Data processing systems, methods, apparatus, storage media, and computer program products

Abstract

The application discloses a data processing system, method, equipment, storage medium and computer program product, relates to the technical field of cooperative computing of artificial intelligence and quantum computing, and the system comprises a computer equipment and a quantum computing equipment cluster, an artificial intelligence model is run in the computer equipment, and the computer equipment is connected with the quantum computing equipment; the computer equipment is used for determining a data processing task in the running process of the artificial intelligence model, selecting an optimal quantum computing equipment, 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 equipment; the optimal quantum computing equipment is used for executing the quantum program to obtain a quantum computing result, and converting the quantum computing result into a data format supported by the artificial intelligence model and returning the quantum computing result to the computer equipment. The application realizes efficient interaction between the quantum computing equipment and the artificial intelligence model run in the computer equipment.

Description

Technical Field

[0001] This application relates to the field of collaborative computing technology of 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 computer and quantum computing device, which is used to realize the efficient use of quantum computing resources and task scheduling by classical artificial intelligence models. Background Technology

[0002] Artificial intelligence models have demonstrated powerful capabilities in processing complex data and pattern recognition. However, as the volume of data and the complexity of computational tasks increase, the performance of AI models is limited by the computational power of classical computers, making it difficult to achieve optimal performance in certain tasks. Meanwhile, quantum computing devices, with their qubit superposition, entanglement, and interference properties, can theoretically break through the computational limits of classical computers, offering new possibilities for solving complex computational problems.

[0003] However, numerous obstacles exist in the interaction between quantum computing devices and classical computers in related technologies. On one hand, the operating environment and methods of quantum computing devices differ significantly from those of classical computers, making it difficult for artificial intelligence models on classical computers to directly access quantum computing resources, posing challenges to data transmission and task scheduling. On the other hand, the complex data format of quantum computing results makes efficient transmission and parsing difficult using traditional data transmission and processing frameworks, requiring data formats and transmission mechanisms compatible with the characteristics of quantum computing. Furthermore, the lack of contextual analysis of historical task data in related technologies prevents dynamic optimization of 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 artificial intelligence models running on classical computer devices can make full use of quantum computing resources, is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a data processing system, method, apparatus, storage medium, and computer program product that enables efficient interaction between quantum computing devices and computer devices, allowing artificial intelligence models running in computer devices to fully utilize quantum computing resources.

[0006] To achieve the above objectives, this application provides a data processing system, including a computer device and a quantum computing device cluster, wherein an artificial intelligence model runs in the computer device, 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 used to determine data processing tasks during the operation of the artificial intelligence model, select 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, convert the data processing task into a quantum program that conforms to the quantum circuit standard format, and send the quantum program to the best quantum computing device.

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

[0009] To achieve the above objectives, this application provides a data processing method applied to a computer device in the data processing system described above, the method comprising:

[0010] During the operation of the artificial intelligence model, data processing tasks are determined, and the best quantum computing device is selected based on the task information of the data processing tasks and the hardware status of each quantum computing device in the sub-device cluster.

[0011] The data processing task is converted into a quantum program conforming to the standard format of quantum circuits, and the quantum program is sent to the optimal quantum computing device so that the optimal quantum computing device can execute the quantum program to obtain quantum computing results, and convert the quantum computing results into a data format supported by the artificial intelligence model;

[0012] Receive the quantum computing results returned by the optimal quantum computing device.

[0013] To achieve the above objectives, this application provides a data processing method applied to a quantum computing device in the data processing system described above, the method comprising:

[0014] Receive quantum programs sent by computer equipment;

[0015] The quantum program is executed to obtain the quantum computation result;

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

[0017] To achieve the above objectives, this application provides a computer device, comprising:

[0018] Memory, used to store computer programs;

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

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

[0021] Memory, used to store 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, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the data processing method described above.

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

[0025] The data processing system provided in this application achieves seamless integration of classical and quantum computing devices through a tight connection between computer equipment and a cluster of quantum computing devices. The computer equipment 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 provides strong support for intelligent task management by embedding historical tasks and hardware data into the context. 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, not only solving the problem of difficult access to quantum computing resources in related technologies but also improving resource utilization efficiency and ensuring that tasks can be executed efficiently on the most suitable quantum computing device. Furthermore, the computer equipment converts the data processing tasks into quantum programs conforming to the standard format of quantum circuits and sends 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, realizing the utilization of quantum resources by the computer equipment. Simultaneously, 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 equipment, allowing the artificial intelligence model to directly use the quantum computing results and 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 achieve the same technical effects as described above.

[0026] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. The drawings are used to provide a further understanding of this disclosure and constitute a part of the specification. They are used together with the following detailed description to explain this disclosure, but do not constitute a limitation of this disclosure. In the drawings:

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

[0029] Figure 2 This is a flowchart illustrating a data processing method according to an exemplary embodiment;

[0030] Figure 3 A flowchart illustrating another data processing method according to an exemplary embodiment;

[0031] Figure 4 This is a structural diagram of a computer device or quantum computing device according to an exemplary embodiment. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Furthermore, in the embodiments of this 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, and 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 practical implementation, the computer device 10 and the quantum computing devices are connected via a network, such as Ethernet or fiber optic networks, 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 consists of multiple quantum computing devices, each with hardware characteristics such as different numbers of qubits and quantum gate fidelity, capable of performing quantum computing tasks. This connection method allows the computer device 10 to utilize the computing resources of the quantum computing devices, enabling collaborative work between quantum and classical computing.

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

[0036] The authorization information may include the quantum computing device's identity and access control keys, used to verify the legitimacy of the quantum computing device and the access rights of the computer device. In practice, 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). Upon receiving the authorization information, the computer device 10 verifies it. If the verification is successful, it logs into the quantum computing device using the corresponding login credentials, establishing a secure communication connection. This process ensures that only authorized computer device 10 can access the quantum computing device, thus improving system security.

[0037] The computer device 10 is used to determine data processing tasks during the operation of the artificial intelligence model, select 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 20, convert the data processing task into a quantum program that conforms to the quantum circuit standard format, and send the quantum program to the best quantum computing device.

[0038] In practical implementation, when running an artificial intelligence model, computer device 10 determines the data processing tasks to be executed based on the model's requirements and the characteristics of the input data. Then, 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 20, the optimal quantum computing device is selected. The task information may include the required quantum circuit depth, number of qubits, number of quantum gates, etc., and the hardware status may include information such as availability and error rate.

[0039] As a feasible implementation, the process by which the computer device 10 selects 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 candidate quantum computing devices in the quantum computing device cluster whose hardware information matches the task information of the data processing task; and selecting the candidate quantum computing device with the best hardware status as the best quantum computing device.

[0040] In practice, computer device 10, based on the task information of the data processing task, determines candidate quantum computing devices in the quantum computing device cluster 20 whose hardware information meets the requirements. This hardware information may include the number of qubits, the number of quantum gates, quantum gate fidelity, coherence time, etc. Simultaneously, computer device 10 acquires the hardware status of each quantum computing device in the quantum computing device cluster 20 in real time and selects the candidate quantum computing device with the optimal hardware status as the best quantum computing device through intelligent scheduling algorithms, such as rule-based algorithms or machine learning algorithms.

[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, which can be understood and executed by the quantum computing device. A standard quantum circuit format such as OpenQASM (Open Quantum Assembly Language) is an example. The conversion process involves decomposing 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 sends the quantum program to the optimal quantum computing device via a network. This process achieves intelligent allocation of data processing tasks and efficient transmission of quantum programs, improving the overall performance of the system.

[0042] In a preferred embodiment, the process by which the computer device 10 sends 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 it to the optimal quantum computing device;

[0043] In practical implementation, computer device 10 can use the AES (Advanced Encryption Standard)-256 encryption algorithm to encrypt the quantum program, ensuring the security of data transmission and preventing data from being stolen or tampered with 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, uses this key to encrypt the quantum program, and then sends the encrypted quantum program and key (through a secure key exchange mechanism) to the optimal quantum computing device. Upon receiving the encrypted quantum program, the optimal quantum computing device decrypts it using the same key to obtain the 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, improving the overall security of the system.

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

[0045] In practice, after receiving a quantum program, the optimal quantum computing device loads it into a quantum processor for execution. The quantum processor manipulates the qubits according to the quantum gate operation instructions in the quantum program, completing the quantum computing task and obtaining the quantum computing results. These results may include the probability distribution of quantum states, measurement results, etc., and are typically stored in the quantum computing device's memory as binary data. Then, the optimal quantum computing device converts the quantum computing results into a data format supported by the artificial intelligence model, such as JSON (JavaScript Object Notation), and sends it back to the computer device 10 via the network. The JSON data can contain metadata, such as the number of qubits, measurement basis, compression method, etc. This process achieves efficient transmission and conversion of quantum computing results, enabling further processing and analysis by the artificial intelligence model.

[0046] In a preferred embodiment, the process by which the optimal quantum computing device converts the quantum computing result into a data format supported by the artificial intelligence model and returns 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 of the superposition state, tensor state, and universal state; performing a compression operation on the quantum computing result according to the structural characteristics of the quantum computing result using a corresponding compression strategy 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.

[0047] It should be noted that quantum computing results usually have complex structures. For example, a superposition state means that a qubit is in a superposition of multiple states, a tensor state means that a quantum state can be decomposed into a tensor product of multiple low-dimensional quantum states, and a universal state is other states that cannot be easily classified.

[0048] In practical implementation, after obtaining the quantum computing results, the optimal quantum computing device first determines its structural characteristics through analysis algorithms. 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, retaining only the subset data corresponding to the main singular values. For general states, a piecewise streaming transmission method can be used, dividing the data into multiple small blocks and transmitting them one by one, avoiding the transmission of large amounts of data at once. This process not only reduces the amount of data transmitted and improves transmission efficiency, but also ensures that the quantum computing results can be effectively utilized by artificial intelligence models.

[0049] As a feasible implementation method, the process of determining the structural characteristics of the quantum computing result by the optimal quantum computing device includes: calculating the standard deviation of the ground state probability of the quantum computing result; if the standard deviation of the ground state probability is less than a first preset value, then the structural characteristic of the quantum computing result is determined to be a superposition state; performing singular value decomposition on the state matrix in the quantum computing result; if the proportion of the data corresponding to the first k singular values ​​to the data volume of the state matrix is ​​greater than a second preset value, then the structural characteristic of the quantum computing result is determined to be 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 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 characteristic of the quantum computing result is determined to be a universal state.

[0050] The standard deviation of the ground state probability is an indicator of the degree of superposition of quantum states; the smaller the standard deviation, the closer the quantum state is to a complete superposition state. The first preset value can be set according to actual needs, for example, 5%. Singular value decomposition (SVD) is a matrix factorization method that decomposes the state matrix into the product of singular values ​​and singular vectors. The proportion of data corresponding to the first k singular values ​​reflects the tensor properties of the quantum state; 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%. Optimal quantum computing equipment uses these mathematical methods and preset thresholds to accurately determine the structural characteristics of quantum computing results, 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 compresses the quantum computing result using a corresponding compression strategy based on the structural characteristics of the quantum computing result to obtain a compressed quantum computing result. 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, then the difference data in the quantum computing result is used as the compressed quantum computing result and converted 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, then the data corresponding to the first k singular values ​​is used as the compressed quantum computing result and converted 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, then the quantum computing result is divided into multiple data segments as the compressed quantum computing result, and the multiple data segments are converted into data formats supported by the artificial intelligence model and returned to the computer device 10 using a streaming transmission method.

[0052] In practical implementation, for superposition states, due to their uniform probability distribution, only the difference between the data and the average value needs to be transmitted, which significantly reduces the data volume. For example, for a 20-qubit system, the probability distribution data of a fully superposition state is approximately 8MB; after differential compression, the data volume can be reduced to about 1MB. For tensor states, retaining the subset data corresponding to the main singular values ​​through singular value decomposition effectively reduces the dimensionality and complexity of the data. For general states, due to their complex structure, direct compression is difficult; therefore, a segmented streaming approach is used, dividing the data into multiple small blocks and transmitting them one by one to avoid transmitting large amounts of data at once, reducing memory usage and transmission latency. It is evident that each compression strategy incorporates the characteristics of quantum computing results, minimizing data transmission volume and improving system performance and efficiency while ensuring data integrity and accuracy.

[0053] As a preferred embodiment, the process by which the optimal quantum computing device converts the quantum computing result into a data format supported by the artificial intelligence model and returns 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 practical implementation, the optimal quantum computing device, after converting the quantum computing results into a data format, also encrypts them using the AES-256 encryption algorithm. The encrypted data is sent back to computer device 10 via the network. Upon receiving the encrypted data, computer device 10 decrypts it using the same key, obtaining the quantum computing results that can be directly used for artificial intelligence model analysis. This encrypted transmission process not only protects the confidentiality of the quantum computing results but also prevents the data from being tampered with or leaked during transmission, ensuring data integrity and security and improving system reliability.

[0055] The data processing system provided in this application embodiment achieves seamless integration of classical computer equipment and quantum computing equipment through the tight connection between computer device 10 and quantum computing device cluster 20. Computer device 10 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 provides strong support for intelligent task management by embedding historical tasks and hardware data into the context. 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, not only solving the problem of difficult access to quantum computing resources in related technologies but also improving resource utilization efficiency and ensuring that tasks can be executed efficiently on the most suitable quantum computing device. Furthermore, computer device 10 converts the data processing tasks into quantum programs conforming to the standard format of quantum circuits and sends 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, realizing the utilization of quantum resources by computer device 10. Meanwhile, 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 10, so that the artificial intelligence model can directly use the quantum computing results, thereby improving the processing efficiency of the artificial intelligence model.

[0056] Based on the above embodiments, as a preferred embodiment, the computer device 10 is further configured to: use a deep learning model 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.

[0057] In practical implementation, the artificial intelligence model in computer device 10 may include a deep learning model, such as a neural network. This model, by learning from historical task data, can predict the execution time of a task based on its current task information (such as task type, data size, accuracy requirements, etc.) and the hardware information of the optimal quantum computing device (such as the number of qubits, quantum gate fidelity, error rate, etc.). 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 task's complexity and the performance of the quantum computing device. This predictive function provides users with a time reference, helping to rationally allocate tasks and resources, and improve system efficiency and user experience. This function, through the predictive capabilities of the deep learning model, provides users with an estimate of task execution time, which helps optimize resource allocation and task scheduling.

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

[0059] In practice, during operation, the computer device 10 collects relevant information for each task, including task type, data size, accuracy requirements, and other task information, as well as hardware information such as the number of qubits, quantum gate fidelity, and error rate of the quantum computing device executing the task, and 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 for different types of tasks on quantum computing devices with different hardware configurations, thus providing a more accurate time estimate during prediction.

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

[0061] In practice, the computer device 10 monitors the hardware status of the optimal quantum computing device in real time during task execution, including queue length, error rate, and availability. If changes in the hardware status are detected, such as an increase in queue length leading to longer task waiting times, or an increase in the error rate requiring the task to be re-executed, the computer device 10 dynamically updates the estimated task completion time based on this real-time information. For example, if the task was originally expected to take 10 minutes to complete, the estimated completion time might be updated to 12 minutes due to an increase in queue length. This dynamic update function makes the system's predictions closer to reality, promptly reflecting changes during task execution and improving the system's real-time performance and reliability.

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

[0063] In practical implementation, the artificial intelligence model in computer device 10 can not only predict task execution time but also analyze quantum computing results. For example, for the results of quantum chemical simulations, the model can analyze the probability distribution of quantum states and extract characteristic information related to chemical reactions, providing valuable references for chemical research. Simultaneously, the model can also locate anomalies in error logs. For instance, by analyzing error codes and contextual information in the logs, it can quickly pinpoint the location of program errors or hardware failures. For example, if frequent quantum gate operation errors appear in the logs, the model can determine that it may be a hardware failure of the quantum computing device and issue an alert promptly. This function improves the system's intelligence level, enabling automatic problem detection and handling, reducing manual intervention, and improving the system's stability and reliability.

[0064] Based on the above embodiments, as a preferred embodiment, the computer device 10 includes an input interface and an output interface; the computer device 10 is further configured to: acquire 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 practical implementation, the input interface of computer device 10 can be a graphical user interface (GUI), a command-line interface (CLI), or a network interface, etc. Users can input data processing tasks through these interfaces, such as uploading data files to be processed and setting task parameters. The output interface of computer device 10 can be a display screen, a printer, or a network interface, etc., used to display the execution results of the quantum program and the quantum computing results to the user. The execution results can include whether the quantum program executed successfully or failed, and the reason for the failure. For example, the quantum computing results can be displayed intuitively on the screen in the form of charts, tables, etc., or the results can be saved as files and sent to the user via the network. This function improves the interactivity of the system, enabling users to easily interact with the system, obtain the execution status and results of tasks, and improve the user experience.

[0066] This application discloses a data processing method applied to a computer device 10. See also... Figure 2 A flowchart illustrating a data processing method according to an exemplary embodiment, such as... Figure 2 As shown, it includes:

[0067] S101: During the operation of the artificial intelligence model, determine the data processing task, and select 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 device cluster.

[0068] S102: Convert the data processing task into a quantum program that conforms to the standard format of quantum circuits, and send the quantum program to the optimal quantum computing device so that the optimal quantum computing device can execute the quantum program to obtain quantum computing results, and convert the quantum computing results into a data format supported by the artificial intelligence model;

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

[0070] This embodiment achieves efficient interaction between the quantum computing devices 10 and the quantum computing device cluster 20 through a tight connection, enabling the artificial intelligence model to fully utilize quantum computing resources. The computer device 10 can intelligently determine data processing tasks based on the operational needs of the artificial intelligence model and select the optimal quantum computing device based on task information and the hardware status of the quantum computing devices. During this process, the system embeds historical tasks and hardware data into the context, providing strong support for intelligent task management. This context embedding mechanism allows the system to dynamically optimize task scheduling based on the execution status of historical tasks and real-time hardware status, not only solving the problem of difficult access to quantum computing resources in related technologies but also improving resource utilization efficiency and ensuring that tasks can be executed efficiently on the most suitable quantum computing device. Furthermore, the computer device 10 converts the data processing tasks into quantum programs conforming to the standard format of quantum circuits and sends the quantum programs to the optimal quantum computing device. This standardized quantum program conversion mechanism enables the data processing tasks to be executed universally on different quantum computing devices, realizing the utilization of quantum resources by the computer device 10.

[0071] Based on the above embodiments, as a preferred implementation, the step of 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 candidate quantum computing devices in the quantum computing device cluster whose hardware information matches the task information of the data processing task; and selecting the candidate quantum computing devices with the best hardware status as the best quantum computing device.

[0072] Based on the above embodiments, as a preferred implementation, it further includes: using a deep learning model 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.

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

[0074] Based on the above embodiments, as a preferred implementation, the method further includes: during the execution of the quantum program by the optimal quantum computing device, updating the estimated completion time of the data processing task according to the real-time hardware status of the optimal quantum computing device.

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

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

[0077] This application discloses a data processing method applied to a quantum computing device. See also... Figure 3 A flowchart illustrating another data processing method according to an exemplary embodiment, such as Figure 3 As shown, it includes:

[0078] S201: Receive quantum programs sent by computer equipment;

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

[0080] S203: Convert the quantum computing results 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 and the quantum computing device cluster 20 through a tight connection between the computer device 10 and the quantum computing device cluster 20, enabling the artificial intelligence model to fully utilize quantum computing resources. The quantum computing results obtained by the quantum computing devices after executing quantum programs 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 and improving the processing efficiency of the artificial intelligence model.

[0082] Based on the above embodiments, as a preferred implementation, 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 of the superposition state, tensor state, and universal state; performing a compression operation on the quantum computing result according to the structural characteristics of the quantum computing result using a corresponding compression strategy 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 embodiments, as a preferred implementation, determining the structural characteristics of the quantum computing result includes: calculating the standard deviation of the ground state probability of the quantum computing result; if the standard deviation of the ground state probability is less than a first preset value, then the structural characteristic of the quantum computing result is determined to be a superposition state; performing singular value decomposition on the state matrix in the quantum computing result; if the proportion of the data corresponding to the first k singular values ​​to the data volume of the state matrix is ​​greater than a second preset value, then the structural characteristic of the quantum computing result is determined to be 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 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 characteristic of the quantum computing result is determined to be a universal state.

[0084] Based on the above embodiments, as a preferred implementation, the quantum computing results are compressed using a corresponding compression strategy according to the structural characteristics of the quantum computing results to obtain compressed quantum computing results. The compressed quantum computing results are then converted into a data format supported by the artificial intelligence model and returned to the computer device 10. This includes: if the quantum computing result is a superposition state, then the difference data in the quantum computing result is used as the compressed quantum computing result and converted 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, then the data corresponding to the first k singular values ​​is used as the compressed quantum computing result and converted 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, then the quantum computing result is divided into multiple data segments as compressed quantum computing results, and each of the multiple data segments is converted into a data format supported by the artificial intelligence model and returned to the computer device 10 using a streaming transmission method.

[0085] The specific implementation methods of each step in the above embodiments have been described in detail in the embodiments 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 embodiments of this application, the embodiments of this application also provide a computer device. Figure 4 This is a structural diagram of a computer device according to an exemplary embodiment, such as... Figure 4 As shown, the computer equipment includes:

[0087] Communication interface 1 enables information exchange with other devices, such as network devices;

[0088] Processor 2 is connected to communication interface 1 to enable information interaction with other devices. When running a computer program, it executes the data processing methods provided by one or more of the above-mentioned technical solutions on the computer device side. The computer program is stored in memory 3.

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

[0090] The memory 3 in this embodiment 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 memory 3 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but 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), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 3 described in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0092] The methods disclosed in the embodiments of this application can be applied to processor 2, or implemented by processor 2. Processor 2 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 2 or by instructions in the form of software. The processor 2 may 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 methods, steps and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested 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 modules may 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 aforementioned method in combination with its hardware.

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

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

[0095] In an exemplary embodiment, this application also provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, such as a memory 3 that stores a computer program, which can be executed by a processor 2 to complete the steps described in the aforementioned method. The computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM.

[0096] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0097] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0098] In an exemplary embodiment, this application also provides a computer program product, including a computer program that can be executed by a processor 2 to perform the steps described in the foregoing method.

[0099] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A data processing system, characterized in that, The system includes computer equipment and a cluster of quantum computing devices, wherein the computer equipment runs an artificial intelligence model, the cluster of quantum computing devices includes multiple quantum computing devices, and the computer equipment is connected to the quantum computing devices. The computer device is used to determine data processing tasks during the operation of the artificial intelligence model, select 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, convert the data processing task into a quantum program that conforms to the quantum circuit standard format, and send the quantum program to the best quantum computing device. The optimal quantum computing device is used to execute the quantum program to obtain quantum computing results, and to convert the quantum computing results into a data format supported by the artificial intelligence model and return it to the computer device. The process by which the optimal quantum computing device converts the quantum computing results into a data format supported by the artificial intelligence model and returns it to the computer device includes: Determine the structural properties of the quantum computing result; wherein the structural properties include any one or a combination of any of the superposition state, tensor state, and universal state; Based on the structural characteristics of the quantum computing results, a corresponding compression strategy is used to compress the quantum computing results to obtain compressed quantum computing results. The compressed quantum computing results are then converted into a data format supported by the artificial intelligence model and returned to the computer device. The process by which the optimal quantum computing device determines the structural properties of the quantum computing results includes: Calculate the standard deviation of the ground state probability of the quantum computing result. If the standard deviation of the ground state probability is less than a first preset value, then determine that the structural characteristic of the quantum computing result is a superposition state. Singular value decomposition is performed on the state matrix in the quantum computing result. 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, then the structural characteristic of the quantum computing result is determined to be 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 corresponding to the first k singular values ​​to the data of the state matrix is ​​less than or equal to the second preset value, then the structural characteristics of the quantum computing result are determined to be a universal state.

2. The data processing system according to claim 1, characterized in that, The optimal quantum computing device compresses the quantum computing results using a corresponding compression strategy based on the structural characteristics of the quantum computing results to obtain compressed quantum computing results. The process of converting the compressed quantum computing results 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, then the differential data in the quantum computing result is converted into a data format supported by the artificial intelligence model and returned to the computer device as a compressed quantum computing result; If the quantum computing result is a tensor state, then the data corresponding to the first k singular values ​​are converted into a data format supported by the artificial intelligence model and returned to the computer device as a compressed quantum computing result; If the quantum computing result is a general state, then the quantum computing result is divided into multiple data fragments as compressed quantum computing results, and the multiple data fragments are converted into data formats supported by the artificial intelligence model and returned to the computer device in a streaming manner.

3. The data processing system according to claim 1, characterized in that, The process by which the computer device selects the optimal 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: In the quantum computing device cluster, candidate quantum computing devices whose hardware information matches the task information of the data processing task are identified. The candidate quantum computing device with the best hardware configuration is selected as the best quantum computing device.

4. The data processing system according to claim 1, characterized in that, The computer device is also used to: predict the estimated 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 using a deep learning model.

5. The data processing system according to claim 4, 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 of quantum computing devices that execute the historical tasks, and execution time; and to train a deep learning model based on the historical task dataset.

6. The data processing system according to claim 4, characterized in that, The computer device is also used to: update the estimated completion time of the data processing task based on the real-time hardware status of the optimal quantum computing device during the execution of the quantum program on the optimal quantum computing device.

7. The data processing system according to claim 1, characterized in that, The quantum computing device is used to: send authorization information to the computer device; The computer device is also used to: log in to the quantum computing device based on the authorization information in order to establish a connection with the quantum computing device.

8. The data processing system according to claim 1, characterized in that, The process by which the computer device sends the quantum program to the optimal quantum computing device includes: The computer device uses a preset encryption method to encrypt and send the quantum program to the optimal quantum computing device; Accordingly, the process by which the optimal quantum computing device converts the quantum computing results into a data format supported by the artificial intelligence model and returns it to the computer device includes: The optimal quantum computing device converts the quantum computing results into a data format supported by the artificial intelligence model, and encrypts them using a preset encryption method before returning them to the computer device.

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

10. The data processing system according to any one of claims 1 to 9, characterized in that, The computer device includes an input interface and an output interface; The computer device is also used to: acquire 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.

11. A data processing method, characterized in that, The method, applied to a computer device in a data processing system as described in any one of claims 1 to 10, comprises: During the operation of the artificial intelligence model, data processing tasks are determined, and the best quantum computing device is selected based on the task information of the data processing tasks and the hardware status of each quantum computing device in the quantum device cluster. The data processing task is converted into a quantum program conforming to the standard format of quantum circuits, and the quantum program is sent to the optimal quantum computing device so that the optimal quantum computing device can execute the quantum program to obtain quantum computing results, and convert the quantum computing results into a data format supported by the artificial intelligence model; Receive the quantum computing results returned by the optimal quantum computing device.

12. The data processing method according to claim 11, characterized in that, The step of selecting the optimal 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 device cluster includes: In the quantum computing device cluster, candidate quantum computing devices whose hardware information matches the task information of the data processing task are identified. The candidate quantum computing device with the best hardware configuration is selected as the best quantum computing device.

13. The data processing method according to claim 11, characterized in that, Also includes: The expected execution time of the data processing task is predicted using a deep learning model based on the task information of the data processing task and the hardware information of the optimal quantum computing device.

14. The data processing method according to claim 13, characterized in that, Also includes: Construct 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 devices that execute the historical tasks, and the execution time; A deep learning model was trained based on the aforementioned historical task dataset.

15. The data processing method according to claim 13, characterized in that, Also includes: During the execution of the quantum program on the optimal quantum computing device, the estimated completion time of the data processing task is updated based on the real-time hardware status of the optimal quantum computing device.

16. The data processing method according to claim 11, characterized in that, Also includes: The received quantum computing results are analyzed using the artificial intelligence model, and / or anomalies are located in the error log.

17. A data processing method, characterized in that, The method, applied to a quantum computing device in a data processing system as described in any one of claims 1 to 10, comprises: Receive quantum programs sent by computer equipment; The quantum program is executed to obtain the quantum computation result; The quantum computing results are converted into a data format supported by the artificial intelligence model and returned to the computer device. The process of converting the quantum computing results into a data format supported by the artificial intelligence model and returning it to the computer device includes: Determine the structural properties of the quantum computing result; wherein the structural properties include any one or a combination of any of the superposition state, tensor state, and universal state; Based on the structural characteristics of the quantum computing results, a corresponding compression strategy is used to compress the quantum computing results to obtain compressed quantum computing results. The compressed quantum computing results are then converted into a data format supported by the artificial intelligence model and returned to the computer device. Determining the structural properties of the quantum computing results includes: Calculate the standard deviation of the ground state probability of the quantum computing result. If the standard deviation of the ground state probability is less than a first preset value, then determine that the structural characteristic of the quantum computing result is a superposition state. Singular value decomposition is performed on the state matrix in the quantum computing result. 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, then the structural characteristic of the quantum computing result is determined to be 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 corresponding to the first k singular values ​​to the data of the state matrix is ​​less than or equal to the second preset value, then the structural characteristics of the quantum computing result are determined to be a universal state.

18. The data processing method according to claim 17, characterized in that, Based on the structural characteristics of the quantum computing results, a corresponding compression strategy is used to compress the quantum computing results to obtain compressed quantum computing results. The compressed quantum computing results are then converted into a data format supported by the artificial intelligence model and returned to the computer device, including: If the quantum computing result is a superposition state, then the differential data in the quantum computing result is converted into a data format supported by the artificial intelligence model and returned to the computer device as a compressed quantum computing result; If the quantum computing result is a tensor state, then the data corresponding to the first k singular values ​​are converted into a data format supported by the artificial intelligence model and returned to the computer device as a compressed quantum computing result; If the quantum computing result is a general state, then the quantum computing result is divided into multiple data fragments as compressed quantum computing results, and the multiple data fragments are converted into data formats supported by the artificial intelligence model and returned to the computer device in a streaming manner.

19. A computer device, characterized in that, include: Memory, used to store computer programs; A processor, configured to perform the steps of the data processing method as described in any one of claims 11 to 16 when executing the computer program.

20. A quantum computing device, characterized in that, include: Memory, used to store computer programs; A processor, configured to perform the steps of the data processing method as described in claim 17 or 18 when executing the computer program.

21. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the steps of the data processing method as described in any one of claims 11 to 18.

22. A computer program product, characterized in that, It includes a computer program that, when executed, implements the steps of the data processing method as described in any one of claims 11 to 18.

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