qubit unit network is traversed to obtain a plurality of screened qubit unit subnetworks, each of the screened qubit unit subnetworks including a first number of connected qubit units; the third connection relationship between the qubit units in the target qubit unit topology is compared with the fourth connection relationship between the qubit units in each of the screened qubit unit subnetworks, so as to determine a plurality of candidate qubit unit subnetworks from the plurality of screened qubit unit subnetworks;

[0290] In step 2140, the overall computational fidelity of each candidate qubit unit subnetwork is determined based on the self-computational fidelity of each qubit unit in each candidate qubit unit subnetwork and the mutual computational fidelity between adjacent qubit units; the overall failure rate of each candidate qubit unit subnetwork is determined based on the failure rate of each qubit unit in each candidate qubit unit subnetwork;

[0291] In step 2150, a target qubit unit subnetwork is determined based on the overall operational fidelity and overall failure rate of the plurality of candidate qubit unit subnetworks, and the target qubit unit subnetwork serves as a virtualized resource for the target quantum computing task.

[0292] The benefits of steps 2110-2150 include, but are not limited to, minimizing the probability that the overall computational result generated by the target qubit unit subnetwork will be inconsistent with the expected computational result of the target quantum computing task, i.e., minimizing distortion. This division of virtualized quantum hardware resources reduces the likelihood of resource waste, improves resource utilization efficiency, and ensures computational performance.

[0293] Description of the apparatus and device of the present disclosure

[0294] In each specific embodiment of the present application, when it comes to the need to perform relevant processing based on data related to the characteristics of an object, such as object attribute information or attribute information sets, the permission or consent of the object will be obtained first, and the collection, use and processing of such data will comply with relevant laws, regulations and standards. In addition, when the embodiment of the present application needs to obtain object attribute information, the object's separate permission or separate consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the object's separate permission or separate consent, the necessary object-related data for the normal operation of the embodiment of the present application will be obtained.

[0295] 22 , which is a schematic diagram of the structure of a quantum hardware resource virtualization device 2200 provided in an embodiment of the present disclosure, the quantum hardware resource virtualization device 2200 includes:

[0296] A first acquisition unit 2210 is configured to acquire a target quantum bit unit topology for a target quantum computing task;

[0297] A second acquisition unit 2220 is used to acquire a quantum bit unit network in the quantum hardware resources;

[0298] A third acquisition unit 2230 is configured to acquire, in the qubit unit network, a plurality of candidate qubit unit sub-networks that conform to the target qubit unit topology;

[0299] A first determining unit 2240 is configured to determine the overall computational fidelity of the candidate qubit unit subnetwork based on the self-computational fidelity of each qubit unit in the candidate qubit unit subnetwork and the mutual computational fidelity between adjacent qubit units;

[0300] The second determining unit 2250 is used to determine a target quantum bit unit subnetwork based on the overall operational fidelity of multiple candidate quantum bit unit subnetworks, and the target quantum bit unit subnetwork serves as a virtualized resource for the target quantum computing task.

[0301] Optionally, the first determining unit 2240 is specifically configured to:

[0302] Obtaining a first weight of the self-operation fidelity and a second weight of the mutual operation fidelity;

[0303] Based on the first weight and the second weight, a weighted sum is performed on the self-operation fidelity and the mutual operation fidelity to obtain the overall operation fidelity.

[0304] Optionally, the first determining unit 2240 is further configured to:

[0305] Get the task type of the target quantum computing task;

[0306] Based on the task type, a preset mapping relationship between the task type and the weight is searched to obtain a first weight and a second weight.

[0307] Optionally, the target qubit unit topology is a universal qubit unit topology; the first acquisition unit 2210 is specifically configured to:

[0308] Acquire multiple sample quantum computing tasks, wherein the multiple sample quantum computing tasks include a target quantum computing task;

[0309] Obtaining multiple sample quantum bit unit topologies for multiple sample quantum computing tasks;

[0310] Take the union of multiple sample qubit unit topologies to obtain the universal qubit unit topology.

[0311] Optionally, the second acquiring unit 2220 is specifically configured to:

[0312] Obtaining original quantum bit units in the quantum hardware resource and a first connection relationship between the original quantum bit units;

[0313] Obtain occupied quantum bit units;

[0314] Obtaining second connection relationships between occupied qubit units and between occupied qubit units and adjacent qubit units;

[0315] The occupied quantum bit units are removed from the original quantum bit units, and the second connection relationship is removed from the first connection relationship to obtain a quantum bit unit network.

[0316] Optionally, the target qubit unit topology includes a first number of qubit units; and the third acquisition unit 2230 is specifically configured to:

[0317] Traversing the qubit unit network to obtain a plurality of screened qubit unit sub-networks, wherein the screened qubit unit sub-networks include a first number of connected qubit units;

[0318] The third connection relationship between the quantum bit units in the target quantum bit unit topology is compared with the fourth connection relationship between the quantum bit units in each screened quantum bit unit sub-network to determine multiple candidate quantum bit unit sub-networks from the multiple screened quantum bit unit sub-networks.

[0319] Optionally, the second determining unit 2250 is specifically configured to:

[0320] determining an overall failure rate for each candidate qubit unit subnetwork based on the failure rates of the individual qubit units in each candidate qubit unit subnetwork;

[0321] The target quantum bit unit sub-network is determined based on the overall operational fidelity and overall failure rate of multiple candidate quantum bit unit sub-networks.

[0322] Optionally, the second determining unit 2250 is specifically configured to:

[0323] Obtain the importance of the target quantum computing task;

[0324] Determine the fidelity level corresponding to the importance;

[0325] A candidate quantum bit unit subnetwork whose overall operational fidelity belongs to the fidelity level among multiple candidate quantum bit unit subnetworks is determined as a target quantum bit unit subnetwork.

[0326] Optionally, the number of target quantum computing tasks is a second number, and the number of candidate quantum bit unit subnetworks is a third number, wherein the second number is less than the third number; the second determining unit 2250 is specifically configured to:

[0327] sorting the third number of candidate quantum bit unit sub-networks in descending order according to overall computational fidelity;

[0328] The top second number of candidate quantum bit unit sub-networks in the ranking are determined as the second number of target quantum bit unit sub-networks for the second number of target quantum computing tasks.

[0329] Optionally, the target quantum computing tasks are a second number of associated target quantum computing tasks, and the candidate quantum bit unit subnetworks are a third number of candidate quantum bit unit subnetworks, wherein the second number is smaller than the third number;

[0330] Optionally, the second determining unit 2250 is specifically configured to:

[0331] Traversing a third number of candidate quantum bit unit sub-networks to obtain a plurality of candidate sub-network combinations, where the candidate sub-network combinations include a second number of candidate quantum bit unit sub-networks;

[0332] Determining a sum of the overall operational fidelity of a second number of candidate qubit unit sub-networks in the candidate sub-network combination;

[0333] determining a concentration of a second number of candidate qubit unit sub-networks in the candidate sub-network combination;

[0334] Based on the sum of the overall operational fidelity and the concentration of the multiple candidate sub-network combinations, a target sub-network combination is determined from the multiple candidate sub-network combinations as the second number of target quantum bit unit sub-networks for the second number of target quantum computing tasks.

[0335] Optionally, the second determining unit 2250 is further configured to:

[0336] Determining the center point distances between each of the second number of candidate quantum bit unit sub-networks in the candidate sub-network combination;

[0337] The reciprocal of the average distance of the center point distance is determined as the concentration of the second number of candidate quantum bit unit sub-networks.

[0338] Referring to FIG23 , FIG23 is a block diagram of a portion of a terminal for implementing the quantum hardware resource virtualization method according to an embodiment of the present disclosure. The terminal includes components such as a radio frequency (RF) circuit 2310, a memory 2315, an input unit 2330, a display unit 2340, a sensor 2350, an audio circuit 2360, a wireless fidelity (WiFi) module 2370, a processor 2380, and a power supply 2390. Those skilled in the art will appreciate that the terminal structure shown in FIG23 does not limit the terminal to a mobile phone or computer, and may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0339] The RF circuit 2310 may be used for receiving and sending signals during information transmission or calls. In particular, after receiving downlink information from the base station, it is sent to the processor 2380 for processing. In addition, the designed uplink data is sent to the base station.

[0340] The memory 2315 may be used to store software programs and modules. The processor 2380 executes various functional applications and data processing of the terminal by running the software programs and modules stored in the memory 2315 .

[0341] The input unit 2330 may be configured to receive input digital or character information and generate key signal input related to the terminal's settings and function control. Specifically, the input unit 2330 may include a touch panel 2331 and other input devices 2332 .

[0342] The display unit 2340 may be configured to display input information or provided information and various menus of the terminal. The display unit 2340 may include a display panel 2341.

[0343] The audio circuit 2360 , the speaker 2361 , and the microphone 2362 may provide an audio interface.

[0344] In this embodiment, the processor 2380 included in the terminal can execute the quantum hardware resource virtualization method of the previous embodiment.

[0345] The terminals of the embodiments of the present disclosure include but are not limited to mobile phones, computers, intelligent voice interaction devices, smart home appliances, vehicle-mounted terminals, aircraft, etc. The embodiments of the present invention can be applied to various scenarios, including but not limited to quantum computing, distributed quantum computing, superconducting quantum computing, etc.

[0346] Figure 24 is a block diagram of a portion of a server implementing the quantum hardware resource virtualization method according to an embodiment of the present disclosure. Servers can vary significantly depending on their configuration or performance, and may include one or more central processing units (CPUs) 2482 (e.g., one or more processors), memory 2432, and one or more storage media 2430 (e.g., one or more mass storage devices) storing applications 2442 or data 2444. Memory 2432 and storage media 2430 may be either transient or persistent storage. Programs stored in storage media 2430 may include one or more modules (not shown), each of which may include a series of instructions for operating on server 2400. Furthermore, CPU 2422 may be configured to communicate with storage media 2430, executing the series of instructions stored in storage media 2430 on server 2400.

[0347] The server 2400 may also include one or more power supplies 2426, one or more wired or wireless network interfaces 2450, one or more input and output interfaces 2458, and / or one or more operating systems 2441, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0348] The processor in the server 2400 can be used to execute the quantum hardware resource virtualization method according to the embodiment of the present disclosure.

[0349] The embodiments of the present disclosure further provide a computer-readable storage medium for storing program code, and the program code is used to execute the quantum hardware resource virtualization method of each of the aforementioned embodiments.

[0350] The present disclosure also provides a computer program product, which includes a computer program. A processor of a computer device reads and executes the computer program, so that the computer device implements the above-mentioned quantum hardware resource virtualization method.

[0351] The terms "first," "second," "third," "fourth," and the like (if any) in the specification of the present disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present disclosure described herein, for example, can be implemented in orders other than those illustrated or described herein. In addition, the terms "comprises" and "comprising," and any variations thereof, are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0352] It should be understood that in the present disclosure, "at least one (item)" refers to one or more, and "plurality" refers to two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can represent: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0353] It should be understood that in the description of the embodiments of the present disclosure, the meaning of multiple (or multiple items) is more than two, greater than, less than, exceed, etc. are understood to exclude the number itself, and above, below, within, etc. are understood to include the number itself.

[0354] In the several embodiments provided in the present disclosure, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0355] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0356] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0357] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the various embodiments of the present disclosure. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0358] It should also be understood that the various implementations provided in the embodiments of the present disclosure can be combined arbitrarily to achieve different technical effects.

[0359] The above is a specific description of the implementation methods of the present disclosure, but the present disclosure is not limited to the above implementation methods. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present disclosure. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present disclosure.

Claims

1. A method for virtualizing quantum hardware resources, characterized in that: include: Obtaining a target quantum bit unit topology for a target quantum computing task; Obtain the quantum bit unit network in the quantum hardware resource; In the qubit unit network, a plurality of candidate qubit unit subnetworks that conform to the target qubit unit topology are obtained; Determining the overall operation fidelity of each candidate qubit unit subnetwork based on the self-operation fidelity of each qubit unit in each candidate qubit unit subnetwork and the mutual operation fidelity between adjacent qubit units in the plurality of candidate qubit unit subnetworks; Based on the overall operational fidelity of the plurality of candidate quantum bit unit subnetworks, a target quantum bit unit subnetwork is determined, and the target quantum bit unit subnetwork serves as a virtualized resource for the target quantum computing task.

2. The method for virtualizing quantum hardware resources according to claim 1, characterized in that: Determining the overall operation fidelity of the candidate qubit unit subnetwork based on the self-operation fidelity of each qubit unit in the candidate qubit unit subnetwork and the mutual operation fidelity between adjacent qubit units includes: Obtaining a first weight of the self-operation fidelity and a second weight of the mutual operation fidelity; Based on the first weight and the second weight, a weighted sum is performed on the self-operation fidelity and the mutual operation fidelity to obtain the overall operation fidelity.

3. The method for virtualizing quantum hardware resources according to claim 2, characterized in that: The obtaining of the first weight of the self-operation fidelity and the second weight of the mutual operation fidelity includes: Obtaining a task type of the target quantum computing task; Based on the task type, a preset mapping relationship between the task type and the weight is searched to obtain the first weight and the second weight.

4. The method for virtualizing quantum hardware resources according to any one of claims 1 to 3, characterized in that: The target qubit unit topology is a universal qubit unit topology; The step of obtaining a target quantum bit unit topology for a target quantum computing task includes: Acquire a plurality of sample quantum computing tasks, wherein the plurality of sample quantum computing tasks include the target quantum computing task; Acquire a plurality of sample quantum bit unit topologies for a plurality of the sample quantum computing tasks; The union of multiple sample quantum bit unit topologies is taken to obtain the universal quantum bit unit topology.

5. The method for virtualizing quantum hardware resources according to any one of claims 1 to 4, characterized in that: The step of obtaining a quantum bit unit network in a quantum hardware resource includes: Acquire original quantum bit units in a quantum hardware resource, and a first connection relationship between the original quantum bit units; Obtaining occupied quantum bit units; Acquire a second connection relationship between the occupied qubit units and between the occupied qubit unit and an adjacent qubit unit; The occupied quantum bit unit is removed from the original quantum bit unit, and the second connection relationship is removed from the first connection relationship to obtain the quantum bit unit network.

6. The method for virtualizing quantum hardware resources according to any one of claims 1 to 5, characterized in that: The target qubit unit topology includes a first number of qubit units; The step of obtaining, in the quantum bit unit network, a plurality of candidate quantum bit unit sub-networks that conform to the target quantum bit unit topology comprises: Traversing the qubit unit network to obtain a plurality of post-screening qubit unit sub-networks, the post-screening qubit unit sub-networks comprising the first number of connected qubit units; The third connection relationship between the quantum bit units in the target quantum bit unit topology is compared with the fourth connection relationship between the quantum bit units in each of the screened quantum bit unit sub-networks to determine a plurality of candidate quantum bit unit sub-networks from the plurality of screened quantum bit unit sub-networks.

7. The method for virtualizing quantum hardware resources according to any one of claims 1 to 6, characterized in that: The step of determining a target qubit unit subnetwork based on the overall operational fidelity of the plurality of candidate qubit unit subnetworks comprises: Determining the overall failure rate of each of the candidate qubit unit subnetworks based on the failure rate of each of the qubit units in each of the candidate qubit unit subnetworks; A target quantum bit unit sub-network is determined based on the overall operational fidelity and the overall failure rate of a plurality of the candidate quantum bit unit sub-networks.

8. The method for virtualizing quantum hardware resources according to any one of claims 1 to 6, characterized in that: The step of determining a target qubit unit subnetwork based on the overall operational fidelity of the plurality of candidate qubit unit subnetworks comprises: Obtaining the importance of the target quantum computing task; determining a fidelity level corresponding to the importance; The candidate quantum bit unit subnetwork whose overall operation fidelity belongs to the fidelity level among the multiple candidate quantum bit unit subnetworks is determined as the target quantum bit unit subnetwork.

9. The method for virtualizing quantum hardware resources according to any one of claims 1 to 6, characterized in that: The number of the target quantum computing tasks is a second number, and the number of the candidate quantum bit unit subnetworks is a third number, wherein the second number is smaller than the third number; The step of determining a target qubit unit subnetwork based on the overall operational fidelity of the plurality of candidate qubit unit subnetworks comprises: sorting the third number of candidate quantum bit unit sub-networks in descending order according to the overall operation fidelity; Determine the first second number of candidate quantum bit unit subnetworks in the sorting as the second number of target quantum bit unit subnetworks for the second number of target quantum computing tasks Network.

10. The method for virtualizing quantum hardware resources according to any one of claims 1 to 6, characterized in that: The target quantum computing task is a second number of the associated target quantum computing tasks, and the candidate quantum bit unit subnetwork is a third number of the candidate quantum bit unit subnetwork, wherein the second number is less than the third number; The step of determining a target qubit unit subnetwork based on the overall operational fidelity of the plurality of candidate qubit unit subnetworks comprises: Traversing the third number of the candidate quantum bit unit sub-networks to obtain a plurality of candidate sub-network combinations, each of the plurality of candidate sub-network combinations containing the second number of the candidate quantum bit unit sub-networks; Determining the sum of the overall operational fidelity of a second number of the candidate qubit unit sub-networks in each candidate sub-network combination; Determining the concentration of a second number of the candidate quantum bit unit sub-networks in each candidate sub-network combination; Based on the sum of the overall operational fidelity and the concentration of the plurality of candidate sub-network combinations, a target sub-network combination is determined from the plurality of candidate sub-network combinations, the target sub-network combination serving as the second number of target quantum bit unit sub-networks for the second number of target quantum computing tasks.

11. The method for virtualizing quantum hardware resources according to claim 10, characterized in that: Determining the concentration of a second number of the candidate quantum bit unit sub-networks in the candidate sub-network combination includes: Determine the center point distances between two of the second number of candidate quantum bit unit sub-networks in the candidate sub-network combination; The reciprocal of the average distance of the center point distance is determined as the concentration of the second number of the candidate quantum bit unit sub-networks.

12. A quantum hardware resource virtualization device, characterized in that: include: A first acquisition unit, used to acquire a target quantum bit unit topology for a target quantum computing task; A second acquisition unit, used to acquire a quantum bit unit network in a quantum hardware resource; A third acquisition unit is used to acquire, in the quantum bit unit network, a plurality of candidate quantum bit unit sub-networks that conform to the target quantum bit unit topology; A first determining unit, configured to determine the overall operation fidelity of each candidate qubit unit subnetwork based on the self-operation fidelity of each qubit unit in each of the candidate qubit unit subnetworks and the mutual operation fidelity between adjacent qubit units; A second determination unit is used to determine a target quantum bit unit subnetwork based on the overall operational fidelity of the plurality of candidate quantum bit unit subnetworks, wherein the target quantum bit unit subnetwork serves as a virtualized resource for the target quantum computing task.

13. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the quantum hardware resource virtualization method according to any one of claims 1 to 11 is implemented.

14. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the quantum hardware resource virtualization method according to any one of claims 1 to 11 is implemented.

15. A computer program product, comprising a computer program, wherein the computer program is read and executed by a processor of a computer device, so that the computer device executes the quantum hardware resource virtualization method according to any one of claims 1 to 11.

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