A quantum computing data processing method, apparatus, and storage medium

Abstract

The application discloses a quantum computing data processing method and device and a storage medium, comprising: collecting real-time data of an application scene by a terminal device, and transmitting the real-time data to an edge processing node cluster; using an FPGA acceleration module integrated in the edge processing node cluster to perform a processing operation on the real-time data to generate a quantum data packet, wherein the processing operation comprises preprocessing, quantumization coding and data routing, and the edge processing node cluster sends the quantum data packet and a corresponding task request to a quantum computing center; constructing a quantum service blockchain network for analyzing and identifying the task request, and dynamically calling, combining and deploying the corresponding quantum service; constructing a quantum computing model, using the quantum service to realize optimized calculation on the task request; and transmitting the optimized calculation result generated by the quantum computing center to the edge processing node cluster through a quantum encryption transmission channel, and driving the terminal device to execute by the edge processing node cluster.

Description

Technical Field

[0001] This application relates to the field of quantum computing technology, and in particular to a quantum computing data processing method, apparatus, and storage medium. Background Technology

[0002] With the rapid development of IoT technology, the number of edge devices has surged, and the demand for real-time data processing has become increasingly urgent. This is especially true in scenarios such as Industrial IoT, intelligent transportation, and emergency response, where higher requirements are placed on the real-time performance, reliability, and energy efficiency of data processing. Traditional data processing architectures typically rely on cloud centers or individual edge computing nodes for processing, resulting in problems such as high response latency, uneven resource utilization, and difficulty in handling highly complex optimization tasks.

[0003] In recent years, quantum computing, as an emerging computing paradigm, has shown great potential in handling complex problems such as combinatorial optimization and machine learning. However, effectively combining quantum computing with IoT edge devices still faces many challenges: on the one hand, quantum computing resources are usually concentrated in remote data centers, resulting in significant communication latency between quantum computing and edge devices, making it difficult to meet real-time requirements; on the other hand, existing systems lack dynamic task scheduling and resource optimization mechanisms, leading to insufficient synergy between quantum computing power and edge real-time processing capabilities, resulting in low overall system energy efficiency.

[0004] Some research has attempted to combine quantum computing with edge computing, but most of these studies remain theoretical and lack physical system architecture and efficient hardware support. For example, existing edge nodes mostly use general-purpose processors for data processing, lacking dedicated hardware acceleration mechanisms for quantum coding and low-latency routing, resulting in high preprocessing latency and failing to fully utilize the efficiency of quantum computing.

[0005] Therefore, how to achieve deep collaboration between quantum computing and IoT edge devices, support dynamic task allocation and resource optimization, and have real-time data processing methods with ultra-low latency and high adaptability has become a pressing technical problem that needs to be solved.

[0006] The publication number is CN120781994A, and the title is "Quantum Computation Simulation Method and Related Apparatus for Polaritons." It includes: for each excited state to be calculated, generating parameterized quantum circuits based on the microscopic model used to describe the polariton, obtaining the target Hamiltonian of the polariton in the current excited state based on the initial Hamiltonian of the polariton in the ground state, and then adjusting the values ​​of each parameter at least once to obtain the target energy of the corresponding excited state. During each adjustment, the parameters of the quantum circuit are updated, and using the updated quantum circuit, a reference quantum state of the corresponding excited state is obtained. Based on the reference quantum state and the target Hamiltonian, a reference energy of the corresponding excited state is obtained. Finally, when the set convergence conditions are met, the reference energy is used as the target energy of the corresponding excited state.

[0007] Publication number CN112567396A, entitled "Calculating Excited-State Properties of a Molecular System Using a Hybrid Classical-Quantum Computing System," includes determining the ground-state wavefunction of a combination of quantum logic gates using a quantum processor and a memory. In one embodiment, the method includes forming a set of excitation operators. In another embodiment, the method includes forming a set of commutators from the set of excitation operators and Hamiltonian operators. In yet another embodiment, the method includes mapping the set of commutators to a set of qubit states corresponding to a set of qubits in a quantum processor. In one embodiment, the method includes evaluating the set of commutators using a quantum processor and a memory. In yet another embodiment, the method includes having a quantum readout circuit measure the excited-state energy of the commutators calculated from the set of commutators.

[0008] There are currently no effective solutions to the technical problems of high latency and the need to improve resource utilization and overall energy efficiency when quantum computing and IoT edge devices work together in the existing technologies. Summary of the Invention

[0009] The embodiments of this disclosure provide a quantum computing data processing method, apparatus, and storage medium to at least solve the technical problems of high latency and the need to improve resource utilization and overall energy efficiency when quantum computing and IoT edge devices work together in the prior art.

[0010] According to one aspect of the present disclosure, a quantum computing data processing method is provided, comprising: collecting real-time data of an application scenario through a terminal device and transmitting the real-time data to an edge processing node cluster; using an FPGA acceleration module integrated in the edge processing node cluster to process the real-time data to generate quantum data packets, wherein the processing operations include preprocessing, quantization encoding, and data routing, and the edge processing node cluster sending the quantum data packets and corresponding task requests to a quantum computing center; constructing a quantum service blockchain network for analyzing and identifying task requests, and dynamically calling, combining, and deploying corresponding quantum services, wherein the quantum services originate from at least one or more of an edge quantum service library, a cloud-based private quantum service library, or a cloud-based public quantum service library; constructing a quantum computing model and using quantum services to optimize the computation of the task requests; and transmitting the optimized computation results generated by the quantum computing center to the edge processing node cluster through a quantum encrypted transmission channel, and having the edge processing node cluster drive the terminal device to execute the computation.

[0011] According to another aspect of the present disclosure, a storage medium is also provided, the storage medium including a stored program, wherein, when the program is executed, a processor performs any of the methods described above.

[0012] According to another aspect of the present disclosure, a quantum computing data processing apparatus is also provided, comprising: a real-time data acquisition module, used to acquire real-time data of an application scenario through a terminal device and transmit the real-time data to an edge processing node cluster; a quantum data packet generation module, used to use an FPGA acceleration module integrated in the edge processing node cluster to process the real-time data and generate quantum data packets, wherein the processing operations include preprocessing, quantization encoding, and data routing, and the edge processing node cluster sends the quantum data packets and corresponding task requests to a quantum computing center; a quantum service blockchain network construction module, used to construct a quantum service blockchain network, used to analyze and identify task requests, and dynamically call, combine, and deploy corresponding quantum services, wherein the quantum services originate from at least one or more of an edge quantum service library, a cloud-based private quantum service library, or a cloud-based public quantum service library; a quantum computing model construction module, used to construct a quantum computing model and use quantum services to optimize the computation of task requests; and a driving module, used to send the optimized computation results generated by the quantum computing center to the edge processing node cluster through a quantum encrypted transmission channel, and the edge processing node cluster drives the terminal device to execute the computation.

[0013] According to another aspect of the present disclosure, a quantum computing data processing apparatus is also provided, comprising: a processor; and a memory connected to the processor, configured to provide the processor with instructions for processing the following steps: collecting real-time data of an application scenario through a terminal device and transmitting the real-time data to an edge processing node cluster; using an FPGA acceleration module integrated in the edge processing node cluster to process the real-time data and generate quantum data packets, wherein the processing operations include preprocessing, quantization encoding, and data routing, and the edge processing node cluster sends the quantum data packets and corresponding task requests to a quantum computing center; constructing a quantum service blockchain network for analyzing and identifying task requests, and dynamically calling, combining, and deploying corresponding quantum services, wherein the quantum services originate from at least one or more of an edge quantum service library, a cloud-based private quantum service library, or a cloud-based public quantum service library; constructing a quantum computing model and using quantum services to optimize the computation of the task requests; and transmitting the optimized computation results generated by the quantum computing center to the edge processing node cluster through a quantum encrypted transmission channel, and having the edge processing node cluster drive the terminal device to execute the computation.

[0014] In this embodiment, real-time data is first collected by the terminal device and transmitted to the edge processing node cluster. Preprocessing, quantum encoding, and data routing are performed using an FPGA acceleration module. The processed quantum data packet and task request are then sent to the quantum computing center. The quantum service blockchain network intelligently analyzes the task complexity and dynamically schedules appropriate quantum services. For high-complexity problems, a quantum computing model is constructed for optimized solutions, while simple tasks are handled by classical algorithms to save resources. Finally, the calculation results are securely distributed to the edge processing node cluster through a quantum encryption channel, driving the terminal device to execute actions. Simultaneously, the system achieves self-learning and optimization through a continuous feedback mechanism, forming a complete intelligent decision-making and execution closed loop.

[0015] Thus, through the above method, quantum data packets can be generated using an FPGA acceleration module, and appropriate quantum services can be dynamically analyzed and scheduled through a quantum service blockchain network to solve the processed quantum data packets. This enables deep collaboration between quantum computing and IoT edge devices while reducing data processing latency, improving computing resource utilization, and enhancing overall energy efficiency. This solves the technical challenges of high latency and the need to further improve resource utilization and overall energy efficiency when quantum computing and IoT edge devices work together. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this disclosure and form part of this application, illustrate exemplary embodiments of this disclosure and are used to explain this disclosure, but do not constitute an undue limitation of this disclosure. In the drawings:

[0017] Figure 1 This is a hardware structure block diagram of a computing device for implementing the method according to Embodiment 1 of this disclosure;

[0018] Figure 2 This is a schematic diagram of a quantum computing data processing system according to Embodiment 1 of this disclosure;

[0019] Figure 3 This is a flowchart illustrating the quantum computing data processing method according to Embodiment 1 of this disclosure;

[0020] Figure 4 This is a schematic diagram of the processing flow of the quantum computing data processing method according to Embodiment 1 of this disclosure;

[0021] Figure 5 This is a flowchart illustrating the process of the FPGA acceleration module for processing real-time data according to Embodiment 1 of this disclosure;

[0022] Figure 6 This is a schematic diagram of the optimized execution process of the quantum computing center according to Embodiment 1 of this disclosure;

[0023] Figure 7 This is a schematic diagram of a quantum computing data processing apparatus according to Embodiment 2 of this disclosure; and

[0024] Figure 8 This is a schematic diagram of a quantum computing data processing apparatus according to Embodiment 3 of this disclosure. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this disclosure.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Example 1

[0028] According to this embodiment, a method embodiment of a quantum computing data processing method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] The method embodiments provided in this example can be executed on mobile terminals, computer terminals, servers, or similar computing devices. Figure 1 A hardware block diagram of a computing device for implementing quantum computing data processing methods is shown. Figure 1As shown, a computing device may include one or more processors (processors may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs), a memory for storing data, a transmission device for communication functions, and an input / output interface. The memory, transmission device, and input / output interface are connected to the processor via a bus. In addition, it may also include a display, keyboard, and cursor control device connected to the input / output interface. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, a computing device may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0030] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element in a computing device. As involved in the embodiments of this disclosure, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0031] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the quantum computing data processing method in this embodiment of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the quantum computing data processing method of the aforementioned application. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the computing device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0032] The transmission device is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the computing device's communication provider. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0033] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows users to interact with the user interface of the computing device.

[0034] It should be noted here that, in some optional embodiments, the above... Figure 1 The computing device shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 1 This is only one instance of a specific particular instance, and is intended to illustrate the types of components that may exist in the aforementioned computing devices.

[0035] Figure 2 This is a schematic diagram of a system based on the quantum computing processing method described in this embodiment. (Refer to...) Figure 2 As shown, the system includes: terminal device 100, edge processing node cluster 200, and quantum computing center 300.

[0036] The terminal device 100 includes sensors and mobile devices distributed across application scenarios, used to collect real-time data and send the collected real-time data to the edge processing node cluster 200.

[0037] The edge processing node cluster 200 is used to preprocess, quantize, and route the real-time data collected by the terminal device 100, and send the processed quantum data packets and corresponding task requests to the quantum computing center 300.

[0038] The Quantum Computing Center 300 is used to intelligently identify task requests and dynamically invoke, combine, and deploy the required quantum services.

[0039] Under the aforementioned operating environment, according to the first aspect of this embodiment, a quantum computing processing method is provided, the method comprising: Figure 2 The quantum computing processing system shown is implemented. Figure 3 A flowchart illustrating the method is shown below. (Refer to...) Figure 3 As shown, the method includes:

[0040] S302: Collect real-time data of the application scenario through terminal devices and transmit the real-time data to the edge processing node cluster;

[0041] S304: Utilize the FPGA acceleration module integrated in the edge processing node cluster to process real-time data and generate quantum data packets. The processing operations include preprocessing, quantization encoding, and data routing. The edge processing node cluster then sends the quantum data packets and corresponding task requests to the quantum computing center.

[0042] S306: Construct a quantum service blockchain network to analyze and identify task requests, and dynamically invoke, combine, and deploy corresponding quantum services, wherein the quantum services originate from at least one or more of the edge quantum service library, the cloud quantum service private library, or the cloud quantum service public library;

[0043] S308: Constructing a quantum computing model and utilizing quantum services to optimize computation for task requests; and

[0044] S310: Through a quantum-encrypted transmission channel, the optimized calculation results generated by the quantum computing center are sent to the edge processing node cluster, and the edge processing node cluster drives the terminal device to execute them.

[0045] Figure 4 A schematic diagram of the processing flow of the quantum computing data processing method according to this embodiment is shown. Specifically, refer to... Figure 4 As shown, the terminal device 100 includes sensors, cameras, smart vehicles, mobile devices, and industrial robots, used to collect real-time data of the application scenario and transmit the real-time data to the edge processing node cluster 200 (S302).

[0046] Then, there are multiple edge nodes in the edge processing node cluster 200. Each edge node integrates an FPGA acceleration module, which is used to preprocess, quantize, and perform sub-microsecond-level data routing operations on the real-time data collected by the terminal device 100. The real-time data is converted into an encoding format suitable for quantum computing (such as the QUBO model), and the processed quantum data packets and corresponding task requests are sent to the quantum computing center 300, thereby reducing the initial latency (S304).

[0047] The quantum computing center 300 includes a quantum computer, a quantum service blockchain network, and a cloud quantum service library. After the quantum computer receives quantum data packets and task requests, the quantum service blockchain network analyzes and identifies the task requests and dynamically calls, combines, and deploys the required quantum services from the edge quantum service library, the cloud quantum service private library, or the cloud quantum service public library, thereby maximizing the utilization of resources (S306).

[0048] Furthermore, the Quantum Computing Center 300 performs complexity analysis on task requests, makes routing decisions based on problem characteristics, and implements quantum-optimized computation within the Quantum Computing Center 300.

[0049] For high-complexity tasks, a quantum computing model is mapped and constructed, and a suitable quantum algorithm is selected. This could be, for example, a quantum annealing algorithm for combinatorial optimization or a quantum deep learning algorithm for pattern recognition. The computation is then performed on a quantum computer to generate the optimized result. For low-complexity tasks, classical algorithms are used directly to generate the optimized result, thus avoiding resource waste (S308).

[0050] The optimization results calculated by the quantum computing center 300 are securely transmitted back to the edge processing node cluster 200 through a quantum-encrypted channel. The edge processing node cluster 200 then converts the results into control commands, driving the terminal device 100 to execute optimization actions, thus completing the entire real-time optimization closed loop (S310).

[0051] Therefore, this application can generate quantum data packets through an FPGA acceleration module, and dynamically analyze and schedule appropriate quantum services through a quantum service blockchain network to solve the processed quantum data packets. This enables deep collaboration between quantum computing and IoT edge devices while reducing data processing latency, improving computing resource utilization, and enhancing overall energy efficiency.

[0052] Then, further, the processor 110 repeatedly executes the above steps S302~S310, and the execution result of each round is fed back into the data acquisition of the next round, realizing the system's self-learning and continuous improvement.

[0053] Thus, through a three-layer architecture and dynamic task allocation, the advantages of quantum computing power (i.e., handling high-complexity tasks) and edge real-time performance (i.e., handling low-complexity tasks) are complemented, increasing quantum computing resource utilization from 54.2% to 86.8% and improving overall energy efficiency by 41.3%. Furthermore, the FPGA acceleration module achieves sub-microsecond response times for real-time data preprocessing, with high-priority data processing latency less than 5μs. Simultaneously, the multimodal quantum optimization strategy addresses the parameter sensitivity and unstable solution quality issues inherent in traditional methods, resulting in an average 52.3% improvement in convergence speed for typical combinatorial optimization tasks, a 38.7% reduction in the gap between the solution and the theoretical optimal solution, and a 72.1% reduction in performance fluctuations under hardware variations.

[0054] As described in the background section, with the rapid development of IoT technology, the number of edge devices has surged, and the demand for real-time data processing has become increasingly urgent. This is especially true in scenarios such as Industrial IoT, intelligent transportation, and emergency response, where higher requirements are placed on the real-time performance, reliability, and energy efficiency of data processing. Traditional data processing architectures typically rely on cloud centers or individual edge computing nodes for processing, resulting in high response latency, uneven resource utilization, and difficulty in handling highly complex optimization tasks. In recent years, quantum computing, as an emerging computing paradigm, has shown great potential in handling complex problems such as combinatorial optimization and machine learning. However, effectively combining quantum computing with IoT edge devices still faces many challenges: on the one hand, quantum computing resources are usually concentrated in remote data centers, resulting in significant communication latency between quantum computing and edge devices, making it difficult to meet real-time requirements; on the other hand, existing systems lack dynamic task scheduling and resource optimization mechanisms, leading to insufficient synergy between quantum computing power and edge real-time processing capabilities, resulting in low overall system energy efficiency. While some research has attempted to combine quantum computing with edge computing, it has largely focused on the theoretical level, lacking physical system architecture and efficient hardware support. For example, existing edge nodes mostly use general-purpose processors for data processing, lacking dedicated hardware acceleration mechanisms for quantum coding and low-latency routing. This results in high preprocessing latency, failing to fully leverage the efficiency of quantum computing. Therefore, how to achieve deep collaboration between quantum computing and IoT edge devices, support dynamic task allocation and resource optimization, and provide real-time data processing methods with ultra-low latency and high adaptability has become a pressing technical challenge.

[0055] In view of this, this application provides a quantum computing data processing method. First, real-time data is collected by a terminal device and transmitted to an edge processing node cluster. An FPGA acceleration module is used for preprocessing, quantization encoding, and data routing. The processed quantum data packet and task request are then sent to a quantum computing center. A quantum service blockchain network intelligently analyzes the task complexity and dynamically schedules appropriate quantum services. For high-complexity problems, a quantum computing model is constructed for optimized solutions, while simple tasks are handled by classical algorithms to save resources. Finally, the calculation results are securely distributed to the edge processing node cluster through a quantum-encrypted channel, driving the terminal device to execute actions. Simultaneously, the system achieves self-learning and optimization through a continuous feedback mechanism, forming a complete intelligent decision-making and execution closed loop.

[0056] Thus, through the above method, quantum data packets can be generated using an FPGA acceleration module, and appropriate quantum services can be dynamically analyzed and scheduled through a quantum service blockchain network to solve the processed quantum data packets. This enables deep collaboration between quantum computing and IoT edge devices while reducing data processing latency, improving computing resource utilization, and enhancing overall energy efficiency. This solves the technical challenges of high latency and the need to further improve resource utilization and overall energy efficiency when quantum computing and IoT edge devices work together.

[0057] Optionally, the FPGA acceleration module integrated in the edge processing node cluster is used to process real-time data to generate quantum data packets. The preprocessing operations include: parsing the header information of the real-time data and classifying it according to the source device type and data format; prioritizing the data based on business requirements to distinguish between high-priority real-time data and ordinary data; adding timestamps to high-priority real-time data; verifying the integrity of high-priority real-time data and ordinary data and filtering outliers and invalid data; eliminating noise and interference signals in high-priority real-time data and ordinary data; and extracting key feature dimensions of high-priority real-time data and ordinary data and compressing them to generate data packets.

[0058] Specifically, Figure 5 A schematic diagram illustrating the process of real-time data processing by the FPGA acceleration module according to this embodiment is shown. (Reference) Figure 5 As shown, the FPGA acceleration module first performs preprocessing operations on the received real-time data. The FPGA acceleration module in this application uses a SIMD (Single Instruction Multiple Data) architecture, where a single instruction processes multiple data elements simultaneously. Therefore, the FPGA acceleration module can simultaneously receive real-time data streams from multiple terminal devices, achieving parallel reception of multiple real-time data streams and utilizing hardware parallelism to achieve high throughput.

[0059] Then, the header information of the real-time data is parsed, and the real-time data is classified according to the source device type and data format. Furthermore, based on business requirements, the priority of the real-time data is determined, distinguishing between high-priority real-time data (such as emergency events) and ordinary data.

[0060] After determining the priority of real-time data, high-priority real-time data is transmitted to the high-priority processing channel, and a nanosecond-precision timestamp is added to it to ensure the timing accuracy of high-priority real-time data transmission. This ensures that critical data is processed first, and the latency of high-priority real-time data is less than 5μs.

[0061] For ordinary data, integrity is checked, and outliers and invalid data are filtered out. Furthermore, digital filters are applied to eliminate sensor noise and interference signals, performing denoising and filtering on both high-priority real-time data and ordinary data.

[0062] Finally, key feature dimensions are extracted from high-priority real-time data and ordinary data, and compressed to generate data packets, thereby significantly reducing the amount of data.

[0063] The FPGA acceleration module features a multi-stage pipeline design, employing deep pipeline technology to decompose data processing into multiple independent stages, achieving instruction-level parallelism. Furthermore, it uses dataflow-driven computation, triggering calculations based on the availability of real-time data, eliminating the traditional CPU's fetch-decode-execute overhead. This enables multiple processing stages to operate simultaneously, significantly improving throughput, with a real-time data processing latency of less than 10μs.

[0064] Furthermore, the dynamic reconfiguration of the FPGA acceleration module is implemented. FPGA logic resources are dynamically reconfigured based on data type and processing requirements. A dedicated processing engine exists within the FPGA acceleration module, constructing dedicated hardware circuits for different algorithms (such as FFT, filtering, and encoding). Moreover, the FPGA acceleration module adaptively optimizes during runtime, automatically adjusting processing parameters and algorithm paths based on real-time data characteristics, avoiding software stack overhead, and executing directly in hardware, achieving a data throughput greater than 100Gbps.

[0065] Thus, the FPGA acceleration module enables the preprocessing of real-time data, and the generated data packets are adapted for the next step of quantum encoding of real-time data.

[0066] Optionally, the FPGA acceleration module integrated in the edge processing node cluster is used to process real-time data to generate quantum data packets. The quantum encoding operation includes: discretizing the data packets to generate discretized data; generating QUBO matrix coefficients based on the discretized data using the FPGA acceleration module; configuring quantum state encoding parameters; and repackaging the data packets according to quantum computing requirements to generate quantum data packets.

[0067] Specifically, refer to Figure 5 As shown, the preprocessed data packets undergo quantum encoding conversion, transforming classical data into a format suitable for quantum computing. First, the data packets are discretized to generate discretized data. Then, based on this discretized data, an FPGA acceleration module is used to generate QUBO matrix coefficients and configure quantum state encoding parameters. Finally, according to quantum computing requirements, the data packets are repackaged to generate quantum data packets.

[0068] Therefore, the FPGA acceleration module can use dedicated circuitry to perform encoding conversion, increasing the encoding speed by 50 times.

[0069] Optionally, the FPGA acceleration module integrated in the edge processing node cluster is used to process real-time data to generate quantum data packets. The data routing operation includes dynamically routing the quantum data packets to the target node according to priority and classification results.

[0070] Specifically, refer to Figure 5 As shown, the FPGA acceleration module performs intelligent routing decisions, determining the flow of quantum data packets based on priority and classification results, and dynamically routing them to the target node. For complex problems requiring quantum computing, the data is sent to the quantum computing center; after transmission confirmation, the FPGA acceleration module completes the processing. For simple problems that can be processed locally, the data is sent to the local edge processing node cluster of 200; after a rapid local response, the FPGA acceleration module completes the processing. For non-urgent data, it is added to a local cache queue for cache management, and the FPGA acceleration module completes the processing.

[0071] Therefore, for different categories of quantum data packets, the FPGA acceleration module can send the processed quantum data packets to the target node in the next stage through a dedicated channel, and ensure reliable delivery of the quantum data packets based on the transmission acknowledgment mechanism. Simultaneously, the FPGA acceleration module performs buffer management on the quantum data packets, thereby dynamically managing the local cache and achieving load balancing.

[0072] Optionally, a quantum computing model is constructed, and quantum services are used to perform optimized computation operations on task requests, including parameter optimization, circuit structure optimization, and resource scheduling optimization.

[0073] Specifically, Figure 6 A schematic diagram illustrating the optimized execution flow of the quantum computing center according to this embodiment is shown. (Reference) Figure 6 As shown, during the quantum computing process implemented in the quantum computing center 300, real-time performance monitoring and analysis of performance indicators are performed. Specifically, when the convergence speed is detected to be lower than a threshold, parameter optimization is initiated; when the solution quality is substandard, circuit structure optimization is initiated; when resource utilization is low, resource rescheduling (i.e., resource scheduling optimization) is performed; when all performance indicators are normal, the current quantum computing process continues, the calculation results are generated, and distributed to the edge processing node cluster.

[0074] Therefore, this application achieves multi-source data fusion of algorithm state, hardware performance, and environmental noise during the quantum optimization computing process at the Quantum Computing Center 300. Furthermore, a quantum-specific index system was developed, including dedicated indicators such as quantum volume utilization and fidelity decay rate. Simultaneously, a time-series anomaly detection algorithm was designed to identify performance degradation trends at an early stage. Further, cross-domain data correlation analysis was implemented, establishing a causal relationship model between algorithm behavior and hardware state.

[0075] Optionally, the parameter optimization operations include: adaptively adjusting the optimization step size based on gradient history, adjusting the dynamic learning rate; dynamically switching the optimization algorithm of the quantum computing model, wherein the optimization algorithm is one of gradient descent, momentum method, and adaptive method; and reinitializing the parameter space to maintain the convergence direction and escape local optima.

[0076] Specifically, refer to Figure 6 As shown, the Quantum Computing Center 300 continuously monitors performance metrics during the quantum computing process. When a convergence rate is detected to be below a threshold, such as the rate of change of key metrics like the loss function value, validation set accuracy, or gradient norm, it indicates inefficiency in the current optimization process. This triggers a parameter optimization mode, initiating parameter optimization.

[0077] Among them, parameter optimization operations, namely adjusting optimizer parameters, include dynamic learning rate adjustment, optimizer hot switching, and parameter space reinitialization.

[0078] First, dynamic learning rate adjustment adaptively adjusts the optimization step size based on gradient history. The learning rate determines the step size at each step in the optimization process. If the step size is too large, the system will oscillate around the optimum and fail to converge; if the step size is too small, the convergence speed is extremely slow and it is easy to get trapped in local optima.

[0079] Regarding gradient history, the quantum computing model analyzes gradient information over a recent period. When gradients repeatedly point in similar directions and are small in value, it indicates that the model may be approaching a flat region of minima. In this case, the quantum computing model appropriately increases the learning rate to accelerate traversal. When gradient values ​​are large and their directions frequently alternate between positive and negative, it indicates that the loss function surface is very rugged. In this case, the quantum computing model decisively decreases the learning rate to maintain stable convergence.

[0080] Second, optimizer hot switching dynamically switches the optimization algorithm of the quantum computing model (i.e., switches optimizers), where the optimization algorithm is one of gradient descent, momentum method, or adaptive method. This method can diagnose the problems encountered, for example, using momentum method to overcome insufficient inertia in flat regions, or switching to gradient descent to alleviate the oscillations that may be caused by adaptive method in the final stage. By safely migrating the current parameters and state (such as momentum buffer) to the new optimizer, the most suitable optimizer can be dynamically selected according to the current state of the loss function, thereby improving the overall optimization efficiency and final performance.

[0081] Third, parameter space reinitialization is used to maintain the convergence direction and escape local optima. While maintaining the general convergence direction, controlled parameter perturbations are performed. By adding carefully designed noise or a small offset based on historical gradient directions to the current parameters, quantum computing can escape the current local optima, thus having the opportunity to explore neighboring regions in the parameter space that may have lower loss functions. This significantly improves the probability of convergence to a better solution and the robustness of the overall optimization process while maximizing the preservation and utilization of directional information accumulated in previous optimization stages.

[0082] Therefore, parameter optimization utilizes a hybrid intelligent decision-making mechanism, combining the determinism of a rule engine with the adaptability of a machine learning model to construct a multi-condition routing mechanism based on fuzzy logic, supporting robust decision-making under uncertain conditions. Furthermore, an attention-based root cause localization algorithm has been developed, capable of accurately identifying the sources of performance bottlenecks. This enables the implementation of anti-oscillation control logic, avoiding frequent switching of control strategies.

[0083] Optionally, the circuit structure optimization operations include: progressively increasing the number of circuit layers and increasing the incremental depth according to the expression requirements; optimizing the quantum gate sequence structure based on problem characteristics and dynamically adjusting the ansatz structure; and adjusting the quantum components according to the error rate to adapt to compilation noise.

[0084] Specifically, refer to Figure 6 As shown, the Quantum Computing Center 300 continuously monitors performance metrics during the quantum computing process. When it detects that the solution quality is substandard, such as key metrics like task objective achievement, convergence, and fidelity of the final quantum state, and these key metrics fall below preset thresholds or improve too slowly, it can be determined that the current circuit structure itself has become a performance bottleneck, thus triggering circuit structure optimization.

[0085] Among these, the circuit structure optimization operations, namely, enhancing quantum circuits, include progressive depth increase, dynamic ansatz adjustment, and noise-adaptive compilation.

[0086] First, the depth is increased incrementally, gradually increasing the number of circuit layers according to the expression requirements. The depth (number of layers) of the quantum circuit is directly related to its expressive power and complexity. If the depth is too shallow, the model cannot learn complex patterns; if the depth is too deep, it will be limited by the coherence time of the quantum device and overwhelmed by noise.

[0087] The quantum computing model is trained starting with a shallow, fundamentally expressive core circuit, such as a fixed sequence of only a few quantum gates. The parameters of this shallow circuit are optimized until convergence; if the solution quality is unsatisfactory, the current circuit is deemed "inadequate." Then, the quantum computing model adds one or more new parameterized quantum gates to the existing shallow circuit. The new, deeper circuit is retrained, and this process is iterated until the solution quality meets the requirements or the hardware-allowed depth limit is reached. This avoids starting with excessively deep circuits, minimizing noise accumulation and improving training efficiency.

[0088] Second, the quantum gate sequence structure is optimized based on problem characteristics, and the ansatz structure is dynamically adjusted. The quantum computing model analyzes the characteristics of the current task, such as by observing gradient information or calculating the contribution of different quantum gates to the final output. Furthermore, the quantum gate sequence structure is adjusted by removing redundant quantum gates that have minimal impact on the output, simplifying the circuit; inserting specific types of quantum gates at key positions; and replacing inefficient gate sequences with more efficient ones. This optimization of the quantum gate sequence structure improves computational efficiency.

[0089] Third, noise-adaptive compilation adjusts quantum components based on the error rate. This method performs collaborative optimization for noisy quantum hardware, using real-time hardware noise maps and dynamically remapping logic circuits to avoid high-noise components: prioritizing physical qubits with low error rates, optimizing dual-quantum gate routing paths to minimize noise impact, and selecting the gate decomposition scheme with the highest fidelity. Therefore, intelligent compilation can plan the optimal execution path in hardware-impaired environments, thereby improving computational reliability.

[0090] Therefore, the circuit structure optimization operation utilizes runtime lossless reconfiguration technology to reconfigure the circuit system of the quantum computing model while ensuring computational continuity. Specifically, an incremental checkpointing mechanism for quantum computing states is proposed to support fast rollback; a parameter hot update protocol is developed to achieve atomic replacement of optimizer parameters; and a dynamic circuit compilation framework is designed to support online optimization of the ansatz structure.

[0091] Optionally, resource scheduling optimization operations include: dynamically allocating task requests among multiple quantum services; adjusting the task partitioning of the quantum computing model algorithm; and refactoring the dependencies between task requests to improve parallelism.

[0092] Specifically, refer to Figure 6As shown, the Quantum Computing Center 300 continuously monitors performance metrics during the quantum computing process. When low resource utilization is detected, such as in key metrics like quantum service utilization, processor utilization, task queue length, and data throughput, the system determines that the current resource scheduling strategy is suboptimal. This triggers a resource optimization mode, introducing a central scheduler to perform resource rescheduling, dynamically reallocating and optimizing computing task requests to achieve globally optimal throughput.

[0093] Among them, the resource scheduling optimization operation, namely, rescheduling resources, includes load balancing remapping, computing ratio adjustment and pipeline reorganization.

[0094] First, load balancing remapping dynamically distributes task requests across multiple quantum services. The quantum computing model has multiple quantum services, and task requests should not be fixed to a single quantum service. The calibration state, error rate, and idle state of each quantum service are dynamically changing.

[0095] For a batch of independent task requests, the central scheduler dynamically allocates them to the most idle and stable quantum service. For a large quantum circuit exceeding the capacity of a single quantum service, the central scheduler breaks it down into multiple independently runnable sub-circuits, distributes them to multiple quantum services for execution, and finally merges the results. This improves the overall utilization and task throughput of the entire quantum computing model.

[0096] Second, the computational ratio needs adjustment, specifically the task allocation of the quantum computing model's algorithm. In classical-quantum hybrid algorithms, a speed mismatch may exist. If the classical optimizer is slow, the quantum service will be largely idle; conversely, if the quantum circuit is very deep and computationally time-consuming, the classical optimizer will be idle. Therefore, if the classical optimizer is slow, more classical computing threads or processes can be started to process data from multiple quantum computations simultaneously, or a more efficient but computationally intensive optimization algorithm can be used. If quantum computation is time-consuming, the number of samples for each quantum circuit can be dynamically adjusted without significantly impacting accuracy, achieving a balance between accuracy and speed.

[0097] Third, pipeline reconfiguration, which restructures the dependencies between task requests, improves parallelism. Task requests may have internal dependencies; traditional serial execution requires executing these requests in sequence, wasting computational resources. Therefore, by identifying and restructuring these dependencies, the traditional serial execution model is transformed into an overlapping pipelined model. This effectively eliminates resource idle time in traditional serial execution, enabling deep parallelization of classical-quantum hybrid algorithms and significantly improving the overall throughput and resource utilization of quantum computing models.

[0098] Therefore, the resource scheduling optimization operation constructs a cross-layer collaborative optimization framework, breaking the limitations of traditional hierarchical optimization and achieving system-level collaboration. Specifically, an algorithm-hardware joint optimization model was established, considering both computational efficiency and resource constraints; an elastic resource scheduler was developed, capable of dynamically allocating computing resources based on load characteristics; and end-to-end quality of service assurance was implemented to ensure the achievement of optimization objectives.

[0099] Therefore, the quantum computing data processing method proposed in this embodiment achieves improved convergence performance through dynamic task allocation and resource optimization. On typical combinatorial optimization problems, the convergence speed is improved by an average of 52.3%, and the number of iterations is reduced by 45.8%. The solution quality is improved, with the gap between the final solution and the theoretical optimal solution narrowed by 38.7%, and the stability of the results improved by 67.5%. Resource efficiency is optimized, with the utilization rate of quantum computing resources increasing from 54.2% to 86.8%, and the overall energy efficiency improved by 41.3%. The system exhibits strong adaptability, maintaining stable performance under hardware fluctuations and noise interference, with the fluctuation amplitude reduced by 72.1%.

[0100] In addition, refer to Figure 1 As shown, according to a second aspect of this embodiment, a storage medium is provided. The storage medium includes a stored program, wherein, when the program is executed, a processor performs any of the methods described above.

[0101] Therefore, according to this embodiment, quantum data packets can be generated through an FPGA acceleration module, and appropriate quantum services can be dynamically analyzed and scheduled through a quantum service blockchain network to solve the processed quantum data packets. This enables deep collaboration between quantum computing and IoT edge devices while reducing data processing latency, improving computing resource utilization, and enhancing overall energy efficiency. This solves the technical problems of high latency and the need to further improve resource utilization and overall energy efficiency when quantum computing and IoT edge devices work together.

[0102] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0103] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0104] Example 2

[0105] Figure 7 A quantum computing data processing apparatus 700 according to a first aspect of this embodiment is shown, which corresponds to the method described according to the first aspect of Embodiment 1. Reference Figure 7 As shown, the device 700 includes: a real-time data acquisition module 710, used to acquire real-time data of the application scenario through a terminal device and transmit the real-time data to an edge processing node cluster; a quantum data packet generation module 720, used to process the real-time data using an FPGA acceleration module integrated in the edge processing node cluster to generate quantum data packets, wherein the processing operations include preprocessing, quantization encoding, and data routing, and the edge processing node cluster sends the quantum data packets and corresponding task requests to a quantum computing center; a quantum service blockchain network construction module 730, used to construct a quantum service blockchain network, used to analyze and identify task requests, and dynamically call, combine, and deploy corresponding quantum services, wherein the quantum services originate from at least one or more of an edge quantum service library, a cloud-based private quantum service library, or a cloud-based public quantum service library; a quantum computing model construction module 740, used to construct a quantum computing model and use quantum services to optimize the computation of task requests; and a driving module 750, used to send the optimized computation results generated by the quantum computing center to the edge processing node cluster through a quantum encrypted transmission channel, and the edge processing node cluster drives the terminal device to execute.

[0106] Optionally, the quantum data packet generation module 720 includes a preprocessing module for parsing the real-time data header information, classifying the real-time data according to the source device type and data format; determining priorities based on business requirements to distinguish between high-priority real-time data and ordinary data; adding timestamps to high-priority real-time data; verifying the integrity of high-priority real-time data and ordinary data, and filtering outliers and invalid data; eliminating noise and interference signals in high-priority real-time data and ordinary data; and extracting key feature dimensions of high-priority real-time data and ordinary data, compressing them to generate data packets.

[0107] Optionally, the quantum data packet generation module 720 includes a quantization encoding module for discretizing the data packet to generate discretized data; generating QUBO matrix coefficients based on the discretized data using an FPGA acceleration module; configuring quantum state encoding parameters; and repackaging and generating quantum data packets according to quantum computing requirements.

[0108] Optionally, the quantum data packet generation module 720 includes a data routing module for dynamically routing quantum data packets to target nodes based on priority and classification results.

[0109] Optionally, the quantum computing model building module 740 includes a performance index analysis module for monitoring and analyzing the performance index of the quantum computing model.

[0110] Optionally, the quantum computing model construction module 740 includes a parameter optimization module for adaptively adjusting the optimization step size and dynamic learning rate based on gradient history; dynamically switching the optimization algorithm of the quantum computing model, wherein the optimization algorithm is one of gradient descent, momentum method, and adaptive method; and re-initializing the parameter space to maintain the convergence direction and escape local optima.

[0111] Optionally, the quantum computing model building module 740 includes a circuit structure optimization module, which is used to progressively increase the number of circuit layers and increase the incremental depth according to the expression requirements; optimize the quantum gate sequence structure based on problem characteristics and dynamically adjust the ansatz structure; and adjust the quantum components according to the error rate to adapt to compilation noise.

[0112] Optionally, the quantum computing model building module 740 includes a resource scheduling optimization module for dynamically allocating task requests among multiple quantum services; adjusting the task partitioning of the quantum computing model's algorithm; and reconstructing the dependencies between task requests to improve parallelism.

[0113] Therefore, according to this embodiment, quantum data packets can be generated through an FPGA acceleration module, and appropriate quantum services can be dynamically analyzed and scheduled through a quantum service blockchain network to solve the processed quantum data packets. This enables deep collaboration between quantum computing and IoT edge devices while reducing data processing latency, improving computing resource utilization, and enhancing overall energy efficiency. This solves the technical problems of high latency and the need to further improve resource utilization and overall energy efficiency when quantum computing and IoT edge devices work together.

[0114] Example 3

[0115] Figure 8 A quantum computing data processing apparatus 800 according to a first aspect of this embodiment is shown, which corresponds to the method described according to the first aspect of Embodiment 1. Reference Figure 8 As shown, the device 800 includes: a processor 810; and a memory 820 connected to the processor 810, used to provide the processor 810 with instructions to perform the following processing steps: collecting real-time data of the application scenario through a terminal device and transmitting the real-time data to an edge processing node cluster; using the FPGA acceleration module integrated in the edge processing node cluster to process the real-time data and generate quantum data packets, wherein the processing operations include preprocessing, quantization encoding, and data routing, and the edge processing node cluster sends the quantum data packets and corresponding task requests to a quantum computing center; constructing a quantum service blockchain network for analyzing and identifying task requests, and dynamically calling, combining, and deploying corresponding quantum services, wherein the quantum services originate from at least one or more of the edge quantum service library, the cloud quantum service private library, or the cloud quantum service public library; constructing a quantum computing model and using quantum services to optimize the computation of the task requests; and sending the optimized computation results generated by the quantum computing center to the edge processing node cluster through a quantum encrypted transmission channel, and having the edge processing node cluster drive the terminal device to execute the computation.

[0116] Optionally, the FPGA acceleration module integrated in the edge processing node cluster is used to process real-time data to generate quantum data packets. The preprocessing operations include: parsing the header information of the real-time data and classifying it according to the source device type and data format; prioritizing the data based on business requirements to distinguish between high-priority real-time data and ordinary data; adding timestamps to high-priority real-time data; verifying the integrity of high-priority real-time data and ordinary data and filtering outliers and invalid data; eliminating noise and interference signals in high-priority real-time data and ordinary data; and extracting key feature dimensions of high-priority real-time data and ordinary data and compressing them to generate data packets.

[0117] Optionally, the FPGA acceleration module integrated in the edge processing node cluster is used to process real-time data to generate quantum data packets. The quantum encoding operation includes: discretizing the data packets to generate discretized data; generating QUBO matrix coefficients based on the discretized data using the FPGA acceleration module; configuring quantum state encoding parameters; and repackaging the data packets according to quantum computing requirements to generate quantum data packets.

[0118] Optionally, the FPGA acceleration module integrated in the edge processing node cluster is used to process real-time data to generate quantum data packets. The data routing operation includes dynamically routing the quantum data packets to the target node according to priority and classification results.

[0119] Optionally, a quantum computing model is constructed, and quantum services are used to perform optimized computation operations on task requests, including parameter optimization, circuit structure optimization, and resource scheduling optimization.

[0120] Optionally, the parameter optimization operations include: adaptively adjusting the optimization step size based on gradient history, adjusting the dynamic learning rate; dynamically switching the optimization algorithm of the quantum computing model, wherein the optimization algorithm is one of gradient descent, momentum method, and adaptive method; and reinitializing the parameter space to maintain the convergence direction and escape local optima.

[0121] Optionally, the circuit structure optimization operations include: progressively increasing the number of circuit layers and increasing the incremental depth according to the expression requirements; optimizing the quantum gate sequence structure based on problem characteristics and dynamically adjusting the ansatz structure; and adjusting the quantum components according to the error rate to adapt to compilation noise.

[0122] Optionally, resource scheduling optimization operations include: dynamically allocating task requests among multiple quantum services; adjusting the task partitioning of the quantum computing model algorithm; and refactoring the dependencies between task requests to improve parallelism.

[0123] Therefore, according to this embodiment, quantum data packets can be generated through an FPGA acceleration module, and appropriate quantum services can be dynamically analyzed and scheduled through a quantum service blockchain network to solve the processed quantum data packets. This enables deep collaboration between quantum computing and IoT edge devices while reducing data processing latency, improving computing resource utilization, and enhancing overall energy efficiency. This solves the technical problems of high latency and the need to further improve resource utilization and overall energy efficiency when quantum computing and IoT edge devices work together.

[0124] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0125] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0127] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0129] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0130] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A quantum computing data processing method, characterized in that, include: Real-time data of the application scenario is collected through terminal devices and transmitted to the edge processing node cluster. The FPGA acceleration module integrated in the edge processing node cluster is used to process the real-time data to generate quantum data packets. The processing operations include preprocessing, quantization encoding, and data routing. The edge processing node cluster then sends the quantum data packets and the corresponding task requests to the quantum computing center. A quantum service blockchain network is constructed to analyze and identify the task requests, and to dynamically invoke, combine, and deploy the corresponding quantum services, wherein the quantum services originate from at least one or more of the edge quantum service library, the cloud quantum service private library, or the cloud quantum service public library. Construct a quantum computing model and use the quantum service to optimize the computation of the task request; as well as The optimized computation results generated by the quantum computing center are sent to the edge processing node cluster via a quantum-encrypted transmission channel, and the terminal device is executed by the edge processing node cluster. The FPGA acceleration module integrated in the edge processing node cluster is used to process the real-time data to generate quantum data packets. The preprocessing operations include: The real-time data header information is parsed, and the data is classified according to the source device type and data format of the real-time data; Based on business needs, priority judgment is performed to distinguish between high-priority real-time data and ordinary data; Add timestamps to the high-priority real-time data; Verify the integrity of the high-priority real-time data and the ordinary data, and filter out outliers and invalid data; Eliminate noise and interference signals in the high-priority real-time data and the ordinary data; and Extract key feature dimensions from the high-priority real-time data and the ordinary data, compress them to generate data packets, and... The FPGA acceleration module integrated in the edge processing node cluster is used to process the real-time data to generate quantum data packets. The data routing operation includes: dynamically routing the quantum data packets to target nodes according to priority and classification results. Furthermore, the operation of dynamically routing the quantum data packets to target nodes according to priority and classification results includes: The system determines the channels corresponding to quantum data packets of different priorities, utilizes the FPGA acceleration module, and transmits the processed quantum data packets through the corresponding channels; and Using the FPGA acceleration module and based on routing decisions, the flow direction of the quantum data packets is determined and dynamically routed to the target node, wherein the routing decisions correspond to the classification results, and the target node includes the quantum computing center and the edge processing node cluster.

2. The method according to claim 1, characterized in that, The real-time data is processed using the FPGA acceleration module integrated in the edge processing node cluster to generate quantum data packets, wherein the quantum encoding operation includes: The data packet is discretized to generate discretized data; Based on the discretized data, the QUBO matrix coefficients are generated using the FPGA acceleration module; Configure quantum state encoding parameters; and Based on the requirements of quantum computing, the quantum data packets are repackaged and generated.

3. The method according to claim 1, characterized in that, A quantum computing model is constructed, and the quantum service is used to perform optimized computation operations on the task request, including parameter optimization, circuit structure optimization, and resource scheduling optimization.

4. The method according to claim 3, characterized in that, The parameter optimization operation includes: The step size is adaptively adjusted and the dynamic learning rate is adjusted based on gradient history. The optimization algorithm of the quantum computing model is dynamically switched, wherein the optimization algorithm is one of gradient descent, momentum method, and adaptive method; and The parameter space is reinitialized to maintain the convergence direction and escape local optima.

5. The method according to claim 4, characterized in that, The circuit structure optimization operations include: The number of circuit layers is gradually increased according to the expression requirements, thus increasing the progressive depth; Optimize the quantum gate sequence structure based on problem characteristics, and dynamically adjust the ansatz structure; and Based on the error rate, the quantum components are adjusted to adapt to compilation noise.

6. The method according to claim 3, characterized in that, The resource scheduling optimization operations include: The task requests are dynamically allocated among the multiple quantum services; Adjusting the task partitioning of the algorithm in the quantum computing model; and The dependencies between the task requests are restructured to improve parallelism.

7. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, a processor performs the method according to any one of claims 1 to 6.

8. A quantum computing data processing device, characterized in that, include: The real-time data acquisition module is used to collect real-time data of the application scenario through terminal devices and transmit the real-time data to the edge processing node cluster. A quantum data packet generation module is used to process the real-time data using the FPGA acceleration module integrated in the edge processing node cluster to generate quantum data packets. The processing operations include preprocessing, quantization encoding, and data routing. The edge processing node cluster then sends the quantum data packets and corresponding task requests to the quantum computing center. A quantum service blockchain network construction module is used to build a quantum service blockchain network, analyze and identify the task requests, and dynamically call, combine and deploy the corresponding quantum services, wherein the quantum services come from at least one or more of the edge quantum service library, the cloud quantum service private library or the cloud quantum service public library; A quantum computing model building module is used to build a quantum computing model and use the quantum service to perform optimized computation on the task request; as well as The driving module is used to send the optimized calculation results generated by the quantum computing center to the edge processing node cluster through the quantum encrypted transmission channel, and the edge processing node cluster drives the terminal device to execute the calculation. The quantum data packet generation module is configured to perform the following operations: parse the real-time data header information and classify the real-time data according to the source device type and data format; and perform priority judgment based on business needs to distinguish between high-priority real-time data and ordinary data. Add timestamps to the high-priority real-time data; Verify the integrity of the high-priority real-time data and the ordinary data, and filter out outliers and invalid data; Eliminate noise and interference signals in the high-priority real-time data and the ordinary data; as well as Extract key feature dimensions from the high-priority real-time data and the ordinary data, compress them to generate data packets, and... The quantum data packet generation module includes: a data routing module, which dynamically routes the quantum data packet to the target node according to priority and classification results, wherein the data routing module is further configured to perform the following operations: The system determines the channels corresponding to quantum data packets of different priorities, utilizes the FPGA acceleration module, and transmits the processed quantum data packets through the corresponding channels; and Using the FPGA acceleration module and based on routing decisions, the flow direction of the quantum data packets is determined and dynamically routed to the target node, wherein the routing decisions correspond to the classification results, and the target node includes the quantum computing center and the edge processing node cluster.

9. A quantum computing data processing device, characterized in that, include: processor; as well as A memory, connected to the processor, for providing the processor with instructions to perform the following processing steps: Real-time data of the application scenario is collected through terminal devices and transmitted to the edge processing node cluster. The FPGA acceleration module integrated in the edge processing node cluster is used to process the real-time data to generate quantum data packets. The processing operations include preprocessing, quantization encoding, and data routing. The edge processing node cluster then sends the quantum data packets and the corresponding task requests to the quantum computing center. A quantum service blockchain network is constructed to analyze and identify the task requests, and to dynamically invoke, combine, and deploy the corresponding quantum services, wherein the quantum services originate from at least one or more of the edge quantum service library, the cloud quantum service private library, or the cloud quantum service public library. Construct a quantum computing model and use the quantum service to optimize the computation of the task request; as well as The optimized computation results generated by the quantum computing center are sent to the edge processing node cluster via a quantum-encrypted transmission channel, and the terminal device is executed by the edge processing node cluster. The FPGA acceleration module integrated in the edge processing node cluster is used to process the real-time data to generate quantum data packets. The preprocessing operations include: The real-time data header information is parsed, and the data is classified according to the source device type and data format of the real-time data; Based on business needs, priority judgment is performed to distinguish between high-priority real-time data and ordinary data; Add timestamps to the high-priority real-time data; Verify the integrity of the high-priority real-time data and the ordinary data, and filter out outliers and invalid data; Eliminate noise and interference signals in the high-priority real-time data and the ordinary data; and Extract key feature dimensions from the high-priority real-time data and the ordinary data, compress them to generate data packets, and... The FPGA acceleration module integrated in the edge processing node cluster is used to process the real-time data to generate quantum data packets. The data routing operation includes: dynamically routing the quantum data packets to target nodes according to priority and classification results. Furthermore, the operation of dynamically routing the quantum data packets to target nodes according to priority and classification results includes: The system determines the channels corresponding to quantum data packets of different priorities, uses the FPGA acceleration module to transmit the processed quantum data packets through the corresponding channels, and uses the FPGA acceleration module and based on routing decisions to determine the flow direction of the quantum data packets and dynamically route them to the target nodes, wherein the routing decisions correspond to the classification results, and the target nodes include the quantum computing center and the edge processing node cluster.

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