Data processing system, method, device, storage medium and computer program product

Abstract

The invention discloses a data processing system, method and device, a storage medium and a computer program product, and relates to the technical field of cooperative computing of artificial intelligence and quantum computing, the system comprises a computer device and a quantum computing device cluster, an artificial intelligence model runs in the computer device, and the computer device is connected with the quantum computing device; the computer device is used for determining a data processing task in the operation process of the artificial intelligence model, selecting an optimal quantum computing device, converting the data processing task into a quantum program conforming to a quantum circuit standard format, and sending the quantum program to the optimal quantum computing device; the optimal quantum computing device is used for executing the quantum program to obtain a quantum computing result, converting the quantum computing result into a data format supported by the artificial intelligence model and returning the data format to the computer device. According to the invention, efficient interaction between the quantum computing device and the artificial intelligence model running in the computer device is realized.

Description

Technical Field

[0001] The present application relates to the field of collaborative computing technology between artificial intelligence and quantum computing, and in particular to a data processing system, method, device, storage medium and computer program product based on a heterogeneous architecture of classical computers and quantum computing devices, for realizing efficient calling and task scheduling of quantum computing resources by classical artificial intelligence models. Background Art

[0002] Artificial intelligence models demonstrate powerful capabilities in processing complex data and recognizing patterns. However, as data volumes and computational complexity increase, they are limited by the computing power of classical computers, making it difficult for AI models to achieve optimal performance in certain tasks. Meanwhile, quantum computing devices, with their quantum bit superposition, entanglement, and interference properties, theoretically can surpass the computational limits of classical computers, offering new possibilities for solving complex computational problems.

[0003] However, in related technologies, the interaction between quantum computing devices and classical computers presents numerous obstacles. For one thing, the operating environment and operation methods of quantum computing devices differ significantly from those of classical computers, making it difficult for AI models running on classical computers to directly access quantum computing resources. This poses challenges in data transmission and task scheduling. Furthermore, the complex data format of quantum computing results makes it difficult for traditional data transmission and processing frameworks to efficiently transmit and parse them. Data formats and transmission mechanisms that match the characteristics of quantum computing are required. Furthermore, related technologies lack contextual analysis of historical task data, making it impossible to dynamically optimize task scheduling based on real-time hardware status and historical data, resulting in low efficiency.

[0004] Therefore, how to achieve efficient interaction between quantum computing devices and classical computer devices so that the artificial intelligence models running in classical computer devices can fully utilize quantum computing resources is a technical problem that technicians in this field need to solve. Summary of the Invention

[0005] The purpose of this application is to provide a data processing system, method, device, storage medium and computer program product to achieve efficient interaction between quantum computing devices and computer devices, so that the artificial intelligence model running in the computer device can fully utilize quantum computing resources.

[0006] To achieve the above objectives, the present application provides a data processing system, comprising a computer device and a quantum computing device cluster, wherein the computer device runs an artificial intelligence model, the quantum computing device cluster includes multiple quantum computing devices, and the computer device is connected to the quantum computing devices;

[0007] The computer device is configured to determine a data processing task during running of the artificial intelligence model, select an optimal quantum computing device according to task information of the data processing task and hardware states of quantum computing devices in the quantum computing device cluster, convert the data processing task into a quantum program in a quantum circuit standard format, and send the quantum program to the optimal quantum computing device.

[0008] The optimal quantum computing device is configured to execute the quantum program to obtain a quantum computing result, and convert the quantum computing result into a data format supported by the artificial intelligence model and return the quantum computing result to the computer device.

[0009] To achieve the above object, the present application provides a data processing method, which is applied to a computer device in the above data processing system, and the method comprises the following steps:

[0010] A data processing task is determined during running of an artificial intelligence model, and an optimal quantum computing device is selected according to task information of the data processing task and hardware states of quantum computing devices in a sub-device cluster.

[0011] The data processing task is converted into a quantum program in a quantum circuit standard format, and the quantum program is sent to the optimal quantum computing device, so that the optimal quantum computing device executes the quantum program to obtain a quantum computing result, and the quantum computing result is converted into a data format supported by the artificial intelligence model.

[0012] The quantum computing result returned by the optimal quantum computing device is received.

[0013] To achieve the above object, the present application provides a data processing method, which is applied to a quantum computing device in the above data processing system, and the method comprises the following steps:

[0014] A quantum program sent by a computer device is received.

[0015] A quantum computing result is obtained by executing the quantum program.

[0016] The quantum computing result is converted into a data format supported by the artificial intelligence model and returned to the computer device.

[0017] To achieve the above object, the present application provides a computer device, which comprises:

[0018] A memory is configured to store a computer program.

[0019] A processor is configured to execute the computer program to implement the steps of the above data processing method of the computer device side.

[0020] To achieve the above objectives, the present application provides a quantum computing device, comprising:

[0021] Memory for storing computer programs;

[0022] A processor is used to implement the steps of the data processing method on the quantum computing device side as described above when executing the computer program.

[0023] To achieve the above objectives, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above data processing method are implemented.

[0024] To achieve the above objectives, the present application provides a computer program product, including a computer program, which implements the steps of the above data processing method when executed by a processor.

[0025] The data processing system provided in this application achieves seamless integration of classical and quantum computing devices through the close connection between computer devices and quantum computing device clusters. The computer devices can intelligently determine data processing tasks based on the operational requirements of the artificial intelligence model and select the optimal quantum computing device based on task information and the hardware status of the quantum computing device. During this process, the system embeds historical tasks and hardware data in context, providing strong support for intelligent task management. This context embedding mechanism enables the system to dynamically optimize task scheduling based on the execution status of historical tasks and real-time hardware status. This not only solves the problem of difficulty in accessing quantum computing resources in related technologies, but also improves resource utilization efficiency, ensuring that tasks are efficiently executed on the most suitable quantum computing device. Furthermore, the computer devices convert data processing tasks into quantum programs that conform to the standard format of quantum circuits and send the quantum programs to the optimal quantum computing device. This standardized quantum program conversion mechanism enables data processing tasks to be universally executed on different quantum computing devices, enabling computer devices to utilize quantum resources. Furthermore, the quantum computing results obtained by the quantum computing device after executing the quantum program can be converted into a data format supported by the artificial intelligence model and returned to the computer device, allowing the artificial intelligence model to directly use the quantum computing results, improving the processing efficiency of the artificial intelligence model. This application also discloses a data processing method, a computer device, a quantum computing device, a computer-readable storage medium, and a computer program product, which can also achieve the above-mentioned technical effects.

[0026] It should be understood that the foregoing general description and the following detailed description are merely illustrative and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. The drawings are used to provide a further understanding of the present disclosure and constitute part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure, but do not constitute a limitation of the present disclosure. In the drawings:

[0028] Figure 1 is a structural diagram of a data processing system according to an exemplary embodiment;

[0029] Figure 2 is a flow chart showing a data processing method according to an exemplary embodiment;

[0030] Figure 3 is a flow chart of another data processing method according to an exemplary embodiment;

[0031] Figure 4 The figure is a structural diagram of a computer device or a quantum computing device according to an exemplary embodiment. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. In addition, in the embodiments of the present application, "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0033] This embodiment provides a data processing system, including a computer device 10 and a quantum computing device cluster 20. The computer device 10 runs an artificial intelligence model. The quantum computing device cluster 20 includes multiple quantum computing devices 201. The computer device 10 is connected to the quantum computing devices 201.

[0034] In a specific implementation, the computer device 10 and the quantum computing device are connected via a network, such as Ethernet or a fiber optic network, to ensure fast and stable data transmission. The computer device 10 can be a high-performance computing device such as a server or workstation, used to run artificial intelligence models, such as deep learning models and reinforcement learning models, which can handle complex tasks such as pattern recognition and data analysis. The quantum computing device cluster 20 is composed of multiple quantum computing devices, each of which has different hardware characteristics such as the number of quantum bits and quantum gate fidelity, and is capable of performing quantum computing tasks. This connection method enables the computer device 10 to call on the computing resources of the quantum computing device, realizing the collaborative work of quantum computing and classical computing.

[0035] As a feasible implementation, the quantum computing device is used to: send authorization information to the computer device 10; the computer device 10 is further used to: log in to the quantum computing device based on the authorization information to establish a connection with the quantum computing device.

[0036] The authorization information may include the quantum computing device's identity and access rights key, which are used to verify the legitimacy of the quantum computing device and the access rights of the computer device. In a specific implementation, the quantum computing device sends the authorization information to the computer device 10 via a secure communication protocol, such as TLS / SSL (Transport Layer Security / Secure Sockets Layer). After receiving the authorization information, the computer device 10 verifies it. If the verification is successful, it uses the corresponding login credentials to log in to the quantum computing device and establish a secure communication connection. This process ensures that only authorized computer devices 10 can access the quantum computing device, improving system security.

[0037] The computer device 10 is configured to determine a data processing task during the operation of the artificial intelligence model, select an optimal quantum computing device based on task information of the data processing task and the hardware status of each quantum computing device in the quantum computing device cluster 20, convert the data processing task into a quantum program conforming to a standard format for quantum circuits, and send the quantum program to the optimal quantum computing device;

[0038] In practice, when running an AI model, computer device 10 determines the data processing task to be performed based on the model's requirements and the characteristics of the input data. It then selects the optimal quantum computing device based on the task information and the hardware status of each quantum computing device in quantum computing device cluster 20. Task information may include the required quantum circuit depth, number of bits, and number of quantum gates, while hardware status may include information such as availability and error rate.

[0039] As a feasible implementation method, the process of the computer device 10 selecting the best quantum computing device based on the task information of the data processing task and the hardware status of each quantum computing device in the quantum computing device cluster includes: determining in the quantum computing device cluster candidate quantum computing devices whose hardware information meets the task information of the data processing task; and selecting the one with the best hardware status from the candidate quantum computing devices as the best quantum computing device.

[0040] In a specific implementation, the computer device 10 identifies candidate quantum computing devices in the quantum computing device cluster 20 whose hardware information meets the requirements based on the task information of the data processing task. The hardware information may include the number of qubits, the number of quantum gates, the fidelity of quantum gates, the coherence time, etc. Simultaneously, the computer device 10 obtains the hardware status of each quantum computing device in the quantum computing device cluster 20 in real time and selects the one with the best hardware status from the candidate quantum computing devices as the optimal quantum computing device through an intelligent scheduling algorithm, such as a rule-based algorithm or a machine learning algorithm.

[0041] After selecting the optimal quantum computing device, the computer device converts the data processing task into a quantum program. This quantum program is written in a standard quantum circuit format, such as OpenQASM (Open Quantum Assembly Language), which can be understood and executed by the quantum computing device. This conversion process involves breaking the task into a series of quantum gate operations and optimizing them according to the hardware characteristics of the quantum computing device. Finally, the computer device 10 transmits the quantum program to the optimal quantum computing device via the network. This process enables intelligent allocation of data processing tasks and efficient transmission of quantum programs, improving overall system performance.

[0042] As a preferred embodiment, the process of the computer device 10 sending the quantum program to the optimal quantum computing device includes: the computer device 10 encrypts the quantum program using a preset encryption method and sends the program to the optimal quantum computing device;

[0043] In a specific implementation, computer device 10 can use the Advanced Encryption Standard (AES)-256 encryption algorithm to encrypt the quantum program, ensuring data transmission security and preventing data theft or tampering during transmission. AES-256 is a symmetric encryption algorithm with high security and encryption efficiency. Before sending the quantum program, computer device 10 generates a random key and uses this key to encrypt the quantum program. The encrypted quantum program and key are then sent to the optimal quantum computing device (via a secure key exchange mechanism). After receiving the encrypted quantum program, the optimal quantum computing device decrypts it using the same key to obtain an executable quantum program. This encrypted transmission process not only protects the confidentiality of the quantum program but also prevents potential man-in-the-middle attacks, thereby improving the overall security of the system.

[0044] The optimal quantum computing device is used to execute the quantum program to obtain a quantum computing result, convert the quantum computing result into a data format supported by the artificial intelligence model and return it to the computer device 10.

[0045] In specific implementations, after receiving a quantum program, the optimal quantum computing device loads it into a quantum processor for execution. The quantum processor operates on qubits according to the quantum gate operation instructions in the quantum program, completing the quantum computing task and generating quantum computation results. These quantum computation results may include probability distributions of quantum states, measurement results, and other information. These results are typically stored as binary data in the quantum computing device's memory. The optimal quantum computing device then converts the quantum computation results into a data format supported by the AI ​​model, such as JSON (JavaScript Object Notation), and sends them back to the computer device 10 via the network. The JSON data can contain metadata, such as the number of qubits, measurement basis, and compression method. This process enables the efficient transmission and conversion of quantum computation results, enabling them to be further processed and analyzed by the AI ​​model.

[0046] As a preferred embodiment, the process of the optimal quantum computing device converting the quantum computing result into a data format supported by the artificial intelligence model and returning it to the computer device 10 includes: determining the structural characteristics of the quantum computing result; wherein the structural characteristics include any one or a combination of any several of the superposition state, tensor state and universal state; according to the structural characteristics of the quantum computing result, a corresponding compression strategy is used to compress the quantum computing result to obtain a compressed quantum computing result, and the compressed quantum computing result is converted into a data format supported by the artificial intelligence model and returned to the computer device 10.

[0047] It should be noted that quantum computing results usually have complex structures, such as superposition states representing quantum bits in a superposition of multiple states, tensor states representing quantum states that can be decomposed into a tensor product of multiple low-dimensional quantum states, and general states which are other states that cannot be simply classified.

[0048] In a specific implementation, after obtaining the quantum computing result, the optimal quantum computing device first determines its structural characteristics through an analysis algorithm. For superposition states, a differential compression strategy can be used to transmit only the difference data from the average value, reducing the data volume; for tensor states, singular value decomposition can be performed to retain only the subset data corresponding to the main singular values; for general states, a fragmented streaming transmission method can be used to divide the data into multiple small blocks and transmit them block by block, avoiding the transmission of a large amount of data at once. This process not only reduces the data transmission volume and improves transmission efficiency, but also ensures that the quantum computing result can be effectively utilized by the artificial intelligence model.

[0049] As a feasible implementation, the process of the optimal quantum computing device determining the structural characteristics of the quantum computing result includes: calculating the ground state probability standard deviation of the quantum computing result, if the ground state probability standard deviation is less than a first preset value, determining that the structural characteristics of the quantum computing result are superposition states; performing singular value decomposition on the state matrix in the quantum computing result, if the data volume of the data corresponding to the first k singular values accounts for more than a second preset proportion of the data volume of the state matrix, determining that the structural characteristics of the quantum computing result are tensor states; if the ground state probability standard deviation of the quantum computing result is greater than or equal to the first preset value, and the data volume of the data corresponding to the first k singular values accounts for less than or equal to the second preset proportion of the data volume of the state matrix, determining that the structural characteristics of the quantum computing result are general states.

[0050] The ground state probability standard deviation is an index that measures the degree of superposition of a quantum state, and the smaller the standard deviation, the closer the quantum state is to a completely superposed state. The first preset value can be set according to actual needs, for example, 5%. Singular value decomposition is a matrix decomposition method that can decompose a state matrix into the product of singular values and singular vectors. The proportion of the data volume corresponding to the first k singular values reflects the tensor characteristics of the quantum state, and the larger the proportion, the closer the quantum state is to a tensor state. The second preset value can also be set according to actual needs, for example, 90%. The optimal quantum computing device accurately determines the structural characteristics of the quantum computing result through these mathematical methods and preset thresholds, providing a scientific basis for subsequent compression operations and improving the accuracy and efficiency of data processing.

[0051] As a feasible implementation method, the optimal quantum computing device uses a corresponding compression strategy to compress the quantum computing result according to the structural characteristics of the quantum computing result to obtain a compressed quantum computing result, and the process of converting the compressed quantum computing result into a data format supported by the artificial intelligence model and returning it to the computer device 10 includes: if the quantum computing result is a superposition state, the differential data in the quantum computing result is converted as a compressed quantum computing result into a data format supported by the artificial intelligence model and returned to the computer device 10; if the quantum computing result is a tensor state, the data corresponding to the first k singular values ​​is converted as a compressed quantum computing result into a data format supported by the artificial intelligence model and returned to the computer device 10; if the quantum computing result is a universal state, the quantum computing result is divided into multiple shard data as compressed quantum computing results, and the multiple shard data are respectively converted into the data format supported by the artificial intelligence model, and returned to the computer device 10 in a streaming manner.

[0052] In specific implementations, for superposition states, due to their uniform probability distribution characteristics, only the difference data from the average value can be transmitted, which can greatly reduce the amount of data. For example, for a 20-qubit system, the probability distribution data volume of the full superposition state is approximately 8MB. After differential compression, the data volume can be reduced to about 1MB. For tensor states, singular value decomposition is used to retain the subset data corresponding to the main singular values, which can effectively reduce the dimension and complexity of the data. For universal states, due to their complex structure, direct compression is difficult. Therefore, a fragmented streaming method is adopted to divide the data into multiple small blocks and transmit them block by block, avoiding the transmission of large amounts of data at one time, reducing memory usage and transmission delays. It can be seen that each compression strategy combines the characteristics of quantum computing results, and can minimize the amount of data transmission while ensuring data integrity and accuracy, thereby improving system performance and efficiency.

[0053] As a preferred embodiment, the process of the optimal quantum computing device converting the quantum computing result into a data format supported by the artificial intelligence model and returning it to the computer device 10 includes: the optimal quantum computing device converts the quantum computing result into a data format supported by the artificial intelligence model, encrypts it using a preset encryption method, and returns it to the computer device 10.

[0054] In a specific implementation, the optimal quantum computing device also uses the AES-256 encryption algorithm to encrypt the quantum computing result converted into a data format. The encrypted data is sent back to the computer device 10 through the network, and the computer device 10 receives the encrypted data and uses the same key to decrypt it to obtain the quantum computing result that can be directly used for artificial intelligence model analysis. This encryption transmission process not only protects the confidentiality of the quantum computing result, but also prevents the data from being tampered with or leaked during transmission, ensuring the integrity and security of the data and improving the reliability of the system.

[0055] The data processing system provided by the embodiments of the present application realizes seamless integration of classical computer devices and quantum computing devices through the close connection between the computer device 10 and the quantum computing device cluster 20. The computer device 10 can intelligently determine data processing tasks according to the running requirements of the artificial intelligence model, and select the optimal quantum computing device according to the task information and the hardware state of the quantum computing device. In this process, the system embeds historical tasks and hardware data through context, providing strong support for intelligent task management. This context embedding mechanism enables the system to dynamically optimize task scheduling according to the execution of historical tasks and real-time hardware state, not only solving the problem of difficult quantum computing resource calling in related technologies, but also improving resource utilization efficiency and ensuring efficient execution of tasks on the most suitable quantum computing device. In addition, the computer device 10 converts the data processing task into a quantum program in a quantum circuit standard format and sends the quantum program to the optimal quantum computing device. This standardized quantum program conversion mechanism enables the data processing task to be executed universally on different quantum computing devices, realizing the utilization of quantum resources by the computer device 10. At the same time, the quantum computing result obtained by the quantum computing device after executing the quantum program can be converted into a data format supported by the artificial intelligence model and returned to the computer device 10, so that the artificial intelligence model can directly use the quantum computing result, improving the processing efficiency of the artificial intelligence model.

[0056] On the basis of the above-mentioned embodiments, as a preferred implementation manner, the computer device 10 is further configured to predict the expected execution time of the data processing task based on the task information of the data processing task and the hardware information of the optimal quantum computing device by using a deep learning model.

[0057] In a specific implementation, the artificial intelligence model in the computer device 10 may include a deep learning model, such as a neural network. By learning from historical task data, this model can predict task execution times based on current task information (such as task type, data size, and accuracy requirements) and optimal quantum computing device hardware information (such as the number of qubits, quantum gate fidelity, and error rate). For example, for a quantum chemistry simulation task, the model can predict that the task will take approximately 10 minutes to complete based on the complexity of the task and the performance of the quantum computing device. This prediction function provides users with a time reference, helping to rationally arrange tasks and resources, improving system efficiency and user experience. Leveraging the predictive capabilities of the deep learning model, this function provides users with task execution time estimates, helping to optimize resource allocation and task scheduling.

[0058] As a feasible implementation method, the computer device 10 is also used to: construct a historical task dataset; wherein the historical task dataset is used to record the task information of the historical tasks, the hardware information and execution time of the quantum computing device that executes the historical tasks; and train a deep learning model based on the historical task dataset.

[0059] In specific implementations, during operation, the computer device 10 collects relevant information about each task, including task information such as task type, data size, and accuracy requirements, as well as hardware information such as the number of qubits, quantum gate fidelity, and error rate of the quantum computing device performing the task, as well as the actual execution time of the task. This information is recorded in a historical task dataset, forming a dataset containing a large number of samples. The computer device 10 then uses this dataset to train a deep learning model. By learning the patterns and regularities in these samples, the model can better predict the execution time of new tasks. For example, the model can learn the differences in execution time between different types of tasks on quantum computing devices with different hardware configurations, thereby providing more accurate time estimates when making predictions.

[0060] As a preferred embodiment, the computer device 10 is further configured to: update the estimated completion time of the data processing task according to the real-time hardware status of the optimal quantum computing device during the process of the optimal quantum computing device executing the quantum program.

[0061] In a specific implementation, the computer device 10 monitors the hardware status of the optimal quantum computing device in real time during task execution, such as queue length, error rate, availability, etc. If a change in hardware status is detected, such as an increase in queue length resulting in a longer task waiting time, or an increase in error rate resulting in the need to re-execute the task, the computer device 10 will dynamically update the estimated completion time of the task based on this real-time information. For example, if the task was originally expected to take 10 minutes to complete, but due to an increase in queue length, the estimated completion time may be updated to 12 minutes. This dynamic update function makes the system's predictions more accurate to actual conditions, can promptly reflect changes during task execution, and improve the real-time performance and reliability of the system.

[0062] As a preferred embodiment, the computer device 10 is further used to: use the artificial intelligence model to analyze the received quantum computing results, and / or locate anomalies in the error log.

[0063] In specific implementations, the artificial intelligence model in the computer device 10 can not only be used to predict task execution time, but also analyze quantum computing results. For example, for the results of quantum chemistry simulations, the model can analyze the probability distribution of quantum states and extract characteristic information related to chemical reactions, providing valuable reference for chemical research. At the same time, the model can also locate anomalies in error logs. For example, by analyzing the error code and context information in the log, it can quickly locate the location of program errors or hardware failures. For example, if frequent quantum gate operation errors appear in the log, the model can determine that it may be a hardware failure of the quantum computing device and issue an alarm in a timely manner. This function improves the intelligence level of the system, can automatically discover and handle problems, reduces manual intervention, and improves the stability and reliability of the system.

[0064] Based on the above embodiment, as a preferred implementation manner, the computer device 10 includes an input interface and an output interface; the computer device 10 is further used to: obtain the data processing task through the input interface, and display the execution result of the quantum program and / or the quantum computing result through the output interface.

[0065] In a specific implementation, the input interface of the computer device 10 can be a graphical user interface (GUI), a command line interface (CLI), or a network interface. Users can use these interfaces to input data processing tasks, such as uploading data files to be processed and setting task parameters. The output interface of the computer device 10 can be a display screen, a printer, or a network interface, used to display the execution results of the quantum program and the quantum computation results to the user. The execution results may include the success or failure of the quantum program and the reasons for the failure. For example, the quantum computation results can be intuitively displayed on the display screen in the form of charts or tables, or the results can be saved as a file and sent to the user via the network. This feature improves the interactivity of the system, allowing users to easily interact with the system and obtain task execution status and results, thereby enhancing the user experience.

[0066] The present application embodiment discloses a data processing method, which is applied to a computer device 10. Figure 2 , according to a flowchart of a data processing method shown in an exemplary embodiment, such as Figure 2 Shown, including:

[0067] S101: Determine a data processing task during the operation of the artificial intelligence model, and select an optimal quantum computing device based on task information of the data processing task and the hardware status of each quantum computing device in the quantum device cluster;

[0068] S102: Convert the data processing task into a quantum program that conforms to a standard format for quantum circuits, and send the quantum program to the optimal quantum computing device so that the optimal quantum computing device executes the quantum program to obtain a quantum computing result, and converts the quantum computing result into a data format supported by the artificial intelligence model;

[0069] S103: Receive the quantum computing result returned by the optimal quantum computing device.

[0070] The embodiment realizes efficient interaction between the quantum computing device and the computer device 10 through the close connection between the computer device 10 and the quantum computing device cluster 20, so that the artificial intelligence model can fully utilize quantum computing resources. The computer device 10 can intelligently determine a data processing task according to the running requirements of the artificial intelligence model, and select the best quantum computing device according to the task information and the hardware state of the quantum computing device. In this process, the system embeds historical tasks and hardware data through context, providing strong support for intelligent task management. This context embedding mechanism enables the system to dynamically optimize task scheduling according to the execution of historical tasks and real-time hardware state, not only solving the problem of difficult quantum computing resource calling in related technologies, but also improving resource utilization efficiency and ensuring that the task can be efficiently executed on the most suitable quantum computing device. In addition, the computer device 10 converts the data processing task into a quantum program in a quantum circuit standard format and sends the quantum program to the best quantum computing device. This standardized quantum program conversion mechanism enables the data processing task to be executed universally on different quantum computing devices, realizing the utilization of quantum resources by the computer device 10.

[0071] On the basis of the above embodiment, as a preferred implementation manner, the selecting the best quantum computing device according to the task information of the data processing task and the hardware state of each quantum computing device in the quantum device cluster comprises: determining a candidate quantum computing device whose hardware information meets the task information of the data processing task in the quantum computing device cluster; and selecting the quantum computing device with the optimal hardware state as the best quantum computing device from the candidate quantum computing devices.

[0072] On the basis of the above embodiment, as a preferred implementation manner, the method further comprises: predicting an estimated execution time of the data processing task based on the task information of the data processing task and the hardware information of the best quantum computing device by using a deep learning model.

[0073] On the basis of the above embodiment, as a preferred implementation manner, the method further comprises: constructing a historical task dataset; wherein the historical task dataset is used to record the task information of historical tasks, the hardware information of the quantum computing device executing the historical tasks, and the execution time; and training a deep learning model based on the historical task dataset.

[0074] On the basis of the above embodiment, as a preferred implementation manner, the method further comprises: updating the estimated completion time of the data processing task according to the real-time hardware state of the best quantum computing device during the execution of the quantum program by the best quantum computing device.

[0075] Based on the above embodiment, as a preferred implementation, it also includes: using the artificial intelligence model to analyze the received quantum computing results, and / or locating anomalies in the error log.

[0076] The specific implementation of each step in the above embodiment has been described in detail in the embodiment of the relevant data processing system and will not be elaborated here.

[0077] The present application discloses a data processing method, which is applied to quantum computing devices. Figure 3 , a flowchart of another data processing method according to an exemplary embodiment is shown, such as Figure 3 Shown, including:

[0078] S201: receiving a quantum program sent by a computer device;

[0079] S202: Execute the quantum program to obtain a quantum computing result;

[0080] S203: Convert the quantum computing result into a data format supported by the artificial intelligence model and return it to the computer device.

[0081] This embodiment achieves efficient interaction between the quantum computing devices 10 and the quantum computing device cluster 20 by tightly connecting them. This allows the AI ​​model to fully utilize quantum computing resources. The quantum computing results obtained by the quantum computing device after executing the quantum program can be converted into a data format supported by the AI ​​model and returned to the computer device. This allows the AI ​​model to directly use the quantum computing results, improving its processing efficiency.

[0082] Based on the above embodiment, as a preferred implementation method, converting the quantum computing result into a data format supported by the artificial intelligence model and returning it to the computer device 10 includes: determining the structural characteristics of the quantum computing result; wherein the structural characteristics include any one or a combination of any several of a superposition state, a tensor state, and a universal state; compressing the quantum computing result using a corresponding compression strategy according to the structural characteristics of the quantum computing result to obtain a compressed quantum computing result, and converting the compressed quantum computing result into a data format supported by the artificial intelligence model and returning it to the computer device 10.

[0083] Based on the above embodiment, as a preferred implementation manner, determining the structural characteristics of the quantum computing result includes: calculating the standard deviation of the ground state probability of the quantum computing result, and if the standard deviation of the ground state probability is less than a first preset value, determining that the structural characteristic of the quantum computing result is a superposition state; performing singular value decomposition on the state matrix in the quantum computing result, and if the proportion of the data volume corresponding to the first k singular values ​​to the data volume of the state matrix is ​​greater than a second preset value, determining that the structural characteristic of the quantum computing result is a tensor state; if the standard deviation of the ground state probability of the quantum computing result is greater than or equal to the first preset value, and the proportion of the data volume corresponding to the first k singular values ​​to the data volume of the state matrix is ​​less than or equal to the second preset value, determining that the structural characteristic of the quantum computing result is a universal state.

[0084] On the basis of the above embodiments, as a preferred implementation mode, a corresponding compression strategy is adopted according to the structural characteristics of the quantum computing result to perform a compression operation on the quantum computing result to obtain a compressed quantum computing result, and the compressed quantum computing result is converted into a data format supported by the artificial intelligence model and returned to the computer device 10, including: if the quantum computing result is a superposition state, the differential data in the quantum computing result is converted as a compressed quantum computing result into a data format supported by the artificial intelligence model and returned to the computer device 10; if the quantum computing result is a tensor state, the data corresponding to the first k singular values ​​is converted as a compressed quantum computing result into a data format supported by the artificial intelligence model and returned to the computer device 10; if the quantum computing result is a universal state, the quantum computing result is divided into multiple shard data as compressed quantum computing results, and the multiple shard data are respectively converted into the data format supported by the artificial intelligence model, and returned to the computer device 10 in a streaming manner.

[0085] The specific implementation of each step in the above embodiment has been described in detail in the embodiment of the relevant data processing system and will not be elaborated here.

[0086] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiment of the present application, the embodiment of the present application further provides a computer device, Figure 4 FIG. 1 is a structural diagram of a computer device according to an exemplary embodiment. Figure 4 As shown, the computer equipment includes:

[0087] Communication interface 1, capable of exchanging information with other devices such as network devices;

[0088] The processor 2 is connected to the communication interface 1 to implement information exchange with other devices and is used to execute the data processing method on the computer device side provided by one or more of the above technical solutions when running a computer program. The computer program is stored in the memory 3.

[0089] Of course, in actual application, the various components in the computer device 10 are coupled together through the bus system 4. It can be understood that the bus system 4 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 4 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 4 Various buses are labeled as bus system 4.

[0090] The memory 3 in the embodiment of the present application is used to store various types of data to support the operation of the computer device 10. Examples of such data include: any computer program used to operate on the computer device 10.

[0091] It is understood that the memory 3 can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a magnetic disk or a magnetic tape. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM).The memory 3 described in the embodiments of the present application is intended to include but is not limited to these and any other suitable types of memories.

[0092] The method disclosed in the above-mentioned embodiment of the present application can be applied to processor 2 or implemented by processor 2. Processor 2 may be an integrated circuit chip with signal processing capabilities. During the implementation process, each step of the above-mentioned method can be completed by the integrated logic circuit of the hardware in processor 2 or instructions in the form of software. The above-mentioned processor 2 can be a general-purpose processor, DSP, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 2 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in memory 3. Processor 2 reads the program in memory 3 and completes the steps of the above-mentioned method in combination with its hardware.

[0093] When the processor 2 executes the program, the corresponding process in the data processing method on the computer device side provided in this application is implemented. For the sake of brevity, it will not be repeated here.

[0094] In an exemplary embodiment, the present application also provides a quantum computing device, the structure of which can also be seen in Figure 4 When the processor 2 is used to run the computer program, it executes the data processing method on the quantum computing device side provided by one or more of the above technical solutions. The computer program is stored in the memory 3.

[0095] In an exemplary embodiment, the present application also provides a storage medium, namely, a computer storage medium, specifically a computer-readable storage medium, including, for example, a memory 3 storing a computer program. The computer program can be executed by a processor 2 to perform the steps of the aforementioned method. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface storage, optical disk, CD-ROM, or the like.

[0096] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, ROM, RAM, disks or optical disks, etc. Various media that can store program codes.

[0097] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling an electronic device (which can be a personal computer, server, network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program code, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0098] In an exemplary embodiment, the present application further provides a computer program product, including a computer program, which can be executed by the processor 2 to complete the steps of the aforementioned method.

[0099] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A data processing system, characterized in that: The invention comprises a computer device and a quantum computing device cluster, wherein the computer device runs an artificial intelligence model, the quantum computing device cluster comprises a plurality of quantum computing devices, and the computer device is connected to the quantum computing devices; The computer device is configured to determine a data processing task during the operation of the artificial intelligence model, select an optimal quantum computing device based on task information of the data processing task and the hardware status of each quantum computing device in the quantum computing device cluster, convert the data processing task into a quantum program that conforms to a standard format for quantum circuits, and send the quantum program to the optimal quantum computing device; The optimal quantum computing device is used to execute the quantum program to obtain quantum computing results, convert the quantum computing results into a data format supported by the artificial intelligence model, and return them to the computer device.

2. The data processing system according to claim 1, wherein: The process of the optimal quantum computing device converting the quantum computing result into a data format supported by the artificial intelligence model and returning the result to the computer device includes: Determining the structural characteristics of the quantum computing result; wherein the structural characteristics include any one or a combination of any two of a superposition state, a tensor state, and a universal state; According to the structural characteristics of the quantum computing result, a corresponding compression strategy is adopted to compress the quantum computing result to obtain a compressed quantum computing result, and the compressed quantum computing result is converted into a data format supported by the artificial intelligence model and returned to the computer device.

3. The data processing system according to claim 2, wherein: The process of determining the structural characteristics of the quantum computing result by the optimal quantum computing device includes: Calculating a ground state probability standard deviation of the quantum calculation result, and if the ground state probability standard deviation is less than a first preset value, determining that the structural characteristic of the quantum calculation result is a superposition state; Performing singular value decomposition on a state matrix in the quantum computation result, and determining that the structural characteristic of the quantum computation result is a tensor state if a ratio of the amount of data corresponding to the first k singular values ​​to the amount of data in the state matrix is ​​greater than a second preset value; If the standard deviation of the ground state probability of the quantum calculation result is greater than or equal to the first preset value, and the proportion of the data volume corresponding to the first k singular values ​​to the data volume of the state matrix is ​​less than or equal to the second preset value, then the structural characteristics of the quantum calculation result are determined to be a universal state.

4. The data processing system according to claim 3, wherein: The optimal quantum computing device compresses the quantum computing result using a corresponding compression strategy according to the structural characteristics of the quantum computing result to obtain a compressed quantum computing result, and the process of converting the compressed quantum computing result into a data format supported by the artificial intelligence model and returning it to the computer device includes: If the quantum computing result is a superposition state, converting the differential data in the quantum computing result into a data format supported by the artificial intelligence model as a compressed quantum computing result and returning it to the computer device; If the quantum computing result is a tensor state, the data corresponding to the first k singular values ​​are converted into a data format supported by the artificial intelligence model as a compressed quantum computing result and returned to the computer device; If the quantum computing result is a universal state, the quantum computing result is divided into multiple shard data as compressed quantum computing results, and the multiple shard data are respectively converted into a data format supported by the artificial intelligence model and returned to the computer device in a streaming manner.

5. The data processing system according to claim 1, wherein: The process of selecting the best quantum computing device by the computer device according to the task information of the data processing task and the hardware status of each quantum computing device in the quantum computing device cluster includes: Determining, in the quantum computing device cluster, a candidate quantum computing device whose hardware information meets the task information of the data processing task; The candidate quantum computing devices having the best hardware status are selected as the optimal quantum computing device.

6. The data processing system according to claim 1, wherein: The computer device is further configured to: use a deep learning model to predict an expected execution time of the data processing task based on task information of the data processing task and hardware information of the optimal quantum computing device.

7. The data processing system according to claim 6, characterized in that: The computer device is also used to: construct a historical task dataset; wherein the historical task dataset is used to record task information of historical tasks, hardware information and execution time of quantum computing devices that perform the historical tasks; and train a deep learning model based on the historical task dataset.

8. The data processing system according to claim 6, wherein: The computer device is further configured to update the estimated completion time of the data processing task according to the real-time hardware status of the optimal quantum computing device during execution of the quantum program by the optimal quantum computing device.

9. The data processing system according to claim 1, wherein: The quantum computing device is used to: send authorization information to the computer device; The computer device is further configured to log in to the quantum computing device based on the authorization information so as to establish a connection with the quantum computing device.

10. The data processing system according to claim 1, wherein: The process of the computer device sending the quantum program to the optimal quantum computing device includes: The computer device encrypts the quantum program using a preset encryption method and sends the encrypted program to the optimal quantum computing device; Accordingly, the process of the optimal quantum computing device converting the quantum computing result into a data format supported by the artificial intelligence model and returning it to the computer device includes: The optimal quantum computing device converts the quantum computing result into a data format supported by the artificial intelligence model, and encrypts it using a preset encryption method and returns it to the computer device.

11. The data processing system according to claim 1, wherein: The computer device is further used to: analyze received quantum computing results using the artificial intelligence model, and / or locate anomalies in an error log.

12. The data processing system according to any one of claims 1 to 11, characterized in that: The computer device includes an input interface and an output interface; The computer device is further configured to: obtain the data processing task through the input interface, and display the execution result of the quantum program and / or the quantum calculation result through the output interface.

13. A data processing method, characterized in that: Applied to a computer device in a data processing system according to any one of claims 1 to 12, the method comprising: Determining a data processing task during the operation of the artificial intelligence model, and selecting an optimal quantum computing device based on task information of the data processing task and the hardware status of each quantum computing device in the quantum device cluster; Converting the data processing task into a quantum program that conforms to a standard format for quantum circuits, and sending the quantum program to the optimal quantum computing device so that the optimal quantum computing device executes the quantum program to obtain a quantum computing result, and converts the quantum computing result into a data format supported by the artificial intelligence model; Receive a quantum computing result returned by the optimal quantum computing device.

14. The data processing method according to claim 13, characterized in that: The selecting the best quantum computing device according to the task information of the data processing task and the hardware status of each quantum computing device in the quantum device cluster includes: Determining, in the quantum computing device cluster, a candidate quantum computing device whose hardware information meets the task information of the data processing task; The candidate quantum computing devices having the best hardware status are selected as the optimal quantum computing device.

15. The data processing method according to claim 13, characterized in that: Also includes: A deep learning model is used to predict an expected execution time of the data processing task based on task information of the data processing task and hardware information of the optimal quantum computing device.

16. The data processing method according to claim 15, characterized in that: Also includes: Constructing a historical task dataset; wherein the historical task dataset is used to record task information of the historical tasks, hardware information of the quantum computing device that executed the historical tasks, and execution time; A deep learning model is trained based on the historical task dataset.

17. The data processing method according to claim 15, characterized in that: Also includes: During the process of executing the quantum program on the optimal quantum computing device, the estimated completion time of the data processing task is updated according to the real-time hardware status of the optimal quantum computing device.

18. The data processing method according to claim 13, characterized in that: Also includes: Utilize the artificial intelligence model to analyze received quantum computing results, and / or locate anomalies in error logs.

19. A data processing method, characterized in that: A quantum computing device applied to a data processing system according to any one of claims 1 to 12, wherein the method comprises: receiving quantum programs sent by computer devices; Executing the quantum program to obtain a quantum computing result; The quantum computing result is converted into a data format supported by the artificial intelligence model and returned to the computer device.

20. The data processing method according to claim 19, characterized in that: Converting the quantum computing result into a data format supported by the artificial intelligence model and returning it to the computer device includes: Determining the structural characteristics of the quantum computing result; wherein the structural characteristics include any one or a combination of any two of a superposition state, a tensor state, and a universal state; According to the structural characteristics of the quantum computing result, a corresponding compression strategy is adopted to compress the quantum computing result to obtain a compressed quantum computing result, and the compressed quantum computing result is converted into a data format supported by the artificial intelligence model and returned to the computer device.

21. The data processing method according to claim 20, characterized in that: Determining structural characteristics of the quantum computing result, including: Calculating a ground state probability standard deviation of the quantum calculation result, and if the ground state probability standard deviation is less than a first preset value, determining that the structural characteristic of the quantum calculation result is a superposition state; Performing singular value decomposition on a state matrix in the quantum computation result, and determining that the structural characteristic of the quantum computation result is a tensor state if a ratio of the amount of data corresponding to the first k singular values ​​to the amount of data in the state matrix is ​​greater than a second preset value; If the standard deviation of the ground state probability of the quantum calculation result is greater than or equal to the first preset value, and the proportion of the data volume corresponding to the first k singular values ​​to the data volume of the state matrix is ​​less than or equal to the second preset value, then the structural characteristics of the quantum calculation result are determined to be a universal state.

22. The data processing method according to claim 20, characterized in that: The method further comprises: performing a compression operation on the quantum computing result using a corresponding compression strategy according to the structural characteristics of the quantum computing result to obtain a compressed quantum computing result, converting the compressed quantum computing result into a data format supported by the artificial intelligence model and returning the data to the computer device, including: If the quantum computing result is a superposition state, converting the differential data in the quantum computing result into a data format supported by the artificial intelligence model as a compressed quantum computing result and returning it to the computer device; If the quantum computing result is a tensor state, the data corresponding to the first k singular values ​​are converted into a data format supported by the artificial intelligence model as a compressed quantum computing result and returned to the computer device; If the quantum computing result is a universal state, the quantum computing result is divided into multiple shard data as compressed quantum computing results, and the multiple shard data are respectively converted into a data format supported by the artificial intelligence model and returned to the computer device in a streaming manner.

23. A computer device, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of the data processing method according to any one of claims 13 to 18 when executing the computer program.

24. A quantum computing device, characterized in that include: memory for storing computer programs; A processor, configured to implement the steps of the data processing method according to any one of claims 19 to 22 when executing the computer program.

25. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed, implements the steps of the data processing method according to any one of claims 13 to 22.

26. A computer program product, characterized in that The method comprises a computer program, which implements the steps of the data processing method according to any one of claims 13 to 22 when the computer program is executed.

Images