Quantum computing system and method

Abstract

The invention relates to the technical field of quantum computing, and particularly discloses a quantum computing system and method.According to the system, a layered architecture is adopted, a resource management layer monitors heterogeneous quantum resources such as superconduction and ion traps, multiple types of information such as quantum bit numbers are collected, and dynamic scheduling and strategy adjustment are achieved through a hybrid heuristic algorithm; the algorithm optimization layer improves the algorithm execution efficiency through three strategies of algorithm decomposition parallelization, adaptive parameter adjustment and mixed classical-quantum computation; the task execution layer is provided with a uniform interface, intelligently matches resources according to tasks and resource states, and monitors and feeds back results. The computing method based on the system runs according to the steps of resource management, algorithm optimization and task execution, efficient quantum computing is achieved, and the resource utilization rate and the algorithm performance are improved. The overall performance, reliability and practicability of the quantum computing system are improved, and the complex requirements of different fields for quantum computing are met.

Description

Technical Field

[0001] The present invention relates to the field of quantum computing technology, and in particular to a quantum computing system and method. Background Art

[0002] With the in-depth development of quantum mechanics theory and continuous breakthroughs in experimental technology, quantum computing has become a research hotspot in the current cutting-edge scientific field. A variety of heterogeneous quantum computing technologies, such as superconducting quantum computing, trapped ion quantum computing, and optical quantum computing, have emerged one after another. Each technology offers advantages in terms of qubit characteristics, gate operation precision, and scalability. Superconducting quantum computing is easy to integrate on a large scale, trapped ion quantum computing boasts high quantum gate fidelity, and optical quantum computing excels in information transmission and processing speed. However, the diversity of these heterogeneous resources also presents a series of challenges.

[0003] On the one hand, existing quantum computing systems lack effective integration and unified management mechanisms for heterogeneous resources. Different types of quantum computing resources often operate independently, and the resource management layer lacks comprehensive, real-time information on the operating status of each resource. This leads to blind resource allocation, prone to idle or overused resources, and significantly reduced resource utilization. For example, in some scenarios, some high-performance resources are underloaded due to improper task matching, while other tasks cannot be executed in a timely manner due to insufficient resources.

[0004] On the other hand, quantum algorithms face numerous challenges in practical implementation. The fragility of quantum systems makes qubits extremely susceptible to environmental noise, leading to quantum state decoherence and increased computational errors. Furthermore, existing quantum algorithm optimization strategies are limited, making it difficult to fully exploit the parallel processing capabilities of heterogeneous quantum computing resources. Complex quantum algorithms cannot dynamically adjust parameters based on the real-time state of resources during execution, nor can they effectively combine the advantages of classical and quantum computing. This results in low computational efficiency and makes it difficult to meet practical application requirements.

[0005] In addition, when quantum computing resources fail or performance degrades, traditional systems lack effective fault-tolerance mechanisms and cannot guarantee task continuity, which seriously affects the reliability and practicality of quantum computing systems.

[0006] Therefore, a quantum computing system and method has become an urgent problem to be solved. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a quantum computing system and method, which realizes comprehensive monitoring and dynamic scheduling of heterogeneous quantum computing resources by constructing a layered architecture, uses multiple optimization strategies to improve the execution efficiency of quantum algorithms, and establishes a complete error mitigation and fault tolerance mechanism to reduce the calculation error rate and ensure the continuous execution of tasks, thereby improving the overall performance, reliability and practicality of the quantum computing system and meeting the complex needs of quantum computing in different fields.

[0008] To solve the above technical problems, the present invention provides a technical solution: a quantum computing system adopts a layered architecture design, specifically including a resource management layer, an algorithm optimization layer and a task execution layer. Each layer cooperates with each other to jointly realize efficient quantum computing functions.

[0009] Resource management layer: As the core of system resource management, it is responsible for dynamically monitoring and managing heterogeneous quantum computing resources such as superconducting, ion traps, and optical quanta. This layer collects rich resource status information in real time through sensors and monitoring modules deployed on various resource devices, including not only key quantum characteristic parameters such as the number of quantum bits, coherence time, and gate operation fidelity, but also covers environmental parameters such as the operating temperature and voltage of the resources, as well as load information such as the current task queue and expected completion time of the resources. Based on these comprehensive monitoring data, the resource management layer uses advanced resource allocation algorithms, combined with the type of task (such as optimization problems, simulation problems, password cracking, etc.), input scale and other requirements, to achieve dynamic allocation and scheduling of resources. The specific methods for dynamic allocation and scheduling of resources are as follows:

[0010] Resource status monitoring: Using high-precision sensors and monitoring modules, real-time information is collected on the number of quantum bits, coherence time, gate operation fidelity, operating temperature, voltage, current task queue, and expected completion time of each quantum computing resource. The data is then transmitted to the resource management layer for storage and analysis, providing an accurate basis for resource scheduling.

[0011] Task Requirements Analysis: Upon receiving a task submitted by a user, we conduct a detailed analysis of the task type and input size. For optimization problems, we evaluate the required number of qubits and computational accuracy. For simulation problems, we analyze the complexity of the simulated physical system, the type and number of quantum gate operations required, and other factors to determine the quantum computing resources required to execute the task.

[0012] Dynamic Scheduling: The resource management layer dynamically allocates and schedules resources based on collected resource status information and analyzed task requirements using a hybrid heuristic algorithm that combines the strengths of genetic algorithms and simulated annealing. During the scheduling process, the optimal resource is allocated based on factors such as task priority and deadline. A dynamic feedback mechanism is also established to adjust scheduling strategies in real time based on changes in resource status (such as resource failures or performance degradation) and task execution feedback (such as slow task progress), ensuring timely and efficient task completion.

[0013] Algorithm optimization layer: Integrates multiple quantum algorithm optimization strategies and is committed to improving the execution efficiency and performance of quantum algorithms in heterogeneous resource environments.

[0014] Algorithm decomposition and parallelization strategy: For complex quantum algorithms, a decomposition method based on task dependencies and resource characteristics is used to break the algorithm into multiple subtasks that can be executed in parallel. For example, for quantum chemistry simulation algorithms, tasks such as molecular structure modeling, quantum state calculation, and result analysis can be split into different parts. At the same time, a specialized subtask scheduling algorithm is designed to rationally allocate subtasks based on the processing capabilities (such as the number of quantum bits and computing speed) and real-time status (load conditions and performance fluctuations) of heterogeneous quantum computing resources, fully leveraging the parallel processing advantages of each resource and accelerating the overall execution of the algorithm.

[0015] Adaptive parameter adjustment mechanism: This mechanism establishes a mapping model between quantum computing resource states and algorithm parameters, and uses machine learning algorithms to train and optimize this model. During quantum algorithm execution, changes in resource states, such as shortened qubit coherence times and decreased gate operation fidelity, are monitored in real time. Algorithm parameters, including the order of quantum gate operations, measurement strategy, and number of iterations, are dynamically adjusted based on the model's predictions. For example, if a decrease in the coherence time of a qubit in a resource is detected, the algorithm automatically adjusts the order of operations involving that qubit to minimize the impact of quantum state decoherence and ensure the algorithm maintains efficient operation despite resource state changes.

[0016] Hybrid classical-quantum computing strategy: By designing a classical preprocessing module and a classical postprocessing module, the advantages of classical and quantum computers are complemented. Before executing the quantum algorithm, the classical preprocessing module leverages the powerful data processing capabilities of classical computers to perform operations such as data compression and feature extraction on the task data, reducing the data size and complexity of the quantum computation. After the quantum computation is complete, the classical postprocessing module decrypts and verifies the quantum computation results, further optimizing them. Furthermore, the boundaries between classical and quantum computing tasks are rationally delineated. For example, in optimization problems, classical computing can be used to generate and screen initial solutions, while quantum computing can be used to search for the global optimal solution, improving overall computational efficiency.

[0017] The Task Execution Layer provides users with a unified quantum computing task interface, allowing them to conveniently submit tasks and obtain results. The Task Execution Layer uses an intelligent matching algorithm to automatically select the most appropriate quantum computing resources for task execution based on the task type, input size, and resource status information provided by the Resource Management Layer. For example, for small optimization tasks with low computational time requirements, optical quantum computing resources with low current load are prioritized. For large-scale simulation tasks, superconducting or ion trap quantum computing resources with sufficient qubits and stable performance are selected. During task execution, task progress and resource usage are continuously monitored to ensure efficient and stable task completion, and computational results are promptly fed back to users.

[0018] The present invention also provides a quantum computing method based on the above quantum computing system, comprising the following steps:

[0019] Resource management steps: Dynamically monitor the status information of heterogeneous quantum computing resources, and use a hybrid heuristic algorithm to achieve dynamic allocation and scheduling of resources based on monitoring data and task requirements.

[0020] Algorithm optimization steps: Use algorithm decomposition and parallelization strategies to decompose complex quantum algorithms into multiple subtasks, use adaptive parameter adjustment mechanisms to dynamically adjust algorithm parameters according to the real-time status of resources, and use hybrid classical-quantum computing strategies combined with classical computers for pre-processing and post-processing.

[0021] Task execution steps: Users submit tasks through a unified interface. The system intelligently matches tasks with resources based on task type, input scale, and resource status information, executes the tasks, and returns results.

[0022] The advantages of the present invention compared with the prior art are:

[0023] The quantum computing system of the present invention comprehensively monitors and dynamically schedules heterogeneous quantum computing resources through the resource management layer, can accurately match resources according to task requirements, significantly improve resource utilization, avoid resource waste, and achieve optimal resource configuration.

[0024] The various optimization strategies of the algorithm optimization layer work together, algorithm decomposition and parallelization fully utilize the parallel processing capabilities of heterogeneous resources, adaptive parameter adjustment enables the algorithm to adapt to changes in resource status, and hybrid classical-quantum computing achieves complementary advantages, greatly improving the execution efficiency and computational accuracy of quantum algorithms.

[0025] The intelligent matching function of the task execution layer ensures the rational allocation of tasks and resources. Combined with the dynamic scheduling of the resource management layer and the performance guarantee of the algorithm optimization layer, it can effectively deal with resource failures and performance fluctuations, ensure continuous and stable execution of tasks, and improve the reliability and practicality of the system.

[0026] The quantum computing system and method of the present invention are applicable to various types of quantum computing tasks and different heterogeneous quantum computing resources. They can meet the complex computing needs of multiple fields such as scientific research, finance, and cryptography, and have broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a system block diagram of a quantum computing system of the present invention.

[0028] Figure 2 It is a flow chart of a quantum computing method of the present invention.

[0029] Figure 3 It is a flow chart of the resource dynamic allocation and scheduling method. DETAILED DESCRIPTION

[0030] Various exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention.

[0031] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0032] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0033] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0034] The following is a further detailed description of a quantum computing system and method of the present invention with reference to the accompanying drawings.

[0035] Combined with attachment Figure 1-3 , the present invention is introduced in detail.

[0036] A quantum computing system adopts a layered architecture design, specifically including a resource management layer, an algorithm optimization layer, and a task execution layer. Each layer collaborates with each other to jointly achieve efficient quantum computing functions.

[0037] 1. Resource Management Layer

[0038] As the core of system resource management, it is responsible for dynamically monitoring and managing heterogeneous quantum computing resources such as superconducting, ion traps, and optical quanta. This layer collects rich resource status information in real time through sensors and monitoring modules deployed on various resource devices, including not only key quantum characteristic parameters such as the number of quantum bits, coherence time, and gate operation fidelity, but also covers environmental parameters such as the operating temperature and voltage of the resources, as well as load information such as the current task queue and expected completion time of the resources. Based on these comprehensive monitoring data, the resource management layer uses advanced resource allocation algorithms, combined with the type of task (such as optimization problems, simulation problems, password cracking, etc.), input scale and other requirements, to achieve dynamic allocation and scheduling of resources. The specific methods are as follows:

[0039] (1) Resource status monitoring

[0040] Using high-precision sensors and monitoring modules, the system collects status information such as the number of quantum bits, coherence time, gate operation fidelity, operating temperature, voltage, current task queue, and expected completion time of each quantum computing resource in real time. The data is then transmitted to the resource management layer for storage and analysis in real time, providing an accurate basis for resource scheduling.

[0041] (2) Task Requirements Analysis

[0042] After receiving a task submitted by a user, we conduct a detailed analysis of the task type and input size. For optimization problems, we evaluate the number of qubits required and the required computational accuracy. For simulation problems, we analyze the complexity of the simulated physical system, the type and number of quantum gate operations required, and other factors to determine the quantum computing resources required to execute the task.

[0043] (3) Dynamic Scheduling

[0044] Based on collected resource status information and analyzed task requirements, the resource management layer dynamically allocates and schedules resources using a hybrid heuristic algorithm that combines the strengths of genetic algorithms and simulated annealing. During the scheduling process, the optimal resource is allocated based on factors such as task priority and deadline. A dynamic feedback mechanism is also established to adjust scheduling strategies in real time based on changes in resource status (such as resource failures or performance degradation) and task execution feedback (such as slow task progress), ensuring timely and efficient task completion.

[0045] Introduce the comprehensive resource evaluation formula, and assume that the resource r i The comprehensive evaluation value is Evaluation parameters include the number of qubits coherence time Door operation fidelity Load factor (The higher the load, The larger the value is), the weighted sum is calculated as:

[0046]

[0047] Among them, α, β, γ, and δ are weight coefficients, which are adjusted according to the task type. For example, the simulation task can increase and The weight of ; max(q) and max(t) are the maximum values ​​of the number of quantum bits and coherence time in all resources respectively.

[0048] The formula for associating task priority with resource allocation is: let the priority of task j be P j , the deadline is D j , the current time is T, and the number of quantum bits required for the task is q j , then task j has a certain impact on resource r i Adaptability for:

[0049]

[0050] in, is the sum of the comprehensive evaluation values ​​of all resources, τ is the time decay coefficient, is the indicator function, when the resource r i It is 1 when the number of quantum bits meets the requirements of task j, otherwise it is 0.

[0051] 2. Algorithm Optimization Layer

[0052] Integrate multiple quantum algorithm optimization strategies and strive to improve the execution efficiency and performance of quantum algorithms in heterogeneous resource environments.

[0053] (1) Algorithm decomposition and parallelization strategy

[0054] For complex quantum algorithms, a decomposition method based on task dependencies and resource characteristics is used to break the algorithm down into multiple subtasks that can be executed in parallel. For example, for quantum chemistry simulation algorithms, tasks such as molecular structure modeling, quantum state calculation, and result analysis can be split into different parts. At the same time, a specialized subtask scheduling algorithm is designed to rationally allocate subtasks based on the processing capabilities (such as the number of quantum bits and computing speed) and real-time status (load conditions and performance fluctuations) of heterogeneous quantum computing resources, fully leveraging the parallel processing advantages of each resource and accelerating the overall execution of the algorithm.

[0055] The load balancing formula for subtask scheduling, assuming that subtask s k The computational cost is Resources i The allocated computational effort is Resources i The processing capacity is Then subtask s k Assigned to resource ri Probability for:

[0056]

[0057] By calculating the proportional relationship between the computational load of the subtask and the remaining processing capacity of the resources, and comprehensively considering the resource load situation, subtasks with larger computational load are more likely to be allocated to resources with strong processing capacity and low load, effectively avoiding the situation where some resources are overloaded while others are idle, ensuring the load balancing of the entire quantum computing system during the parallel execution of the algorithm, and improving resource utilization efficiency and algorithm execution speed.

[0058] (2) Adaptive parameter adjustment mechanism

[0059] A mapping model between quantum computing resource states and algorithm parameters is established, and this model is trained and optimized using machine learning algorithms. During quantum algorithm execution, resource state changes, such as shortened qubit coherence times and decreased gate operation fidelity, are monitored in real time. Algorithm parameters, including the order of quantum gate operations, measurement strategy, and number of iterations, are dynamically adjusted based on the model's predictions. For example, if a decrease in the coherence time of a qubit in a resource is detected, the algorithm automatically adjusts the order of operations involving that qubit to minimize the impact of quantum decoherence and ensure the algorithm maintains efficient operation despite resource state changes.

[0060] The formula for adjusting the number of iterations based on the coherence time is as follows: the initial number of iterations is N0, and the current coherence time is t cur , the reference coherence time is t ref , the adjusted number of iterations N is:

[0061]

[0062] Based on this, the number of iterations can be flexibly adjusted according to the actual status of resources and task requirements, effectively responding to the impact of changes in quantum bit coherence time on algorithm performance, and ensuring the stability and efficiency of quantum algorithms under different resource conditions.

[0063] (3) Hybrid classical-quantum computing strategy

[0064] Through the classical pre-processing module and the classical post-processing module, the advantages of classical and quantum computers are complemented. Before executing the quantum algorithm, the classical pre-processing module leverages the powerful data processing capabilities of classical computers to perform operations such as data compression and feature extraction on the task data, reducing the data size and complexity of the quantum computation. After the quantum computation is completed, the classical post-processing module decrypts the quantum computation results and verifies the results, further optimizing them. Furthermore, the boundaries between classical and quantum computing tasks are rationally delineated. For example, in optimization problems, classical computing can be used to generate and screen initial solutions, while quantum computing is used to search for the global optimal solution, improving overall computational efficiency.

[0065] 3. Task Execution Layer

[0066] A unified quantum computing task interface is provided for users, allowing them to conveniently submit tasks and obtain results. The task execution layer uses an intelligent matching algorithm to automatically select the most appropriate quantum computing resource for task execution based on the task type, input size, and resource status information provided by the resource management layer. For example, for small optimization tasks with low computational time requirements, optical quantum computing resources with low current load are prioritized. For large-scale simulation tasks, superconducting or ion trap quantum computing resources with sufficient qubits and stable performance are selected. During task execution, task progress and resource usage are continuously monitored to ensure efficient and stable completion, and computational results are promptly fed back to users.

[0067] The present invention also provides a quantum computing method based on the above quantum computing system, comprising the following steps:

[0068] (1) Resource management

[0069] Dynamically monitor the status information of heterogeneous quantum computing resources. Based on monitoring data and task requirements, a hybrid heuristic algorithm is adopted, combined with the comprehensive resource evaluation formula and the task-resource adaptation formula to achieve dynamic allocation and scheduling of resources.

[0070] (2) Algorithm Optimization

[0071] Adopting the algorithm decomposition and parallelization strategy, complex quantum algorithms are decomposed into multiple subtasks based on the load balancing formula for subtask scheduling; utilizing the adaptive parameter adjustment mechanism, the formula is adjusted according to the number of iterations based on the coherence time, and the algorithm parameters are dynamically adjusted according to the real-time status of resources; using the hybrid classical-quantum computing strategy combined with classical computers for pre-processing and post-processing.

[0072] (3) Task Execution

[0073] Users submit tasks through a unified interface. The system uses an intelligent matching algorithm based on the task type, input scale, and resource status information, and refers to comprehensive resource evaluation and task-resource compatibility to match tasks and resources, execute the tasks, and return the results.

[0074] The specific implementation process of a quantum computing system and method of the present invention is as follows:

[0075] Example 1: RSA password cracking task

[0076] 1. Mission Background

[0077] A user submits an RSA password cracking task with a 1024-bit encryption key, requiring the task to be cracked within 24 hours. The task priority is set to high. This task is computationally intensive, requiring a large number of quantum bit resources and high computational precision to quickly factor large integers.

[0078] 2. Resource management layer operations

[0079] Resource status monitoring: The resource management layer collects resource status information in real time through sensors deployed on quantum computing devices, such as superconducting, trapped ion, and optical quantum computing devices. For example, superconducting quantum computing device A currently has 80 available qubits, a coherence time of 120 microseconds, a gate operation fidelity of 99.2%, an operating temperature of 50mK, and stable voltage. There are three low-priority tasks in the current task queue, with an estimated completion time of two hours. Trap ion quantum computing device B has 60 available qubits, a coherence time of 100 microseconds, a gate operation fidelity of 98.8%, an operating temperature of 300mK, and normal voltage. There are currently no tasks queued. Optical quantum computing device C has 40 available qubits, a coherence time of 80 microseconds, a gate operation fidelity of 98%, an operating temperature of room temperature, and stable voltage. There is one medium-priority task in the current task queue, with an estimated completion time of three hours.

[0080] Task requirement analysis: Based on the analysis of the RSA password cracking task and empirical formulas and historical data, it is estimated that the task requires about 60-80 quantum bits, with high requirements for coherence time and gate operation fidelity, and the calculation accuracy needs to reach 10 -6 level.

[0081] Dynamic scheduling: Use the resource comprehensive evaluation formula to calculate the comprehensive evaluation value of each resource. For superconducting quantum computing device A:

[0082]

[0083] Similarly, the comprehensive evaluation values ​​of the ion trap quantum computing device B and the optical quantum computing device C are calculated and recorded as S B and S C .

[0084] Then calculate the adaptability of the task to each resource based on the formula related to task priority and resource allocation. Let the current time be T0 and the deadline be T end , τ = 1 (hour), task priority P = 3 (high priority):

[0085]

[0086] After calculation, superconducting quantum computing device A has the highest adaptability, so the task is assigned to superconducting quantum computing device A.

[0087] 3. Algorithm optimization layer operations

[0088] Algorithm decomposition and parallelization: The RSA password cracking algorithm is decomposed into large integer decomposition subtasks, quantum state preparation subtasks, quantum measurement subtasks, etc. based on task dependencies. Using the load balancing formula for subtask scheduling, according to the processing power of superconducting quantum computing device A (each quantum bit can execute 10 5 The system allocates execution order and qubit resources to each subtask based on the number of gate operations (times gate operations) and the allocated computational load (currently three low-priority tasks occupy approximately 30% of the computational resources). For example, the large integer factorization subtask is assigned to the first 40 qubits with higher computational power, while the quantum state preparation subtask is assigned to the remaining 40 qubits.

[0089] Adaptive parameter adjustment: During the task execution, the resource status of the superconducting quantum computing device A is monitored in real time. After 12 hours of operation, it is detected that the coherence time of a certain quantum bit has dropped from 120 microseconds to 100 microseconds. Using the iteration adjustment formula based on the coherence time, the initial iteration number N0 is set to 1000, and the reference coherence time t ref =120 microseconds, adjustment factor λ = 0.5:

[0090]

[0091] The number of iterations of the relevant quantum gate operations was automatically adjusted from 1000 to 913 to reduce the impact of quantum state decoherence.

[0092] Hybrid classical-quantum computing: The classical pre-processing module performs feature extraction and format conversion on the encrypted ciphertext data to reduce data redundancy; the classical post-processing module verifies and decrypts the possible private keys obtained by quantum computing, screens out the correct private keys, and improves cracking efficiency.

[0093] 4. Task execution layer operations

[0094] After receiving a task through a unified interface, the task execution layer uses an intelligent matching algorithm to identify device A as a suitable execution resource based on the task type, input size, and the resource status of superconducting quantum computing device A allocated by the resource management layer. During task execution, the layer continuously monitors task progress and device A's resource usage, recording metrics such as the number of executed quantum gate operations and quantum bit occupancy every 10 minutes. When the task is completed after 20 hours, the cracked private key is provided to the user via the interface.

[0095] Example 2: Quantum Chemistry Molecular Dynamics Simulation Task

[0096] 1. Mission Background

[0097] A user submitted a quantum chemistry molecular dynamics simulation task to simulate the dynamic behavior of a 50-atom protein molecule in solution. The simulation duration was 100 picoseconds and the task priority was medium. This task required a large number of quantum bits to accurately describe the quantum state of the molecule and had certain requirements for computing speed and stability.

[0098] 2. Resource management layer operations

[0099] Resource status monitoring: Real-time acquisition of the status of each quantum computing resource. Superconducting quantum computing device D currently has 100 available qubits, a coherence time of 110 microseconds, a gate operation fidelity of 99%, an operating temperature of 45mK, stable voltage, and two medium-priority tasks in the current task queue with an estimated completion time of 1.5 hours; ion trap quantum computing device E has 70 available qubits, a coherence time of 90 microseconds, a gate operation fidelity of 98.5%, an operating temperature of 280mK, normal voltage, and one high-priority task in the current task queue with an estimated completion time of 4 hours; optical quantum computing device F has 30 available qubits, a coherence time of 70 microseconds, a gate operation fidelity of 97.5%, an operating temperature of room temperature, stable voltage, and no tasks are currently queued.

[0100] Task requirement analysis: After analysis, the molecular dynamics simulation task is expected to require 80-90 quantum bits, with a coherence time requirement of more than 100 microseconds, and a large number of quantum gate operations to simulate the dynamic changes of molecules.

[0101] Dynamic scheduling: Calculate the comprehensive evaluation value of each resource and select superconducting quantum computing device D, which has the highest comprehensive evaluation value and meets the required number of qubits, to execute the task. Using a formula linking task priority with resource allocation, and taking into account factors such as task priority and expected completion time, device D is determined to be the optimal choice.

[0102] 3. Algorithm optimization layer operations

[0103] Algorithm decomposition and parallelization: The quantum chemistry molecular dynamics simulation algorithm is decomposed into molecular structure initialization subtasks, quantum state evolution subtasks, intermolecular interaction calculation subtasks, and result analysis subtasks. Based on the resource characteristics of the superconducting quantum computing device D, qubits and execution order are assigned to each subtask to fully utilize the device's parallel processing capabilities.

[0104] Adaptive parameter adjustment: During the simulation process, when the gate operation fidelity of device D is monitored to drop to 98.8%, the adaptive parameter adjustment mechanism automatically optimizes the quantum gate operation sequence to reduce the impact of low-fidelity gate operations on the simulation results.

[0105] Hybrid classical-quantum computing: The classical preprocessing module compresses and preprocesses the initial structural data of protein molecules to reduce the amount of quantum computing data; the classical post-processing module filters and analyzes the molecular dynamics trajectory data obtained by quantum computing to extract key information and improve the usability of simulation results.

[0106] 4. Task execution layer operations

[0107] After receiving the task, the task execution layer confirms that superconducting quantum computing device D is a suitable resource and initiates task execution. During execution, a monitoring system tracks task progress and device D's resource usage in real time. Upon task completion, the simulated molecular dynamics trajectory and related data processing results are returned to the user for subsequent analysis and research.

[0108] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A quantum computing system, characterized in that include: The resource management layer is used to dynamically monitor and manage heterogeneous quantum computing resources, collect resource status information in real time, and use resource allocation algorithms to dynamically allocate and schedule resources based on monitoring data and task requirements; The algorithm optimization layer integrates multiple quantum algorithm optimization strategies, including algorithm decomposition and parallelization strategies that decompose complex quantum algorithms into multiple subtasks to leverage the parallel processing capabilities of heterogeneous resources; adaptive parameter adjustment mechanisms that dynamically adjust the quantum gate operation sequence, measurement strategy, and number of iterations based on the real-time status of resources; and hybrid classical-quantum computing strategies that combine the numerical computing capabilities of classical computers with classical pre-processing or post-processing of quantum algorithms. The task execution layer provides a unified quantum computing task interface for users to submit tasks and obtain results. Based on the task type, input scale, and resource status information provided by the resource management layer, it intelligently matches tasks and resources and selects the most appropriate quantum computing resources to execute tasks.

2. A quantum computing system according to claim 1, characterized in that: The heterogeneous quantum computing resources include superconductors, ion traps, and optical quanta; the resource status information includes the number of quantum bits, coherence time, gate operation fidelity, and resource load.

3. A quantum computing system according to claim 2, characterized in that: The resource status information collected by the resource management layer also includes the operating temperature and voltage of the resource, as well as the current task queue and expected completion time of the resource.

4. A quantum computing system according to claim 3, characterized in that: The method for dynamic allocation and scheduling of resources specifically includes the following steps: Resource status monitoring: Utilize sensors and monitoring modules to collect real-time information on the number of qubits, coherence time, gate operation fidelity, operating temperature, voltage, current task queue, and expected completion time of each quantum computing resource; Task requirement analysis: After receiving a task submitted by a user, the system evaluates the quantum computing resources required to execute the task based on the task type and input scale, including the number of quantum bits, computational accuracy requirements, and the type and number of quantum gate operations required. Dynamic scheduling: Based on the collected resource status information and analyzed task requirements, a hybrid heuristic algorithm combining genetic algorithm and simulated annealing algorithm is used to dynamically allocate and schedule resources, and the scheduling strategy is adjusted in real time according to changes in resource status and task execution feedback information.

5. A quantum computing system according to claim 4, characterized in that: The algorithm decomposition and parallelization strategy of the algorithm optimization layer adopts a decomposition strategy based on task dependencies and resource characteristics to decompose the algorithm into multiple subtasks that can be executed in parallel, and designs a subtask scheduling algorithm to reasonably allocate subtasks according to the processing power and status of heterogeneous quantum computing resources.

6. A quantum computing system according to claim 5, characterized in that: The adaptive parameter adjustment mechanism of the algorithm optimization layer adopts an adaptive parameter adjustment strategy to establish a mapping relationship model between quantum computing resource status and algorithm parameters. The model is trained and optimized through a machine learning algorithm. During the execution of the quantum algorithm, the algorithm parameters are dynamically adjusted according to the model prediction results and changes in resource status.

7. A quantum computing system according to claim 6, characterized in that: The hybrid classical-quantum computing strategy of the algorithm optimization layer includes a classical preprocessing module and a classical post-processing module; the classical preprocessing module uses a classical computer to perform data compression and feature extraction before the quantum algorithm is executed; the classical post-processing module performs data decryption and result verification after the quantum computing is completed, dividing the task boundaries between classical computing and quantum computing.

8. A quantum computing method based on the quantum computing system according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Resource Management: Dynamically monitor the status of heterogeneous quantum computing resources and use a hybrid heuristic algorithm to dynamically allocate and schedule resources based on monitoring data and task requirements. S2. Algorithm optimization: Use algorithm decomposition and parallelization strategies to decompose complex quantum algorithms into multiple subtasks, utilize adaptive parameter adjustment mechanisms to dynamically adjust algorithm parameters according to the real-time status of resources, and use hybrid classical-quantum computing strategies combined with classical computers for pre-processing and post-processing. S3. Task execution: Users submit tasks through a unified interface. The system intelligently matches tasks and resources based on task type, input scale, and resource status information, executes tasks, and returns results.

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