A heterogeneous quantum computing resource scheduling method, device, equipment and medium

Abstract

This invention proposes a method, apparatus, device, and medium for scheduling heterogeneous quantum computing resources, relating to the field of combinatorial optimization technology for quantum computing resource management. The method includes: scale-free modeling of the actual operational structure of the Industrial Internet (IIoT) to obtain an IIoT network graph, and acquiring hub nodes within the IIoT network graph; capability modeling of heterogeneous quantum computing resources, and constructing decision variables mapping heterogeneous quantum computing resources to hub nodes based on the capability model; constructing constraint models for hub nodes and heterogeneous quantum computing resources based on the decision variables; constructing an objective function, establishing an optimization model based on the constraint model and the objective function, solving the optimization model using simulated annealing, and scheduling heterogeneous quantum computing resources based on the solution results. This invention avoids the resource waste problem caused by simple rule-based allocation, while ensuring the efficient utilization of quantum resources in the IIoT environment.

Description

Technical Field

[0001] This invention relates to the field of quantum computing resource management and combinatorial optimization technology, and more specifically, to a method, apparatus, device, and medium for scheduling heterogeneous quantum computing resources. Background Technology

[0002] With the rapid development of the Industrial Internet, industrial big data, and intelligent manufacturing technologies, traditional centralized industrial control and computing models are gradually evolving into ultra-large-scale, distributed, and highly heterogeneous Industrial Internet systems. Numerous studies have shown that these industrial systems, in their long-term evolution, have gradually exhibited significant complex network characteristics, typically manifesting as scale-free networks in their topology. A key feature of this network structure is that a small number of hub nodes possess extremely high degree and information aggregation capabilities, while most ordinary nodes have only limited connections. In Industrial Internet scenarios, these hub nodes often undertake critical functions such as data acquisition, task aggregation, computation scheduling, and resource distribution; their performance and stability directly determine the overall system's operational efficiency and reliability.

[0003] Meanwhile, quantum computing, as a new generation of computing paradigm, has shown potential computational advantages in areas such as combinatorial optimization, machine learning, and industrial scheduling. However, constrained by the current stage of quantum hardware development, existing quantum computing devices still generally face a series of practical constraints:

[0004] 1) The size of qubits is limited, and different quantum processors vary significantly in terms of the number of available qubits, bit connection topology, gate operation fidelity, and the depth of quantum circuits they can support;

[0005] 2) Quantum computing devices are physically distributed, making it difficult to build a unified large-scale quantum processor in a centralized manner. Quantum computing power usually exists in the form of distributed resources.

[0006] 3) If quantum computing tasks are executed through industrial internet nodes, their scheduling efficiency and execution performance will inevitably be strongly affected by network topology, communication latency and node load distribution.

[0007] Against this backdrop, how to efficiently and rationally schedule and allocate heterogeneous quantum computing resources in the scale-free topological environment of the Industrial Internet is foreseeably becoming a crucial issue restricting the large-scale application of quantum computing in industrial scenarios. However, existing quantum resource scheduling methods are scarce, and most employ simple rule-based allocation strategies or heuristic optimization models, typically focusing only on single-dimensional resource constraints. They fail to simultaneously characterize the highly uneven distribution of node importance in the scale-free network of the Industrial Internet, as well as the significant heterogeneity of quantum hardware capabilities. In practical applications, these methods often result in low quantum resource utilization, prominent bottlenecks in the computing power of hub nodes, and a lack of global optimality in the scheduling results, making it difficult to meet the urgent need for efficient quantum computing resource scheduling for complex industrial tasks. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to provide a heterogeneous quantum computing resource scheduling method, apparatus, device and medium to at least solve some of the problems of low utilization of existing quantum computing resources and lack of global optimality of scheduling results.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] In a first aspect, the present invention provides a method for scheduling heterogeneous quantum computing resources, comprising:

[0011] Scale-free modeling is performed based on the actual operational structure of the Industrial Internet to obtain the Industrial Internet network graph, and the hub nodes in the Industrial Internet network graph are obtained.

[0012] A capability model is performed on heterogeneous quantum computing resources, and decision variables for mapping the heterogeneous quantum computing resources to the hub node are constructed based on the capability model.

[0013] Based on the decision variables, construct a constraint model for the hub node and the heterogeneous quantum computing resources;

[0014] An objective function is constructed, an optimization model is established based on the constraint model and the objective function, the optimization model is solved using the simulated annealing algorithm, and the heterogeneous quantum computing resources are scheduled based on the solution results.

[0015] In an optional implementation, the step of obtaining an industrial internet network graph by performing scale-free modeling based on the actual operational structure of the industrial internet, and acquiring the hub nodes in the industrial internet network graph, includes:

[0016] The industrial nodes of the industrial internet are abstracted into a set of network nodes, and the communication or business dependencies between nodes are abstracted into a set of edges to obtain the industrial internet network graph.

[0017] Calculate and normalize the structural indicators of each node, and then select the hub node from the set of network nodes based on the normalized structural indicators.

[0018] In an optional implementation, the step of performing capability modeling on heterogeneous quantum computing resources includes:

[0019] The set of heterogeneous quantum computing resources available in the system is defined as follows:

[0020] ,

[0021] Each quantum processor The capability is described as a vector:

[0022] ,

[0023] in, The number of available qubits, For the quantum bit connectivity, For noise rate, The operating cost of quantum devices;

[0024] The step of constructing decision variables for mapping heterogeneous quantum computing resources to hub nodes based on the capability model includes:

[0025] Define the binary decision variables as:

[0026]

[0027] in As a hub node in a scale-free network, For quantum processors.

[0028] In an optional implementation, the step of constructing a constraint model of the hub node and the heterogeneous quantum computing resources based on the decision variables includes:

[0029] Construct a uniqueness constraint model for node tasks:

[0030]

[0031] in, This represents the penalty value for distributing a node task across multiple processors for computation;

[0032] Constructing a capacity constraint model for quantum processors:

[0033]

[0034] in, Quantum processor The total number of qubits allocated exceeds its inherent number. The penalty value, The quantum computing bit requirements for hub nodes;

[0035] Constructing a quantum resource uniqueness constraint model:

[0036]

[0037] in, This represents the penalty value for assigning multiple node tasks to a single quantum processor. , and The parameters are known.

[0038] In an optional implementation, the step of constructing the objective function and establishing an optimization model based on the constraint model and the objective function includes:

[0039] Construct the matching quality function:

[0040]

[0041] Constructing the cost function for running a quantum processor:

[0042]

[0043] Construct the communication cost function:

[0044]

[0045] in, Given the importance of hub nodes, Let be the adjacency matrix of the hub nodes. Measuring the importance between hub nodes , This represents the communication cost between quantum processors, with 0 for the same processor and 1 for multiple processors. , and The parameters are known.

[0046] The optimized model is:

[0047] .

[0048] In an optional implementation, the step of solving the optimization model using the simulated annealing algorithm includes:

[0049] make Expanding the optimization model yields:

[0050] ,

[0051] Using simulated annealing algorithm The solution is obtained by solving the problem.

[0052] In an optional implementation, the step of scheduling heterogeneous quantum computing resources based on the solution results includes:

[0053] Based on the solution results, the heterogeneous quantum computing resources are allocated to the designated hub nodes, and feedback information from the hub nodes is obtained.

[0054] The heterogeneous quantum computing resources are dynamically adjusted based on the feedback information.

[0055] Secondly, the present invention provides a heterogeneous quantum computing resource scheduling device, comprising:

[0056] The hub node selection module is used to perform scale-free modeling based on the actual operation structure of the industrial internet to obtain the industrial internet network graph, and to obtain the hub nodes in the industrial internet network graph.

[0057] The decision variable construction module is used to perform capability modeling on heterogeneous quantum computing resources and construct decision variables that map the heterogeneous quantum computing resources to the hub node based on the capability modeling.

[0058] A constraint model construction module is used to construct constraint condition models for the hub node and the heterogeneous quantum computing resources based on the decision variables;

[0059] The computational resource scheduling module is used to construct an objective function, establish an optimization model based on the constraint model and the objective function, solve the optimization model using the simulated annealing algorithm, and schedule heterogeneous quantum computing resources based on the solution results.

[0060] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing machine-executable instructions executable by the processor, the processor executing the machine-executable instructions to implement the heterogeneous quantum computing resource scheduling method described in the first aspect.

[0061] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the heterogeneous quantum computing resource scheduling method described in the first aspect.

[0062] This invention provides a heterogeneous quantum computing resource scheduling method, apparatus, device, and medium. By uniformly modeling various factors of network hub nodes and heterogeneous quantum resources, and solving the model using simulated annealing algorithm, an optimal or near-optimal quantum resource scheduling scheme is obtained. This scheme can fully consider the topological characteristics of scale-free networks and the differences in quantum hardware capabilities, avoid the resource waste caused by simple rule allocation, and ensure the efficient utilization of quantum resources in the industrial internet environment.

[0063] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0064] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 A block diagram of an electronic device provided by an embodiment of the present invention is shown;

[0066] Figure 2 A flowchart illustrating a heterogeneous quantum computing resource scheduling method provided in an embodiment of the present invention is shown.

[0067] Figure 3 A schematic diagram illustrating the principle of a heterogeneous quantum computing resource scheduling method provided by an embodiment of the present invention is shown;

[0068] Figure 4 The diagram illustrates a functional block diagram of a heterogeneous quantum computing resource scheduling device provided in an embodiment of the present invention.

[0069] icon:

[0070] 100 - Electronic device; 110 - Memory; 120 - Processor; 130 - Communication module; 400 - Heterogeneous quantum computing resource scheduling device; 410 - Hub node selection module; 420 - Decision variable construction module; 430 - Constraint model construction module; 440 - Computing resource scheduling module. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0072] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0073] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0074] Please refer to Figure 1 , Figure 1 This is a block diagram of an electronic device 100 provided in this embodiment. The electronic device 100 includes a memory 110, a processor 120, and a communication module 130. The memory 110, processor 120, and communication module 130 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0075] The memory 110 is used to store programs or data. The memory 110 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0076] The processor 120 is used to read / write data or programs stored in the memory 110 and to perform corresponding functions.

[0077] The communication module 130 is used to establish a communication connection between the electronic device 100 and other communication terminals through the network, and to send and receive data through the network.

[0078] It should be understood that, Figure 1 The structure shown is only a schematic diagram of the electronic device 100. The electronic device 100 may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown. Figure 1 The components shown can be implemented using hardware, software, or a combination thereof.

[0079] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating a heterogeneous quantum computing resource scheduling method provided in this embodiment. The method includes:

[0080] S210. Scale-free modeling is performed based on the actual operation structure of the Industrial Internet to obtain the Industrial Internet network graph, and the hub nodes in the Industrial Internet network graph are obtained.

[0081] Please refer to Figure 3 , Figure 3 This is a schematic diagram illustrating the principle of a heterogeneous quantum computing resource scheduling method provided in this embodiment.

[0082] The Industrial Internet is a new type of infrastructure, application model, and industrial ecosystem formed by the deep integration of next-generation information and communication technologies with the industrial economy. As an important cornerstone of the Fourth Industrial Revolution, it reshapes the industrial production and service system and promotes the digital, networked, and intelligent development of industries by comprehensively connecting people, machines, things, and systems.

[0083] In the industrial internet system, industrial nodes are the smallest independent units that constitute network connections. They can be physical entities with data acquisition and communication capabilities, or logical units that carry specific industrial functions. They can include device nodes, gateway nodes, terminal nodes, and logical nodes.

[0084] Considering the complexity and scale of the actual operation of the Industrial Internet system, industrial nodes can be abstracted into a set of network nodes:

[0085] ,

[0086] Abstract the communication or business dependencies between nodes into a set of edges:

[0087] ,

[0088] This allows us to construct an industrial internet network diagram:

[0089] ,

[0090] By statistically analyzing the node degree distribution, we can verify that the network satisfies the power-law distribution property:

[0091]

[0092] Then, the structural index for each node is calculated, and each structural index is normalized to the range of 0 and 1. The structural index includes the node degree. Intermediation Centrality Proximity centrality and PageRank value

[0093] The hub node set is selected based on node degree using either Top-K or percentage threshold methods:

[0094] ,

[0095] Then, the importance of a hub node is measured using a combination of node degree, two centralities, and PageRank:

[0096] ,

[0097] And Normalized to between 0.1 and 1.

[0098] Next, these structural metrics of the hub nodes are used to measure the number of quantum computing bits required by the nodes:

[0099] ,

[0100] Where parameters The value is between 0 and 1, and , The value depends on the actual quantum computing bit requirement, with a default setting of 5.

[0101] Then, the hub nodes in the industrial internet network diagram are obtained. In the node system of the industrial internet, the hub nodes are the key nodes that undertake the core functions of data aggregation, protocol conversion, routing and forwarding, local decision-making and collaborative scheduling.

[0102] S220. Perform capability modeling on heterogeneous quantum computing resources, and construct decision variables for mapping the heterogeneous quantum computing resources to the hub node based on the capability modeling.

[0103] Since heterogeneous quantum computing resources are limited, the key is to rationally allocate these limited resources. First, the capabilities of heterogeneous quantum computing resources can be modeled to simulate all available computing resources. Then, decision variables that map heterogeneous quantum computing resources to the hub nodes can be constructed to determine the initial allocation method of heterogeneous quantum computing resources.

[0104] S230. Construct a constraint model for the hub node and the heterogeneous quantum computing resources based on the decision variables.

[0105] In addition to allocating computing resources according to the needs of hub nodes, the number of quantum processors allocated to each hub node, and whether each quantum processor executes the tasks of one or multiple hub nodes, will affect the resource utilization of the Industrial Internet. Therefore, it is also necessary to build corresponding constraint models to simulate data such as resource loss under different computing resource allocation conditions.

[0106] S240. Construct an objective function, establish an optimization model based on the constraint model and the objective function, solve the optimization model using the simulated annealing algorithm, and schedule heterogeneous quantum computing resources based on the solution results.

[0107] In addition, the processing power of different quantum processors, as well as their operating and communication costs, are key factors affecting the utilization rate and cost of quantum computing resources. Based on these factors, a corresponding objective function can be constructed, and then an optimization model can be determined by combining the constraint model and the objective function. Based on the optimization model, heterogeneous quantum computing resources can be scheduled.

[0108] This embodiment obtains the optimal or near-optimal resource scheduling scheme by uniformly modeling various factors of network hub nodes and solving the model using the annealing algorithm. It can fully consider the topological characteristics of scale-free networks and the differences in quantum hardware capabilities, avoid the resource waste caused by simple rule allocation, and ensure the efficient utilization of quantum resources in the industrial Internet environment.

[0109] In one implementation, the step of performing capability modeling on heterogeneous quantum computing resources includes:

[0110] The set of heterogeneous quantum computing resources available in the system is defined as follows:

[0111] ,

[0112] Each quantum processor The capability is described as a vector:

[0113] ,

[0114] in, The number of available qubits, For the quantum bit connectivity, For noise rate, The operating cost of quantum devices;

[0115] The effective computing power of a quantum processor is defined as:

[0116] ,

[0117] in, , and For parameters.

[0118] The step of constructing decision variables for mapping heterogeneous quantum computing resources to hub nodes based on the capability model includes:

[0119] Define the binary decision variables as:

[0120]

[0121] in As a hub node in a scale-free network, For quantum processors, binary decision variables Used to represent the allocation relationship between quantum computing resources and industrial internet hub nodes.

[0122] In one implementation, the step of constructing a constraint model of the hub node and the heterogeneous quantum computing resources based on the decision variables includes:

[0123] Construct a uniqueness constraint model for node tasks:

[0124]

[0125] in, This represents the penalty value for distributing a node task across multiple processors for computation;

[0126] Constructing a capacity constraint model for quantum processors:

[0127]

[0128] in, Quantum processor The total number of qubits allocated exceeds its inherent number. The penalty value, The quantum computing bit requirements for hub nodes;

[0129] Constructing a quantum resource uniqueness constraint model:

[0130]

[0131] in, This represents the penalty value for assigning multiple node tasks to a single quantum processor. , and The parameters are known.

[0132] In one implementation, the step of constructing the objective function and establishing an optimization model based on the constraint model and the objective function includes:

[0133] Construct a matching quality function to maximize the use of high-capacity quantum processors by high-importance hub nodes:

[0134]

[0135] high The nodes are more important, high Quantum processors have stronger computing power.

[0136] Constructing the cost function for running a quantum processor:

[0137]

[0138] Construct the communication cost function:

[0139]

[0140] in, Given the importance of hub nodes, Let be the adjacency matrix of the hub nodes. Measuring the importance between hub nodes , This represents the communication cost between quantum processors, with 0 for the same processor and 1 for multiple processors. , and The parameters are known.

[0141] The optimized model is:

[0142] .

[0143] In one embodiment, the step of solving the optimization model using the simulated annealing algorithm includes:

[0144] make Expanding the optimization model yields:

[0145] ,

[0146] Using simulated annealing algorithm The solution is obtained by solving the problem.

[0147] In both quantum and classical computing, the Quadratic Unconstrained Binary Optimization (QUBO) model is commonly used to represent various combinatorial optimization problems. In this embodiment, the QUBO model can be solved using simulated annealing to obtain optimal or near-optimal solutions. Alternatively, quantum annealing can be used, which is expected to result in faster convergence and shorter solution time.

[0148] Quantum annealing machines utilize the tunneling effect of quantum mechanics to overcome local optima and find the global optimum. Classical annealing algorithms, on the other hand, simulate thermodynamic processes to gradually "cool" the system, reducing its energy and ultimately finding a near-optimal solution. The optimal solution to the problem is obtained through the annealing process. This solution includes the allocation of various quantum computing resources within the Industrial Internet network. For example, Indicates that quantum processors Assigned to industrial nodes If the task is not executed, it means that the resource has not been allocated.

[0149] In one implementation, the step of scheduling heterogeneous quantum computing resources based on the solution results includes:

[0150] Based on the solution results, the heterogeneous quantum computing resources are allocated to the designated hub nodes, and feedback information from the hub nodes is obtained.

[0151] The heterogeneous quantum computing resources are dynamically adjusted based on the feedback information.

[0152] Based on the optimal solution obtained from the annealing algorithm, quantum computing resources (such as quantum processors and qubits) are allocated to designated industrial internet hub nodes. Each hub node allocates resources according to its network centrality, task priority, and quantum computing capabilities (such as the number of available qubits and computing power).

[0153] After resource allocation, each quantum computing node will execute specific quantum computing tasks. These tasks may include: solving quantum optimization problems, such as using the annealing algorithm to solve specific optimization problems (e.g., graph coloring, traveling salesman problem, etc.); and quantum machine learning tasks, in which quantum nodes undertake part of the computational tasks of the neural network in a distributed quantum neural network architecture, collaboratively performing data processing, training, and inference.

[0154] During task execution, the industrial internet system needs to monitor the operational status of quantum computing resources in real time, including computation progress, accuracy of computation results, and hardware failures. Simultaneously, the system can dynamically adjust the scheduling results based on feedback from task execution to ensure the task is completed as expected.

[0155] To perform the corresponding steps in the above embodiments and various possible methods, an implementation of a heterogeneous quantum computing resource scheduling device is given below. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a functional block diagram of a heterogeneous quantum computing resource scheduling device provided in an embodiment of the present invention. It should be noted that the basic principle and technical effects of the heterogeneous quantum computing resource scheduling device provided in this embodiment are the same as those in the above embodiments. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above embodiments. The heterogeneous quantum computing resource scheduling device 400 includes:

[0156] The hub node selection module 410 is used to perform scale-free modeling based on the actual operation structure of the industrial internet to obtain the industrial internet network graph, and to obtain the hub nodes in the industrial internet network graph.

[0157] The decision variable construction module 420 is used to perform capability modeling on heterogeneous quantum computing resources and construct decision variables that map the heterogeneous quantum computing resources to the hub node based on the capability modeling.

[0158] The constraint model construction module 430 is used to construct a constraint condition model for the hub node and the heterogeneous quantum computing resources based on the decision variables;

[0159] The computing resource scheduling module 440 is used to construct an objective function, establish an optimization model based on the constraint model and the objective function, solve the optimization model using the simulated annealing algorithm, and schedule heterogeneous quantum computing resources based on the solution results.

[0160] Optionally, the above modules can be stored in the form of software or firmware. Figure 1 The memory shown is either stored in or embedded in the operating system (OS) of the electronic device, and can be used by... Figure 1 The processor executes the commands. Meanwhile, the data and program code required to execute these modules can be stored in memory.

[0161] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0162] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0163] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion 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 this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0164] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A heterogeneous quantum computing resource scheduling method, characterized in that, include: Scale-free modeling is performed based on the actual operational structure of the Industrial Internet to obtain the Industrial Internet network graph, and the hub nodes in the Industrial Internet network graph are obtained. A capability model is performed on heterogeneous quantum computing resources, and decision variables for mapping the heterogeneous quantum computing resources to the hub node are constructed based on the capability model. Based on the decision variables, construct a constraint model for the hub node and the heterogeneous quantum computing resources; An objective function is constructed, an optimization model is established based on the constraint model and the objective function, the optimization model is solved using the simulated annealing algorithm, and heterogeneous quantum computing resources are scheduled based on the solution results; The steps for capability modeling of heterogeneous quantum computing resources include: The set of heterogeneous quantum computing resources available in the system is defined as follows: ， Each quantum processor The capability is described as a vector: ， in, The number of available qubits, For the quantum bit connectivity, For noise rate, The operating cost of quantum devices; The step of constructing decision variables for mapping heterogeneous quantum computing resources to hub nodes based on the capability model includes: Define the binary decision variables as: in As a hub node in a scale-free network, For quantum processors; The step of constructing the constraint model of the hub node and the heterogeneous quantum computing resources based on the decision variables includes: Construct a uniqueness constraint model for node tasks: in, This represents the penalty value for distributing a node task across multiple processors for computation; Constructing a capacity constraint model for quantum processors: in, Quantum processor The total number of qubits allocated exceeds its inherent number. The penalty value, The quantum computing bit requirements for hub nodes; Constructing a quantum resource uniqueness constraint model: in, This represents the penalty value for assigning multiple node tasks to a single quantum processor; The steps of constructing the objective function and establishing an optimization model based on the constraint model and the objective function include: Construct the matching quality function: Constructing the cost function for running a quantum processor: Construct the communication cost function: in, Given the importance of hub nodes, Let be the adjacency matrix of the hub nodes. Measuring the importance between hub nodes , This represents the communication cost between quantum processors, with 0 for the same processor and 1 for multiple processors. The optimized model is: ； The step of solving the optimization model using the simulated annealing algorithm includes: make Expanding the optimization model yields: ， Using simulated annealing algorithm The solution is obtained by solving the problem.

2. The heterogeneous quantum computing resource scheduling method according to claim 1, characterized in that, The steps of obtaining an industrial internet network graph by performing scale-free modeling based on the actual operational structure of the industrial internet, and obtaining the hub nodes in the industrial internet network graph, include: The industrial nodes of the industrial internet are abstracted into a set of network nodes, and the communication or business dependencies between nodes are abstracted into a set of edges to obtain the industrial internet network graph. Calculate and normalize the structural indicators of each node, and then select the hub node from the set of network nodes based on the normalized structural indicators.

3. The heterogeneous quantum computing resource scheduling method according to claim 1, characterized in that, The step of scheduling heterogeneous quantum computing resources based on the solution results includes: Based on the solution results, the heterogeneous quantum computing resources are allocated to the designated hub nodes, and feedback information from the hub nodes is obtained. The heterogeneous quantum computing resources are dynamically adjusted based on the feedback information.

4. A heterogeneous quantum computing resource scheduling device, characterized in that, The apparatus for performing the heterogeneous quantum computing resource scheduling method as described in any one of claims 1-3 includes: The hub node selection module is used to perform scale-free modeling based on the actual operation structure of the industrial internet to obtain the industrial internet network graph, and to obtain the hub nodes in the industrial internet network graph. The decision variable construction module is used to perform capability modeling on heterogeneous quantum computing resources and construct decision variables that map the heterogeneous quantum computing resources to the hub node based on the capability modeling. A constraint model construction module is used to construct constraint condition models for the hub node and the heterogeneous quantum computing resources based on the decision variables; The computational resource scheduling module is used to construct an objective function, establish an optimization model based on the constraint model and the objective function, solve the optimization model using the simulated annealing algorithm, and schedule heterogeneous quantum computing resources based on the solution results.

5. An electronic device, characterized in that, It includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, the processor executing the machine-executable instructions to implement the heterogeneous quantum computing resource scheduling method according to any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the heterogeneous quantum computing resource scheduling method as described in any one of claims 1-3.

Images