Method for preparing optical quantum GKP state and quantum computing device

Abstract

The application relates to a preparation method of an optical quantum GKP state and a quantum computing device. The method comprises the following steps: obtaining a preparation target of an optical quantum GKP state and a hardware constraint condition of a preparation device used for preparing the optical quantum GKP state; calling an intelligent agent, generating line simulation information of a simulation preparation device and a plurality of groups of candidate parameters based on the preparation target and the hardware constraint condition, the simulation preparation device being used for simulating preparation of the optical quantum GKP state, the line simulation information being used for constructing a simulation optical quantum line, and each candidate parameter comprising a post-selection strategy and line parameters of the simulation optical quantum line; determining a current optimal simulation parameter based on a simulation result of the simulation preparation of the optical quantum GKP state under each group of candidate parameters by the simulation optical quantum line based on the simulation preparation device; and controlling the preparation device to perform the preparation of the optical quantum GKP state based on the current optimal simulation parameter. By using the method, the preparation efficiency of an approximate GKP state and a comprehensive quality evaluation index thereof can be improved under the physical hardware constraint of an actual preparation device.

Description

Technical Field

[0001] This application relates to the field of quantum computing technology, and in particular to a method for preparing photonic quantum GKP states, a computer device, a computer-readable storage medium, a computer program product, and a quantum computing device. Background Technology

[0002] With the development of quantum computing technology, optical quantum GKP (Gottesman-Kitaev-Preskill) state preparation technology has emerged. Among them, optical quantum GKP state is a continuous variable qubit encoding scheme designed specifically for quantum error correction. It is a key resource state for realizing continuous variable fault-tolerant quantum computing. It is usually prepared by using a post-selection mechanism that combines Gaussian Boson Sampling (GBS) with photon number-resolved detection to obtain non-Gaussian states, such as approximate GKP states.

[0003] In related technologies, the preparation parameters of photonic GKP states (such as effective compressibility, photonic quantum circuit parameters, post-selection strategies, etc.) are usually theoretically simulated on classical computers. However, due to the complex and difficult-to-model noise in the physical hardware of real preparation equipment, the photonic GKP states prepared by theoretical simulation with extremely high fidelity often suffer significant decay and low quality when run directly on real preparation equipment. On the other hand, if the preparation parameters are tested directly on real preparation equipment, the huge parameter space of the optical system (high-dimensional parameter space) and the long time consumption of quantum state tomography will lead to extremely long test iteration cycles, high test costs, and a high risk of getting trapped in local optima.

[0004] Therefore, how to improve the preparation efficiency of the approximate GKP state and its comprehensive quality evaluation index under the physical hardware constraints of real preparation equipment has become an urgent technical problem to be solved. Summary of the Invention

[0005] Based on this, and in response to the aforementioned technical problems, this application provides a method for preparing photonic quantum GKP states and a quantum computing device, which can improve the preparation efficiency of approximate GKP states and their comprehensive quality evaluation index under the physical hardware constraints of real preparation devices.

[0006] In a first aspect, this application provides a method for preparing a photonic quantum GKP state, comprising:

[0007] The objective of obtaining the photonic quantum GKP state and the hardware constraints of the equipment used to prepare the photonic quantum GKP state;

[0008] The intelligent agent is invoked to generate circuit simulation information and multiple sets of candidate parameters for the simulated fabrication device based on the fabrication goal and hardware constraints. The simulated fabrication device is used to simulate the fabrication of photonic GKP states, and the circuit simulation information is used to construct simulated photonic circuits. Each candidate parameter includes a post-selection strategy and circuit parameters of the simulated photonic circuit.

[0009] Based on the simulation results of the simulated preparation device using simulated photonic quantum circuits to simulate the preparation of photonic GKP states under each set of candidate parameters, the current optimal simulation parameters are determined; and,

[0010] The control and preparation equipment is used to prepare photonic GKP states based on the current optimal simulation parameters.

[0011] In one embodiment, determining the current optimal simulation parameters based on the simulation results of the simulated quantum preparation device simulating the preparation of photonic GKP states under each set of candidate parameters using simulated photonic quantum circuits includes: acquiring the simulation results of the simulated quantum preparation device simulating the preparation of photonic GKP states under each set of candidate parameters using simulated photonic quantum circuits; if the obtained simulation results do not meet preset conditions, calling an agent to correct multiple sets of candidate parameters to obtain new multiple sets of candidate parameters, and based on the new multiple sets of candidate parameters, returning to execute the operation of acquiring the simulation results of the simulated quantum preparation device simulating the preparation of photonic GKP states under each set of candidate parameters using simulated photonic quantum circuits; if the obtained simulation results meet preset conditions, selecting the optimal result among all currently determined simulation results, and determining the simulation parameters corresponding to the optimal result as the current optimal simulation parameters.

[0012] In one embodiment, the simulation results include effective compression, and the preset conditions include any of the following: each simulation result obtained includes a target result, the effective compression in the target result is greater than or equal to a preset compression threshold; the current round of each simulation result obtained reaches a preset round; the difference between the maximum values ​​of the effective compression determined in two consecutive rounds is less than a preset threshold.

[0013] In one embodiment, the optimal result among all currently determined simulation results is selected, including: selecting the simulation result with the highest quality evaluation index among all currently determined simulation results to obtain the optimal result; wherein the quality evaluation index is determined based on at least one of effective compression, fidelity, Wigner function distribution similarity and preparation success rate.

[0014] In one embodiment, controlling the fabrication device to prepare photonic quantum GKP states based on the current optimal simulation parameters includes: converting the current optimal simulation parameters into control signals for the fabrication device; and controlling the fabrication device to prepare photonic quantum GKP states based on the control signals.

[0015] In one embodiment, the method further includes: acquiring the preparation result of the photonic quantum GKP state preparation by the preparation device; determining the result error between the preparation result and the simulation result corresponding to the current optimal simulation parameters; if the result error is greater than a preset error, calling an agent to determine the source of the result error; updating the control signal based on the source of the error, and controlling the preparation device to prepare the photonic quantum GKP state based on the updated control signal.

[0016] In one embodiment, updating the control signal based on the source of error includes: determining a parameter compensation value corresponding to the real environmental noise when the source of error includes the real environmental noise of the fabrication equipment; and compensating the control signal based on the parameter compensation value to obtain an updated control signal.

[0017] In one embodiment, updating the control signal based on the source of error includes: optimizing the current Fock state truncation condition when the source of error includes the current photon count Fock state truncation condition, to obtain an optimized current Fock state truncation condition; correcting the simulation preparation device based on the optimized current Fock state truncation condition; and returning to the execution calling agent based on the corrected simulation preparation device to generate circuit simulation information and multiple sets of candidate parameters for the simulation preparation device based on the preparation target and hardware constraints.

[0018] In one embodiment, if the error source also includes the actual environmental noise of the fabrication equipment, the method further includes: determining the parameter compensation value corresponding to the actual environmental noise;

[0019] The control preparation equipment prepares photonic GKP states based on control signals, and also includes: compensating the control signals based on parameter compensation values; and the control preparation equipment prepares photonic GKP states based on the compensated control signals.

[0020] In one embodiment, an intelligent agent is invoked to generate circuit simulation information and multiple sets of candidate parameters for a simulated fabrication device based on the fabrication goal and hardware constraints. This includes: inputting the fabrication goal and hardware constraints into the intelligent agent to obtain circuit simulation information for the simulated fabrication device; and inputting the fabrication goal, hardware constraints, and circuit simulation information into the intelligent agent to obtain multiple sets of candidate parameters for the simulated fabrication device.

[0021] Secondly, this application also provides a device for preparing photonic quantum GKP states, comprising:

[0022] The first acquisition module is used to acquire the preparation target of the photonic quantum GKP state and the hardware constraints of the preparation equipment for preparing the photonic quantum GKP state.

[0023] The generation module is used to call the intelligent agent to generate circuit simulation information and multiple sets of candidate parameters for the simulated preparation device based on the preparation target and hardware constraints. The simulated preparation device is used to simulate the preparation of photonic GKP states, and the circuit simulation information is used to construct simulated photonic circuits. Each candidate parameter includes a post-selection strategy and the circuit parameters of the simulated photonic circuit.

[0024] The first determining module is used to determine the current optimal simulation parameters based on the simulation results of the simulated preparation device using simulated photonic quantum circuits to simulate the preparation of photonic GKP states under each set of candidate parameters.

[0025] The control module is used to control the preparation equipment to prepare photonic quantum GKP states based on the current optimal simulation parameters.

[0026] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the methods provided in the first aspect.

[0027] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the methods provided in the first aspect.

[0028] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the first aspect.

[0029] In a sixth aspect, this application also provides a quantum computing device, including a classical computing unit and a quantum computing unit, wherein the quantum computing unit is used to implement the methods provided in the first aspect.

[0030] The aforementioned method and quantum computing device for preparing photonic GKP states, by acquiring the preparation target of the photonic GKP state and the hardware constraints of the preparation device, can invoke an intelligent agent to generate circuit simulation information and multiple sets of candidate parameters for the simulated preparation device based on the preparation target and hardware constraints. This simulated photonic circuit is then constructed within the simulated preparation device using the simulated circuit information to obtain the preparation results of the photonic GKP state under each set of candidate parameters. Based on these preparation results, the current optimal simulation parameters are determined, and the preparation device then prepares the photonic GKP state based on these optimal simulation parameters. In this way, on the one hand, by invoking an intelligent agent, the efficiency of classical computer simulation can be combined with the objectivity of the hardware constraints of the real preparation device, achieving closed-loop optimization of the software and hardware systems. On the other hand, the intelligent agent uses the simulated preparation device to conduct preliminary exploration of preparation parameters, quickly narrowing down the parameter space, and utilizes the agent's strong logical reasoning ability to analyze the current optimal simulation parameters, thereby improving the preparation efficiency of the approximate GKP state and its overall quality evaluation index under the physical hardware constraints of the real preparation device. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 The application environment diagram shows the preparation method of photonic quantum GKP state provided in some embodiments of this application;

[0033] Figure 2 A schematic flowchart illustrating the preparation method of photonic quantum GKP states provided in some embodiments of this application;

[0034] Figure 3 A flowchart illustrating the process of determining the current optimal simulation parameters provided in some embodiments of this application;

[0035] Figure 4 A schematic diagram illustrating the process of preparing photonic quantum GKP states using a controlled preparation apparatus provided in some embodiments of this application;

[0036] Figure 5 A schematic flowchart illustrating the preparation method of photonic quantum GKP states provided in other embodiments of this application;

[0037] Figure 6 A flowchart illustrating the update control signals provided in some embodiments of this application;

[0038] Figure 7 A flowchart illustrating the update control signals provided in other embodiments of this application;

[0039] Figure 8 A schematic flowchart illustrating the method for preparing photonic GKP states provided in some embodiments of this application;

[0040] Figure 9 This is a structural block diagram of the apparatus for preparing photonic quantum GKP states provided in some embodiments of this application;

[0041] Figure 10 This is an internal structural diagram of a computer device provided in some embodiments of this application. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below.

[0044] In this document, unless explicitly stated otherwise, performing a step in response to A does not mean that the step is performed immediately after A, but may include one or more intermediate steps.

[0045] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0046] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0047] In related technologies, the preparation parameters of photonic GKP states (such as effective compressibility, photonic quantum circuit parameters, post-selection strategies, etc.) are usually theoretically simulated on classical computers. However, due to the complex and difficult-to-model noise in the physical hardware of real preparation equipment, the photonic GKP states prepared by theoretical simulation with extremely high fidelity often suffer significant decay and low quality when run directly on real preparation equipment. On the other hand, if the preparation parameters are tested directly on real preparation equipment, the huge parameter space of the optical system (high-dimensional parameter space) and the long time consumption of quantum state tomography will lead to extremely long test iteration cycles, high test costs, and a high risk of getting trapped in local optima.

[0048] Therefore, how to improve the preparation efficiency of the approximate GKP state and its comprehensive quality evaluation index under the physical hardware constraints of real preparation equipment has become an urgent technical problem to be solved.

[0049] Based on this, in order to solve the above-mentioned technical problems, an exemplary embodiment provides a method for preparing photonic quantum GKP states. This method can be applied to computer devices, which can be terminals or servers. Terminals can be, but are not limited to, various personal computers, laptops, smartphones, tablets, etc. Servers can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers providing cloud computing services.

[0050] Based on the above embodiments, in one exemplary embodiment, the method for preparing photonic GKP states provided in this application can be applied to, for example... Figure 1 In the application environment shown, the preparation device 102 communicates with the server 104 via a network. The data storage system can store the data that the server 104 needs to process, such as the preparation target of the photonic GKP state and the hardware constraints of the preparation equipment used to prepare the photonic GKP state. The data storage system can be integrated on the server 104 or placed in the cloud or on another network server. The server 104 is equipped with an intelligent agent 1041, a simulation preparation device 1042, and a control and verification module 1043 to execute the photonic GKP state preparation method provided in this application embodiment.

[0051] Based on the above embodiments, in an exemplary embodiment, such as Figure 2 As shown, a method for preparing photonic GKP states is provided, which can be applied to... Figure 1 Taking the server in the example, the following steps are included:

[0052] S201, Obtain the preparation target of the photonic quantum GKP state and the hardware constraints of the preparation equipment used to prepare the photonic quantum GKP state.

[0053] In this embodiment, the preparation goal of the photonic quantum GKP state is a pre-set objective that the photonic quantum GKP state to be prepared can satisfy, such as the target logic state, the required effective compression threshold, etc.; the hardware constraints are the objective constraints of the preparation equipment used to prepare the photonic quantum GKP state, such as the maximum effective compression of the preparation equipment, the maximum number of selected photons of the post-selection mechanism of the preparation equipment, etc.

[0054] In this embodiment, the fabrication device is actual optical quantum computing hardware. Optionally, the fabrication device includes a host computer and a hardware structure, wherein the host computer is used to communicate with other devices and control the hardware structure to fabricate optical quantum GKP states. Optionally, the hardware structure in the fabrication device may include a tunable compressed light source, a programmable linear optical interferometer network, a photon number-resolved detector (for post-selection), and a zero-difference detector (for quantum state tomography).

[0055] To obtain optimal preparation parameters to improve the preparation efficiency and overall quality evaluation index of approximate GKP states under the physical hardware constraints of actual preparation equipment, the determination of these optimal preparation parameters will be influenced by the preparation objectives and hardware constraints. Therefore, it is necessary to first obtain these preparation objectives and hardware constraints. The preparation objectives can be set based on the preparation scenario and requirements of the photonic GKP states, while the hardware constraints can be set based on the actual hardware parameters of the preparation equipment. No specific limitations are imposed on either.

[0056] Optionally, the fabrication objective and hardware constraints input by the user can be obtained. Alternatively, the fabrication objective input by the user and the device identifier of the fabrication device input by the user can be obtained, and the hardware constraints corresponding to the user-input device identifier can be queried from the pre-stored hardware constraints corresponding to each device identifier. Therefore, the method of obtaining the fabrication objective and hardware constraints is not limited.

[0057] S202 invokes the intelligent agent to generate circuit simulation information and multiple sets of candidate parameters for the simulation fabrication device based on the fabrication goal and hardware constraints.

[0058] In this embodiment, the simulation preparation device is used to simulate the preparation of photonic GKP states, and the circuit simulation information is used to construct simulated photonic circuits. Each candidate parameter includes a post-selection strategy and the circuit parameters of the simulated photonic circuit.

[0059] An intelligent agent is a smart entity possessing the capabilities of environmental perception, autonomous decision-making, task execution, and feedback iteration. Optionally, the intelligent agent can be an AI (Artificial Intelligence) agent, built upon a large language model, possessing capabilities such as long text memory, a physical knowledge library, tool invocation, multimodal data processing, and reasoning. It is responsible for coordinating the data from the simulation preparation device and executing a "perception-reasoning-action" cycle. In this embodiment, the aforementioned intelligent agent can be located in the computer device used to execute the preparation method of the photonic quantum GKP state, or it can be located in other computer devices, allowing communication between the two computer devices to invoke the intelligent agent.

[0060] The simulated preparation device is a virtual module installed in a computer device. Based on input simulation preparation parameters, it can simulate the preparation of photonic quantum GKP states using a real preparation device and output the corresponding simulation results. Optionally, the simulation results include various theoretical indices of the non-Gaussian state, such as effective compressibility, energy-constrained diamond norm, stabilizer constraint conditions, phase-space marginal function of final state, and Wigner function distribution map. Optionally, the simulated preparation device includes a Gaussian boson sampling evolution simulator and a numerical simulator for a post-selection mechanism. In this embodiment, the simulated preparation device can be installed in the computer device used to execute the photonic quantum GKP state preparation method, or it can be installed in another computer device, allowing communication between the two computer devices to invoke the simulated preparation device.

[0061] In this embodiment, after obtaining the aforementioned preparation target and hardware constraints, an intelligent agent can be invoked to initialize the initial parameter space of the simulated preparation device. This allows for the simulation of building a simulated quantum circuit with a network configuration identical to the real quantum circuit in the preparation device, enabling the simulated preparation device to approximate the real preparation device as closely as possible. This allows the simulated preparation device to simulate the preparation device's GKP state preparation. The result of initializing the initial parameter space of the simulated preparation device is the circuit simulation information used to construct the simulated quantum circuit in the simulated preparation device. Optionally, the network configuration of the simulated quantum circuit is the same as the network configuration of the real quantum circuit in the preparation device, or the similarity is greater than a similarity threshold. This similarity threshold can be set based on empirical values, etc., and is not specifically limited. Optionally, the circuit simulation information includes the initial compression degree under each preparation mode in the simulated preparation device, the simulated network configuration of the simulated interferometer in the simulated preparation device, and the simulated noise model of the simulated preparation device.

[0062] After obtaining the aforementioned circuit simulation information, the simulated photonic quantum circuit in the simulation preparation device can be determined based on this information. Then, the intelligent agent can be invoked to determine multiple sets of candidate parameters for the simulation preparation device based on the preparation objective, hardware constraints, and circuit simulation information. Each set of candidate parameters is a set of preparation parameters that may be applied to the preparation device to control the preparation of photonic GKP states. These parameters include the post-selection strategy and the circuit parameters of the simulated photonic quantum circuit. Optionally, when the simulation preparation device includes a Gaussian boson sampling simulator and a numerical simulator with a post-selection mechanism, each set of candidate parameters includes GBS parameters and a post-selection strategy.

[0063] Optionally, an intelligent agent is invoked to generate circuit simulation information and multiple sets of candidate parameters for simulating the fabrication device, based on prior knowledge of the fabrication equipment for photonic quantum GKP fabrication, as well as the fabrication goals and hardware constraints. The aforementioned prior knowledge may include relevant literature in a local library, historical fabrication parameters and corresponding fabrication results of the fabrication equipment, and the hardware parameters of the fabrication equipment, etc., without specific limitations.

[0064] In an optional embodiment, S202 above includes: inputting the preparation target and hardware constraints into the intelligent agent to obtain circuit simulation information of the simulated preparation device; inputting the preparation target, hardware constraints and circuit simulation information into the intelligent agent to obtain multiple sets of candidate parameters of the simulated preparation device.

[0065] In this embodiment, the preparation target and hardware constraints can be input into the agent first, so that the agent can determine the circuit simulation information of the simulation preparation device based on the prior knowledge and the preparation target and hardware constraints. Then, the preparation target, hardware constraints and circuit simulation information can be input into the agent again, so that the agent can determine multiple sets of candidate parameters of the simulation preparation device based on the prior knowledge and the preparation target, hardware constraints and circuit simulation information.

[0066] S203, Based on the simulation results of the simulated preparation device using simulated photonic quantum circuits to simulate the preparation of photonic GKP states under each set of candidate parameters, determine the current optimal simulation parameters.

[0067] As mentioned earlier, after obtaining the aforementioned circuit simulation information, a simulated quantum circuit with a network configuration identical to the real quantum circuit in the fabrication device is first constructed using this information. Then, for each set of candidate parameters, the circuit parameters in that set are assigned to the constructed simulated quantum circuit, and the post-selection strategy in that set is assigned to the numerical simulator of the post-selection mechanism in the simulation fabrication device. Thus, the simulated fabrication device with the assigned values ​​is used to simulate the fabrication of quantum GKP states, obtaining the simulation results corresponding to that set of candidate parameters output by the simulated fabrication device. These results serve as the simulation results of the simulated fabrication device simulating the fabrication of quantum GKP states under those candidate parameters using the simulated quantum circuit. For example, the simulated fabrication device uses the simulated quantum circuit to perform efficient parameter space exploration under those candidate parameters, calculating theoretical indices in the truncated Fock (photon number) space, and feeding back sensory data such as effective compression, energy-constrained rhombus norm, and Wigner function distribution as simulation results.

[0068] Based on this, simulation results corresponding to each group of candidate parameters can be obtained, and the current optimal simulation parameters can be determined based on these results. The current optimal simulation parameters refer to those simulation parameters that, among all currently determined simulation parameters, can produce the optimal photonic GKP state for the current stage. For example, simulation parameters that can produce the photonic GKP state with the highest effective compression, the highest fidelity, or the most standard grid shape in the Wigner function distribution diagram, etc., are not specifically limited in this regard.

[0069] Optionally, from multiple sets of candidate parameters, the candidate parameter with the highest effective compression ratio in the corresponding simulation results is selected as the current optimal simulation parameter. The above embodiments are merely illustrative examples of specific implementation methods for determining the current optimal simulation parameter, and are not intended to limit the scope of this application. Any specific implementation method that can determine the current optimal simulation parameter based on the simulation results corresponding to the above sets of candidate parameters falls within the protection scope of this application.

[0070] Optionally, multiple execution threads of the simulation preparation device can be set, and different candidate parameters can be assigned to each thread, so that multiple execution threads can synchronously perform photonic GKP state simulation preparation through the simulated photonic quantum circuit under the assigned candidate parameters, thereby improving the efficiency of obtaining simulation results corresponding to each set of candidate parameters.

[0071] S204, the control equipment is used to prepare photonic quantum GKP states based on the current optimal simulation parameters.

[0072] After obtaining the above-mentioned current optimal simulation parameters, the preparation equipment can be controlled to prepare photonic quantum GKP states based on the current optimal simulation parameters, thereby improving the preparation efficiency of the preparation equipment in preparing approximate GKP states, the fidelity relative to the target GKP state, and the preparation success rate.

[0073] Optionally, the aforementioned current optimal simulation parameters are sent to the host computer of the fabrication device, so that the host computer generates a corresponding control command based on the aforementioned current optimal simulation parameters and issues the control command to the fabrication device. The fabrication device then responds to the control command to prepare the photonic quantum GKP state.

[0074] The aforementioned method for preparing photonic GKP states, by acquiring the preparation target of the photonic GKP state and the hardware constraints of the preparation equipment, can invoke an intelligent agent to generate circuit simulation information and multiple sets of candidate parameters for a simulated preparation device based on the preparation target and hardware constraints. This simulated photonic circuit is then constructed within the simulated preparation device using the circuit simulation information. The preparation results of the simulated preparation device under each set of candidate parameters are obtained, and the current optimal simulation parameters are determined based on these results. The preparation equipment then prepares the photonic GKP state based on these optimal simulation parameters. In this way, on the one hand, by invoking an intelligent agent, the efficiency of classical computer simulation can be combined with the objectivity of the hardware constraints of the actual preparation equipment, achieving closed-loop optimization of the software and hardware systems. On the other hand, the intelligent agent uses the simulated preparation device to conduct preliminary exploration of preparation parameters, quickly narrowing down the parameter space, and utilizes the agent's strong logical reasoning ability to analyze the current optimal simulation parameters, thereby improving the preparation efficiency and overall quality evaluation index of the approximate GKP state under the physical hardware constraints of the actual preparation equipment.

[0075] Based on the above embodiments, in an exemplary embodiment, the determination of the current optimal simulation parameters in S203 is further refined, such as... Figure 3 As shown, it includes the following steps:

[0076] S301, Obtain the simulation results of the simulated preparation device preparing photonic GKP states under each set of candidate parameters by simulating photonic quantum circuits.

[0077] After obtaining the above-mentioned circuit simulation information and multiple sets of candidate parameters for the first time, a simulated photonic quantum circuit is first constructed in the simulation preparation device based on the above-mentioned circuit simulation information. Then, the simulation preparation device is controlled to simulate the preparation of photonic GKP states under each set of candidate parameters through the simulated photonic quantum circuit, so as to obtain the simulation results corresponding to each set of candidate parameters.

[0078] Subsequently, after obtaining multiple new sets of candidate parameters, while keeping the constructed simulated photonic quantum circuit unchanged, the simulation preparation device is controlled to simulate the preparation of photonic GKP states under each new set of candidate parameters through the simulated photonic quantum circuit, so as to obtain the simulation results corresponding to each new set of candidate parameters.

[0079] S302, if the obtained simulation results do not meet the preset conditions, call the agent to correct the multiple sets of candidate parameters, obtain new multiple sets of candidate parameters, and return to S301 based on the new multiple sets of candidate parameters.

[0080] After obtaining simulation results corresponding to multiple sets of candidate parameters each time, it is first determined whether each simulation result obtained this time meets the preset conditions. If the preset conditions are not met, the agent is invoked to correct the current multiple sets of candidate parameters to obtain new multiple sets of candidate parameters. Optionally, the agent is invoked to use optimization algorithms such as genetic algorithms or context learning mechanisms to correct the current multiple sets of candidate parameters.

[0081] In an optional embodiment, the simulation results include effective compression, and correspondingly, the preset conditions include any of the following:

[0082] 1) The simulation results obtained include the target result, and the effective compression ratio in the target result is greater than or equal to a preset compression ratio threshold. In this embodiment, a preset compression ratio threshold can be preset. Then, after each simulation result is obtained, for each simulation result, the effective compression ratio in the simulation result is compared with the preset compression ratio threshold. If the effective compression ratio in the simulation result is greater than or equal to the preset compression ratio threshold, the simulation result is determined to be the target result. Thus, if the target result is included in the multiple sets of simulation results obtained in this instance, it is determined that each simulation result obtained in this instance satisfies the preset condition; if the target result is not included in the multiple sets of simulation results obtained in this instance, it is determined that each simulation result obtained in this instance does not satisfy the preset condition.

[0083] 2) The current round of each simulation result obtained reaches the preset round. In this embodiment, a preset round can be set in advance, and then after each simulation result is obtained, the current round of each simulation result is determined. If the current round reaches the preset round, the simulation result obtained this time is determined to meet the preset condition; if the current round is less than the preset round, the simulation result obtained this time is determined not to meet the preset condition.

[0084] 3) The difference between the maximum effective compression ratios determined in two consecutive rounds is less than a preset threshold. In this embodiment, a preset threshold can be pre-set. After each simulation result is obtained, the maximum effective compression ratio included in the current simulation result is determined. Then, the difference between the maximum effective compression ratio included in the previous simulation result and the maximum effective compression ratio determined in the current simulation is determined. This difference is compared with the preset threshold. If the difference is less than the preset threshold, the current simulation result is determined to meet the preset condition; otherwise, if the difference is greater than or equal to the preset threshold, the current simulation result is determined not to meet the preset condition. Specifically, for the first determined simulation result, the maximum effective compression ratio included in the previous simulation result is set to a preset value, such as 0.

[0085] In this embodiment, the preset compression threshold, preset number of rounds, and preset threshold can all be set based on empirical values, hardware parameters of the preparation equipment, preparation scenario of photonic GKP state, and preparation requirements, etc., and no specific limitations are imposed on them.

[0086] S303, if the obtained simulation results meet the preset conditions, select the optimal result among all the currently determined simulation results, and determine the simulation parameters corresponding to the optimal result as the current optimal simulation parameters.

[0087] If the simulation results obtained in this study meet the preset conditions, the optimal result among all the simulation results obtained in this study is first determined, and then the simulation parameters corresponding to the optimal result (the simulation parameters used when obtaining the optimal result) are determined as the current optimal simulation parameters.

[0088] Optionally, if the simulation results determined in the first iteration meet the preset conditions, the optimal result is selected from the simulation results determined in the first iteration; or, if the simulation results determined in the Nth iteration (N is an integer greater than 1) meet the preset conditions, the optimal result is selected from all the simulation results determined in the 1st to Nth iterations.

[0089] In an optional embodiment, S303 above includes selecting the simulation result with the highest quality evaluation index among all currently determined simulation results to obtain the optimal result; wherein the quality evaluation index is determined based on at least one of effective compression, fidelity, Wigner function distribution similarity, and preparation success rate.

[0090] In this embodiment, for the determined simulation results, a quality evaluation index can be determined based on evaluation metrics such as effective compressibility, fidelity, Wigner function distribution similarity, and fabrication success rate. This allows the simulation result with the highest quality evaluation index to be selected from all currently determined simulation results. Optionally, any one of the evaluation metrics—effective compressibility, fidelity, Wigner function distribution similarity, and fabrication success rate—can be used as a quality evaluation index; alternatively, weights can be assigned to at least two of the evaluation metrics, and a weighted sum can be calculated based on these weights to obtain the quality evaluation index. Of course, the above quality evaluation index can also be determined based on other evaluation metrics, such as the energy-constrained rhombus norm.

[0091] Optionally, the simulation result with the maximum effective squeezing among all currently determined simulation results is selected to obtain the optimal result. In this embodiment, squeezing (unit dB (Decibel)) refers to the degree to which a certain orthogonal component (position q or momentum p) of a single-mode optical field is compressed. The higher the dB number, the stronger the compression and the lower the noise. An ideal photonic GKP state requires extremely high compression. Furthermore, effective squeezing (unit dB) is used to measure how close the actual photonic GKP state is to the ideal photonic GKP lattice state. The higher the effective squeezing, the narrower the corresponding "lattice peak width," and the closer the obtained actual photonic GKP state is to the ideal photonic GKP lattice state. Therefore, the obtained actual photonic GKP state has higher fidelity, stronger resistance to displacement noise, and stronger error correction capability. In other words, the obtained actual photonic GKP state has higher quality and stronger fault tolerance. Therefore, from all the simulation results that have been determined so far, the simulation result with the highest effective compression can be selected as the optimal result. It can be considered that controlling the preparation equipment to prepare photonic GKP states based on the simulation parameters used when obtaining the optimal result can yield the highest quality photonic GKP states. Thus, the simulation parameters used when obtaining the optimal result can be determined as the current optimal simulation parameters.

[0092] In this embodiment, the agent uses a "perception-reasoning-action" mechanism to rapidly and repeatedly correct the obtained simulation parameters in order to find the optimal balance between high fidelity and high fabrication success rate in the parameter space (such as multimode interferometer phase and non-Gaussian post-selection conditions). Thus, it can efficiently fabricate approximate GKP states that meet the preset quality evaluation requirements under the physical hardware constraints of real fabrication equipment.

[0093] Based on the above embodiments, in an optional embodiment, the control of the preparation device for photonic GKP state preparation in S204 is further refined, such as... Figure 4 As shown, it includes the following steps:

[0094] S401 converts the current optimal simulation parameters into control signals for the fabrication equipment.

[0095] S402, the control preparation equipment prepares photonic quantum GKP states based on control signals.

[0096] In this embodiment, after determining the current optimal simulation parameters, the current optimal simulation parameters can be converted into control signals for the fabrication equipment, such as control voltage, so as to control the fabrication equipment to prepare photonic quantum GKP states based on the control signals.

[0097] Optionally, an agent can be invoked to map the current optimal simulation parameters to physical control signals for the fabrication equipment.

[0098] Optionally, the above control signal is sent to the host computer of the preparation device so that the host computer drives the preparation device to perform the actual GBS evolution and post-selection operation based on the above control signal to obtain the prepared photonic quantum GKP state.

[0099] In this embodiment, by converting the current optimal simulation parameters into control signals for the fabrication equipment, the fabrication equipment can perform photonic quantum GKP state preparation under the current optimal simulation parameters, thereby improving the preparation efficiency of approximate GKP states and its comprehensive quality evaluation index under the physical hardware constraints of the actual fabrication equipment.

[0100] Based on the above embodiments, in an exemplary embodiment, such as Figure 5 As shown, the preparation method of this photonic GKP state includes the following steps:

[0101] S501, Obtain the preparation target of the photonic quantum GKP state and the hardware constraints of the preparation equipment used to prepare the photonic quantum GKP state.

[0102] S502 invokes the intelligent agent to generate circuit simulation information and multiple sets of candidate parameters for the simulation fabrication device based on the fabrication goals and hardware constraints.

[0103] S503, based on the simulation results of the simulated photonic quantum GKP state preparation by the simulated photonic quantum circuit under each set of candidate parameters using the simulation preparation device, determine the current optimal simulation parameters.

[0104] The specific implementation methods of S501-S503 are the same as those of S201-S203, and will not be repeated here.

[0105] S504 converts the current optimal simulation parameters into control signals for the fabrication equipment.

[0106] S505, the control preparation equipment, prepares photonic quantum GKP states based on control signals.

[0107] The specific implementation methods of S504-S505 are the same as those of S401-S402, and will not be repeated here.

[0108] S506, Obtain the preparation results of the photonic quantum GKP state preparation using the preparation equipment.

[0109] After the photonic quantum GKP state is prepared by the fabrication device, the fabrication result is obtained. Optionally, quantum state tomography is performed on the actual photonic quantum GKP state prepared by the fabrication device using a zero-difference detector to obtain the Wigner function distribution map of the actual photonic quantum GKP state. Based on the Wigner function distribution map, the effective compression degree of the actual photonic quantum GKP state is determined as the fabrication result.

[0110] S507, determine the result error between the preparation result and the simulation result corresponding to the current optimal simulation parameters.

[0111] As mentioned earlier, for the current optimal simulation parameters, the simulation preparation device has already simulated the preparation of photonic GKP states under these parameters using a simulated photonic quantum circuit, and obtained the corresponding simulation results. Ideally, the preparation results obtained by the preparation device under the aforementioned current optimal simulation parameters should be the same as or very close to the simulation results corresponding to the current optimal simulation parameters. However, in reality, due to the possibility of complex and difficult-to-model noise in the preparation device, and / or the influence of the current Fock state cutoff condition of the simulation preparation device, there may be a large error between the above preparation results and the simulation results corresponding to the current optimal simulation parameters. This results in an unsatisfactory preparation result, requiring the preparation device to be re-controlled for photonic GKP state preparation.

[0112] Based on this, after obtaining the above preparation results, the preparation results can be compared with the simulation results corresponding to the current optimal simulation parameters to determine the result error between the two. Optionally, the preparation results include the actual effective compressibility, while the simulation results corresponding to the current optimal simulation parameters include the simulated effective compressibility. Therefore, the difference between the actual effective compressibility and the simulated effective compressibility can be determined as the result error.

[0113] Then, the result error is compared with the preset error to determine whether it is necessary to re-control the preparation equipment to prepare the photonic GKP state. Optionally, if the result error is less than or equal to the preset error, it indicates that the error between the preparation result and the simulation result corresponding to the current optimal simulation parameters is within an acceptable range, and the quality of the photonic GKP state prepared by the preparation equipment is relatively ideal. Therefore, the photonic GKP state prepared by the preparation equipment can be directly used as the final photonic GKP state for subsequent operations, such as quantum error correction.

[0114] In this embodiment, the aforementioned preset error can be set based on empirical values, hardware parameters of the preparation equipment, preparation scenario of the photonic GKP state, and preparation requirements, etc., and no specific limitations are imposed on it.

[0115] S508: If the result error is greater than the preset error, call the agent to determine the source of the result error.

[0116] In this embodiment, if the error in the above-mentioned result is greater than the preset error, it indicates that the error between the prepared result and the simulation result corresponding to the current optimal simulation parameters is large, and the quality of the photonic GKP state prepared by the preparation equipment is poor. It is necessary to re-control the preparation equipment to prepare the photonic GKP state in order to improve the quality of the prepared photonic GKP state. Since the above-mentioned result error is greater than the preset error due to different reasons, different processing methods are required to re-control the preparation equipment to prepare the photonic GKP state. Therefore, the intelligent agent can be invoked first to determine the source of the above-mentioned result error.

[0117] Optionally, the agent, based on at least one of the preparation results, the simulation results corresponding to the current optimal simulation parameters, the preparation objectives, and the hardware constraints mentioned above, combined with prior knowledge, infers the source of error and outputs the source of error of the inferred results.

[0118] S509 updates the control signal based on the source of error and controls the preparation equipment to prepare photonic GKP states based on the updated control signal.

[0119] Once the aforementioned error sources are identified, the corresponding processing method can be adopted to update the control signal. Optionally, the control signal can be updated directly based on the error source; alternatively, since the control signal is derived from the current optimal simulation parameters, the current optimal simulation parameters can be updated indirectly based on the error source.

[0120] After receiving the updated control signal, the fabrication equipment can be controlled to prepare the photonic GKP state based on the updated control signal, so as to obtain the re-prepared photonic GKP state. Optionally, after obtaining the re-prepared photonic GKP state, the above S506 can be executed again, and the process can be repeated until the result error between the fabrication result and the simulation result corresponding to the current optimal simulation parameters is less than or equal to the preset error, so that the photonic GKP state obtained by the fabrication equipment in the last preparation is taken as the final photonic GKP state.

[0121] In this embodiment, by comparing the result error between the preparation result and the simulation result corresponding to the current optimal simulation parameters, and when the result error is greater than the preset error, the control signal is updated based on the source of the result error, so that the preparation equipment can be controlled to re-prepare the photonic GKP state based on the updated control signal, thereby improving the quality of the final photonic GKP state.

[0122] Based on the above embodiments, in an exemplary embodiment, the update of the control signal in S509 is further refined; optionally, as... Figure 6 As shown, it includes the following steps:

[0123] S601, when the error source includes the real environmental noise of the preparation equipment, determine the parameter compensation value corresponding to the real environmental noise.

[0124] S602 compensates the control signal based on the parameter compensation value to obtain an updated control signal.

[0125] In this embodiment, when the error source includes the real environmental noise of the preparation equipment, it can be determined that the above result error is greater than the preset error because of the complex and difficult-to-model noise of the preparation equipment. Therefore, it can be determined that the accuracy of the current optimal simulation parameters is high, and the influence of the real environmental noise can be eliminated by correcting the control signal.

[0126] Based on this, the parameter compensation value corresponding to the aforementioned real environmental noise is first determined. This parameter compensation value can offset the influence of the aforementioned real environmental noise on the preparation of photonic GKP states. Optionally, an intelligent agent is invoked to determine the parameter compensation value corresponding to the aforementioned real environmental noise in conjunction with prior knowledge. Then, the control signal can be compensated based on the aforementioned parameter compensation value to obtain an updated control signal. Optionally, the sum or difference between the parameter compensation value and the control signal is determined as the updated control signal.

[0127] Optionally, the real-world noise of the fabrication equipment includes the implicit noise channel characteristics of the fabrication equipment, such as the additional losses and / or phase jitter of the real optical quantum circuit.

[0128] In this embodiment, when the error source includes the real environmental noise of the preparation equipment, the influence of the real environmental noise on the preparation of the photonic GKP state can be offset by the parameter compensation value corresponding to the real environmental noise, thereby improving the accuracy of the updated control signal and thus improving the quality of the final photonic GKP state.

[0129] Based on the above embodiments, in another exemplary embodiment, the update of the control signal in S509 is further refined; optionally, as... Figure 7 As shown, it includes the following steps:

[0130] S701, when the error source includes the current Fock state truncation condition, optimize the current Fock state truncation condition to obtain the optimized current Fock state truncation condition.

[0131] S702 corrects the simulation preparation device based on the optimized current Fock state truncation condition, and returns to the execution calling agent based on the corrected simulation preparation device. Based on the preparation target and hardware constraints, it generates the circuit simulation information and multiple sets of candidate parameters for the simulation preparation device.

[0132] In this embodiment, the so-called current Fock state truncation condition refers to the truncation of the Fock state basis vectors in the settings parameters of the simulation preparation device, that is, the preset maximum value of the number and value of photons corresponding to the Fock state basis vectors. Theoretically, the number of photons in a Fock state basis vector can be infinite, for example, a single-photon Fock state is... , It can take values ​​up to infinity, but in general, a cutoff value is chosen. For example, if L=3, then k=0, 1, 2.

[0133] Based on this, if the error source includes the current Fock state cutoff condition, it can be determined that the error result is greater than the preset error because the setting parameters of the simulation preparation device are inappropriate. Therefore, it can be determined that the accuracy of the current optimal simulation parameters is low. Thus, the current Fock state cutoff condition of the simulation preparation device can be optimized to redetermine the current optimal simulation parameters and eliminate the influence of the current Fock state cutoff condition.

[0134] Based on this, the current Fock state truncation condition is first optimized to obtain the optimized current Fock state truncation condition. Optionally, an agent is invoked to optimize the current Fock state truncation condition based on prior knowledge. Then, the simulation preparation device can be modified based on the optimized current Fock state truncation condition to change its simulation accuracy and other settings. Since the settings of the simulation preparation device are modified, the circuit simulation information and multiple candidate parameters generated based on the above preparation goals and hardware constraints will also change. Therefore, the process of invoking the agent to generate the circuit simulation information and multiple candidate parameters based on the preparation goals and hardware constraints can be repeated to obtain new circuit simulation information and multiple candidate parameters, thus obtaining new current optimal simulation parameters. These new current optimal simulation parameters can then be converted into control signals for the preparation device, controlling the device to perform photonic quantum GKP state preparation based on these control signals.

[0135] In an optional embodiment, if the above-mentioned error sources also include the actual environmental noise of the fabrication equipment, the method further includes determining the parameter compensation value corresponding to the actual environmental noise.

[0136] Accordingly, the aforementioned control preparation equipment prepares photonic quantum GKP states based on control signals, including compensating the control signals based on parameter compensation values; and preparing photonic quantum GKP states based on the compensated control signals.

[0137] In this embodiment, in some cases, when the above-mentioned error sources include the current Fock state cutoff condition, the above-mentioned error sources may also include the real environmental noise of the preparation equipment. Therefore, when the preparation equipment is re-controlled to prepare the photonic quantum GKP state, it is also necessary to eliminate the influence of the above-mentioned real environmental noise. Thus, when it is determined that the above-mentioned error sources include the current Fock state cutoff condition and the real environmental noise of the preparation equipment, the parameter compensation value corresponding to the real environmental noise can also be determined. Then, upon returning to execute the aforementioned calling agent, and based on the preparation target and hardware constraints, generating the circuit simulation information and multiple sets of candidate parameters for the simulation preparation device to determine the new current optimal simulation parameters, it can first determine whether there is a parameter compensation value determined in the previous iteration due to error sources including the real environmental noise of the preparation equipment. If so, after converting the new current optimal simulation parameters into the control signal of the preparation equipment, the control signal obtained from the conversion of the new current optimal simulation parameters can be compensated based on the parameter compensation value, thereby controlling the preparation equipment to perform photonic quantum GKP state preparation based on the compensated control signal. Conversely, if not, after converting the new current optimal simulation parameters into the control signal of the preparation equipment, the preparation equipment can be directly controlled to perform photonic quantum GKP state preparation based on the obtained new control signal.

[0138] In this embodiment, when the error source includes the current Fock state cutoff condition, by optimizing the current Fock state cutoff condition and correcting the simulation preparation device based on the optimized current Fock state cutoff condition, the influence of the device parameters of the simulation preparation device on the preparation of the photonic GKP state can be eliminated, thereby improving the accuracy of the updated current optimal simulation parameters and thus improving the quality of the final obtained photonic GKP state.

[0139] Based on the above embodiments, in an exemplary embodiment, such as Figure 8 As shown, the method for preparing this photonic GKP state includes the following steps:

[0140] S801, Obtain the preparation target of the photonic quantum GKP state and the hardware constraints of the preparation equipment for the photonic quantum GKP state.

[0141] S802, the preparation target and hardware constraints are input into the intelligent agent to obtain the circuit simulation information of the simulated preparation device; and the preparation target, hardware constraints and circuit simulation information are input into the intelligent agent to obtain multiple sets of candidate parameters of the simulated preparation device.

[0142] S803, obtain the simulation results of the simulated preparation device preparing photonic GKP states under each set of candidate parameters by simulating photonic quantum circuits.

[0143] The simulation results include the effective compression ratio.

[0144] S804: If the obtained simulation results do not meet the preset conditions, call the agent to correct the multiple sets of candidate parameters, obtain new sets of candidate parameters, and return to S803 based on the new sets of candidate parameters.

[0145] S805, if the obtained simulation results meet the preset conditions, select the simulation result with the highest quality evaluation index among all the currently determined simulation results to obtain the optimal result, and determine the simulation parameters corresponding to the optimal result as the current optimal simulation parameters.

[0146] S806 converts the current optimal simulation parameters into control signals for the fabrication equipment.

[0147] S807, if a parameter compensation value determined in the previous iteration exists, the control signal is compensated based on the parameter compensation value, and the preparation device is controlled to prepare the photonic GKP state based on the compensated control signal; or, if a parameter compensation value determined in the previous iteration does not exist, the preparation device is controlled to prepare the photonic GKP state based on the control signal.

[0148] S808, obtain the preparation results of the photonic quantum GKP state preparation equipment.

[0149] S809, determine the result error between the preparation result and the simulation result corresponding to the current optimal simulation parameters.

[0150] S810, if the result error is greater than the preset error, calls the agent to determine the source of the result error.

[0151] S811, when the error source includes the real environmental noise of the preparation equipment, determine the parameter compensation value corresponding to the real environmental noise of the preparation equipment, and when the error source includes the current Fock state truncation condition, optimize the current Fock state truncation condition to obtain the optimized current Fock state truncation condition.

[0152] S812, based on the optimized current Fock state cutoff condition, the simulation preparation device is corrected, and based on the corrected simulation preparation device, the operation of S802 is returned.

[0153] S813, if the result error is less than or equal to the preset error, determine that the preparation of the photonic quantum GKP state is complete.

[0154] The specific implementation methods of S801-S813 are the same as those in the above method embodiments, and will not be repeated here.

[0155] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0156] Based on the same inventive concept, this application also provides an apparatus for preparing photonic GKP states using the aforementioned method for preparing photonic GKP states. The solution provided by this apparatus is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the photonic GKP state preparation apparatus provided below can be found in the limitations of the photonic GKP state preparation method described above, and will not be repeated here.

[0157] In one exemplary embodiment, such as Figure 9 As shown, a device for preparing photonic quantum GKP states is provided, comprising: a first acquisition module 910, a generation module 920, a first determination module 930, and a control module 940, wherein:

[0158] The first acquisition module 910 is used to acquire the preparation target of the photonic quantum GKP state and the hardware constraints of the preparation equipment for preparing the photonic quantum GKP state.

[0159] The generation module 920 is used to call the intelligent agent to generate circuit simulation information and multiple sets of candidate parameters for the simulated preparation device based on the preparation target and hardware constraints. The simulated preparation device is used to simulate the preparation of photonic GKP states, and the circuit simulation information is used to construct simulated photonic circuits. Each candidate parameter includes a post-selection strategy and circuit parameters of the simulated photonic circuit.

[0160] The first determining module 930 is used to determine the current optimal simulation parameters based on the simulation results of the simulated preparation device preparing photonic GKP states under each set of candidate parameters through simulated photonic quantum circuits.

[0161] The control module 940 is used to control the preparation equipment to prepare photonic quantum GKP states based on the current optimal simulation parameters.

[0162] In one exemplary embodiment, the first determining module 930 includes:

[0163] The acquisition unit is used to acquire the simulation results of the simulated preparation device preparing photonic GKP states by simulating photonic quantum circuits under each set of candidate parameters;

[0164] The correction unit is used to call the agent to correct multiple sets of candidate parameters when the obtained simulation results do not meet the preset conditions, so as to obtain new sets of candidate parameters, and trigger the acquisition unit based on the new sets of candidate parameters.

[0165] The determining unit is used to select the optimal result among all the currently determined simulation results, and determine the simulation parameters corresponding to the optimal result as the current optimal simulation parameters, provided that the obtained simulation results meet the preset conditions.

[0166] In an exemplary embodiment, the simulation results include effective compression, and the preset conditions include any of the following: each of the obtained simulation results includes a target result, the effective compression in the target result is greater than or equal to a preset compression threshold; the current round of each of the obtained simulation results reaches a preset round; the difference between the maximum values ​​of the effective compression determined in two consecutive rounds is less than a preset threshold.

[0167] In an exemplary embodiment, the determining unit is specifically used to: select the simulation result with the highest quality evaluation index among all currently determined simulation results to obtain the optimal result; wherein the quality evaluation index is determined based on at least one of effective compression, fidelity, Wigner function distribution similarity, and preparation success rate.

[0168] In one exemplary embodiment, the control module 940 includes:

[0169] The conversion unit is used to convert the current optimal simulation parameters into control signals for the fabrication equipment;

[0170] The control unit is used to control the preparation equipment to prepare photonic quantum GKP states based on control signals.

[0171] In one exemplary embodiment, the device further includes:

[0172] The second acquisition module is used to acquire the preparation results of the photonic quantum GKP state preparation by the preparation equipment;

[0173] The second determining module is used to determine the result error between the preparation result and the simulation result corresponding to the current optimal simulation parameters;

[0174] The third determination module is used to call the intelligent agent to determine the source of the error when the result error is greater than the preset error.

[0175] The update module is used to update the control signal based on the source of error, and to control the preparation equipment to prepare photonic GKP states based on the updated control signal.

[0176] In an exemplary embodiment, the update module is specifically used to: determine the parameter compensation value corresponding to the real environmental noise when the error source includes the real environmental noise of the preparation equipment; and compensate the control signal based on the parameter compensation value to obtain the updated control signal.

[0177] In an exemplary embodiment, the update module is specifically configured to: optimize the current Fock state truncation condition when the error source includes the current photon number Fock state truncation condition, to obtain the optimized current Fock state truncation condition; correct the simulation preparation device based on the optimized current Fock state truncation condition, and trigger the generation module 920 based on the corrected simulation preparation device.

[0178] In an exemplary embodiment, the updating module is further configured to: determine the parameter compensation value corresponding to the real environmental noise when the error source also includes the real environmental noise of the preparation equipment;

[0179] The control module is also used to: compensate the control signal based on the parameter compensation value; and control the preparation equipment to prepare photonic quantum GKP states based on the compensated control signal.

[0180] In an exemplary embodiment, the generation module 920 is specifically used to: input the preparation target and hardware constraints into the intelligent agent to obtain the circuit simulation information of the simulated preparation device; input the preparation target, hardware constraints and circuit simulation information into the intelligent agent to obtain multiple sets of candidate parameters of the simulated preparation device.

[0181] Each module in the aforementioned apparatus for preparing photonic GKP states can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0182] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data such as the preparation target of the photonic GKP state and the hardware constraints of the preparation equipment. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for preparing a photonic GKP state.

[0183] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0184] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0185] In one embodiment, a computer-readable storage medium is provided that stores a computer program or instructions thereon, which, when executed by a processor, implement the steps in the above method embodiments.

[0186] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0187] In one embodiment, a quantum computing device is provided, including a classical computing unit and a quantum computing unit, which is used to implement the steps in the above-described method embodiments. In this embodiment, the classical computing unit and the quantum computing unit are connected. Optionally, the classical computing unit includes the computer device described above.

[0188] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0189] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0190] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0191] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for preparing a photonic quantum GKP state, characterized in that, The method includes: The objective of obtaining the photonic quantum GKP state and the hardware constraints of the equipment used to prepare the photonic quantum GKP state; The intelligent agent is invoked to generate circuit simulation information and multiple sets of candidate parameters for the simulated fabrication device based on the fabrication goal and the hardware constraints. The simulated fabrication device is used to simulate the fabrication of photonic GKP states, and the circuit simulation information is used to construct simulated photonic circuits. Each candidate parameter includes a post-selection strategy and the circuit parameters of the simulated photonic circuit. Based on the simulation results of the simulated preparation device using the simulated photonic quantum circuit to simulate the preparation of photonic GKP states under each set of candidate parameters, the current optimal simulation parameters are determined; and, The fabrication equipment is controlled to prepare photonic GKP states based on the current optimal simulation parameters.

2. The method according to claim 1, characterized in that, The simulation results of preparing photonic GKP states using the simulated photonic quantum circuit under each set of candidate parameters based on the simulated preparation device determine the current optimal simulation parameters, including: The simulation results of the simulated preparation device preparing photonic GKP states under each set of candidate parameters through the simulated photonic quantum circuit are obtained. If the obtained simulation results do not meet the preset conditions, the intelligent agent is invoked to correct the multiple sets of candidate parameters to obtain new multiple sets of candidate parameters. Based on the new multiple sets of candidate parameters, the operation of obtaining the simulation results of the simulation preparation device preparing photonic GKP states through the simulated photonic quantum circuit under each set of candidate parameters is returned. If the obtained simulation results meet the preset conditions, select the optimal result among all the currently determined simulation results, and determine the simulation parameters corresponding to the optimal result as the current optimal simulation parameters.

3. The method according to claim 2, characterized in that, The simulation results include the effective compression ratio, and the preset conditions include any one of the following: The simulation results obtained include target results, wherein the effective compression ratio in the target results is greater than or equal to a preset compression ratio threshold; The current round of each simulation result has reached the preset round; The difference between the maximum effective compression values ​​determined in two consecutive rounds is less than a preset threshold.

4. The method according to claim 2, characterized in that, The selection of the optimal result from all currently determined simulation results includes: The optimal result is obtained by selecting the simulation result with the highest quality evaluation index from all currently determined simulation results. The quality evaluation index is determined based on at least one of effective compressibility, fidelity, Wigner function distribution similarity, and preparation success rate.

5. The method according to any one of claims 1-4, characterized in that, The control of the fabrication device to prepare photonic quantum GKP states based on the current optimal simulation parameters includes: The current optimal simulation parameters are converted into control signals for the preparation equipment; The preparation equipment is controlled to prepare photonic quantum GKP states based on the control signal.

6. The method according to claim 5, characterized in that, The method further includes: Obtain the preparation results of the photonic quantum GKP state preparation using the aforementioned preparation equipment; Determine the result error between the preparation result and the simulation result corresponding to the current optimal simulation parameters; If the result error is greater than a preset error, the agent is invoked to determine the source of the result error; Based on the source of the error, the control signal is updated, and the preparation device is controlled to prepare photonic GKP states based on the updated control signal.

7. The method according to claim 6, characterized in that, The step of updating the control signal based on the error source includes: When the error source includes the actual environmental noise of the preparation equipment, determine the parameter compensation value corresponding to the actual environmental noise; Based on the parameter compensation value, the control signal is compensated to obtain an updated control signal.

8. The method according to claim 6, characterized in that, The step of updating the control signal based on the error source includes: When the error sources include the current photon number Fock state truncation condition, the current Fock state truncation condition is optimized to obtain the optimized current Fock state truncation condition; Based on the optimized current Fock state truncation condition, the simulation preparation device is modified, and based on the modified simulation preparation device, the execution calling agent is returned to generate the circuit simulation information and multiple sets of candidate parameters of the simulation preparation device based on the preparation target and the hardware constraints.

9. The method according to claim 8, characterized in that, If the error sources also include the actual ambient noise of the fabrication equipment, the method further includes: Determine the parameter compensation value corresponding to the actual environmental noise; The method of controlling the preparation device to prepare photonic quantum GKP states based on the control signal further includes: The control signal is compensated based on the parameter compensation value; The preparation equipment is controlled to prepare photonic quantum GKP states based on a compensated control signal.

10. The method according to any one of claims 1-4, characterized in that, The invoked intelligent agent, based on the preparation target and the hardware constraints, generates circuit simulation information and multiple sets of candidate parameters for the simulation preparation device, including: The preparation target and the hardware constraints are input into the intelligent agent to obtain the circuit simulation information of the simulated preparation device; The preparation target, the hardware constraints, and the circuit simulation information are input into the intelligent agent to obtain multiple sets of candidate parameters for the simulation preparation device.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 10.

12. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that, When the computer-executable instructions are executed by a processor, the method of any one of claims 1 to 10 is implemented.

13. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the method of any one of claims 1 to 10.

14. A quantum computing device, comprising a classical computing unit and a quantum computing unit, characterized in that, The quantum computing device is used to perform the method as described in any one of claims 1 to 10.

Images