Quantum network path selection and resource allocation method and system for realizing high throughput

Abstract

The invention discloses a quantum network path selection and resource allocation method and system for realizing high throughput, and the method comprises the steps: initializing global variables and network parameters at least comprising a network topology structure, a request number and the maximum capacity of edges through building a quantum network model; setting the initial capacity of each edge, adjusting the capacity according to the fidelity of the edge, and removing the edge of which the fidelity is less than 0.5; designing a path selection algorithm, and selecting an optimal path by using the characteristics of the quantum state; constructing a resource allocation strategy based on quantum entanglement and quantum parallelism, and dynamically allocating network resources; and verifying the validity of the path selection and resource allocation method through the quantum network simulator. The resource allocation sequence is optimized, redundant operation in the resource allocation process is reduced, and therefore consumption of computing resources is reduced.

Description

Technical Field

[0001] The present invention relates to the field of quantum communication and network technologies, and particularly to a method and system for quantum network path selection and resource allocation to achieve high throughput. Background Art

[0002] With the rapid development of quantum technologies, quantum networks have gradually become an important part of future communication networks. Quantum networks utilize technologies such as quantum entanglement, quantum teleportation, and quantum encryption to enable efficient and secure communication. However, the practical applications of quantum networks also face many challenges, especially in path selection and network resource allocation. Different from traditional classical networks, quantum networks need to consider multiple factors such as the capacity limit of quantum channels, quantum fidelity, entanglement generation rate, and the scarcity of quantum resources. These characteristics make the path selection and resource allocation problems in quantum networks more complex, and also pose new challenges to existing communication and security protocols. Most of the existing quantum network path selection and resource allocation methods are based on classical network strategies and fail to fully utilize the unique characteristics of quantum networks, such as quantum entanglement and quantum parallelism. In addition, the existing methods often do not fully consider the limitations of network resources and the need for dynamic adjustment. Therefore, it is difficult to make timely and effective responses when network conditions change. Current research mainly focuses on the design of entanglement routing in quantum networks, especially the path selection problem, whose goal is to satisfy the entanglement requests between users as much as possible. However, in addition to path selection, effective entanglement resource allocation is also crucial for supporting concurrent entanglement requests between multiple source-destination (S-D) pairs. Based on this, an efficient entanglement routing scheme is proposed in this paper. This scheme aims to maximize the utilization efficiency of resources while satisfying entanglement connection requests, thereby improving the overall performance of quantum networks. By optimizing path selection and resource allocation strategies, our scheme can better handle multi-user concurrent requests in quantum networks, reduce network congestion, and improve the security and reliability of communication. Summary of the Invention

[0003] The purpose of the present invention is to solve the above problems, and a method and system for quantum network path selection and resource allocation to achieve high throughput are designed.

[0004] Furthermore, in the above-mentioned method for quantum network path selection and resource allocation to achieve high throughput, the quantum network path selection and resource allocation method includes the following steps: Establish a quantum network model, initialize global variables and network parameters, including at least network topology, the number of requests, and the maximum capacity of edges; Set the initial capacity of each edge, adjust the capacity according to the fidelity of the edge, and remove the edges with a fidelity less than 0.5; Design a path selection algorithm to select the optimal path using the characteristics of quantum states; Based on quantum entanglement and quantum parallelism, construct a resource allocation strategy to dynamically allocate network resources; Verify the effectiveness of the path selection and resource allocation methods through a quantum network simulator.

[0005] Furthermore, in the above method for path selection and resource allocation in a quantum network for achieving high throughput, the adjustment of capacity according to the fidelity of the edge and removing the edges with a fidelity less than 0.5 includes: The expression for the fidelity on the edge is: .

[0006] Furthermore, in the above method for path selection and resource allocation in a quantum network for achieving high throughput, the construction of a resource allocation strategy based on quantum entanglement and quantum parallelism to dynamically allocate network resources includes: The constraint on the quantum channel capacity of each edge indirectly considers the capacity constraint of the node by restricting the number of paths and traffic allocation on each edge. The calculation equation for the qubit capacity constraint is expressed as: ; The qubit capacity constraint calculation equation ensures that in the time slot , for all SD pairs the total number of qubits of the selected paths assigned to the node does not exceed the qubit capacity of this node ; The calculation equation for the quantum channel capacity constraint is expressed as: ; The quantum channel capacity constraint calculation equation ensures that in the time slot , for all SD pairs the total number of qubits of the selected paths assigned to the edge does not exceed the quantum channel capacity of this edge .

[0007] Furthermore, in the above method for path selection and resource allocation in a quantum network for achieving high throughput, the construction of a resource allocation strategy based on quantum entanglement and quantum parallelism to dynamically allocate network resources further includes: Assume that the qubit allocation scheme on the path is , where represents the edge on the path of the path For the number of qubits allocated above, the entanglement success rate across the entire path is calculated by multiplying the success probabilities of each edge on the path, as shown in the following formula: ; where represents the success probability of establishing entanglement for an edge when qubits are allocated on the edge .

[0008] Furthermore, in the above method for quantum network path selection and resource allocation to achieve high throughput, the path selection algorithm utilizes the parallelism of quantum states and selects the optimal path by setting an objective function.

[0009] Furthermore, in the above method for quantum network path selection and resource allocation to achieve high throughput, the determinants of the objective function include the bottleneck capacity of the path, quantum fidelity, and entanglement generation rate: The bottleneck capacity of the path indicates that the minimum edge capacity on the path determines the upper limit of resource allocation for the path; Quantum fidelity means that the quantum fidelities of different links are different, and the path selects links with higher fidelity; Entanglement generation rate means that the entanglement generation rates of different links are different, and the path selection considers the entanglement generation rate and preferentially selects links with higher entanglement generation rate; The expression of the objective function is: ; where represents the average fidelity, represents the path bottleneck capacity, and represents the average entanglement generation rate, and is used to represent the relative weight of each metric.

[0010] Furthermore, in the above method for quantum network path selection and resource allocation to achieve high throughput, the path selection algorithm includes the MOPA multi-objective path optimization algorithm, and the optimization steps include: Initialize the path set; For each request, calculate the objective function value of the candidate paths and perform normalization; Select the path with the highest objective value and without any saturated edges on the path as the optimal path; Return the determined path set and the request failure set.

[0011] Furthermore, in the above method for quantum network path selection and resource allocation to achieve high throughput, the resource allocation strategy includes a two-stage resource optimization algorithm, and the optimization steps include: Initialize the network throughput and the traffic allocation for each request; The first stage: Maximize the number of successful requests; For each request, check whether the path exceeds the capacity limit of the edge; If not exceeded, mark it as a successful request; if exceeded, add the request ID to the set of failed requests; The second stage: Allocate traffic according to the remaining resources to optimize resource utilization; For each request, allocate traffic according to the remaining resources to ensure optimal utilization of resources; Return the set of failed requests, the total number of successful requests, and the set of traffic allocations.

[0012] Furthermore, in the above method for quantum network path selection and resource allocation to achieve high throughput, the quantum network simulator includes network topology generation, communication request generation, path selection, resource allocation, and performance evaluation: The network topology generation is to generate different types of network topology structures, including at least square, triangular, and hexagonal topologies; The communication request generation is to randomly generate communication requests to simulate the communication requirements in an actual network; The path selection and resource allocation is to perform path selection and resource allocation according to the input algorithm; The performance evaluation is to evaluate the performance of the algorithm, including at least throughput, weighted traffic, and resource utilization metrics.

[0013] Furthermore, in a quantum network path selection and resource allocation system for achieving high throughput, the quantum network path selection and resource allocation system includes the following modules: The quantum network model establishment module is used to establish a quantum network model, initialize global variables and network parameters, including at least network topology structure, number of requests, and maximum capacity of the edge; The edge capacity setting module is used to set the initial capacity of each edge, adjust the capacity according to the fidelity of the edge, and remove the edges with a fidelity less than 0.5; The optimal path selection module is used to design a path selection algorithm and select the optimal path using the characteristics of quantum states; The resource dynamic allocation module is used to construct a resource allocation strategy based on quantum entanglement and quantum parallelism and dynamically allocate network resources; The message verification module is used to verify the effectiveness of the path selection and resource allocation method through the quantum network simulator.

[0014] Its beneficial effects are as follows: establish a quantum network model, initialize global variables and network parameters, including at least network topology, number of requests, and maximum capacity of edges; set the initial capacity of each edge, adjust the capacity according to the fidelity of the edge, and remove edges with a fidelity less than 0.5; design a path selection algorithm to select the optimal path using the characteristics of quantum states; based on quantum entanglement and quantum parallelism, construct a resource allocation strategy to dynamically allocate network resources; verify the effectiveness of the path selection and resource allocation methods through a quantum network simulator.

[0015] 1. By introducing the characteristics of quantum entanglement and quantum parallelism, the present invention enables the quantum network to process multiple entangled connection requests simultaneously, significantly enhancing the parallel processing ability. Compared with traditional network resource allocation methods, the quantum resource allocation strategy adopted by the present invention reduces the complexity of resource allocation and greatly improves the resource allocation efficiency. At the same time, due to quantum parallelism, the present invention can complete complex resource allocation and large-scale request processing tasks in a shorter time, and is applicable to application scenarios in quantum networks that require high throughput and real-time response, such as distributed quantum computing and quantum communication networks. 2. By designing an efficient quantum network model and optimization algorithm, the present invention optimizes the sequence of resource allocation, reduces redundant operations in the resource allocation process, and thus reduces the consumption of computing resources. Compared with traditional resource allocation models, quantum network resource allocation significantly reduces energy consumption while maintaining high performance, meeting the requirements of quantum networks for efficient and low-carbon operation. 3. Through the superposition and entanglement characteristics of quantum states, the present invention can capture high-dimensional features and complex relationships in the quantum network, and extract more accurate network state information. At the same time, it can not only capture local network features, but also capture global network features through the quantum entanglement characteristic, improving the accuracy and adaptability of network resource allocation. 4. The present invention shows advantages of high precision and high efficiency under resource constraints, significantly improving the success rate in network resource allocation tasks, and being able to better balance the complexity of resource allocation and network performance, reducing the risk of resource allocation failure, and improving the performance of the network in complex environments. 5. The method proposed by the present invention has a positive effect in many aspects of quantum networks, such as distributed quantum computing, quantum communication, and quantum sensing. In terms of distributed quantum computing, it can efficiently allocate computing resources, optimize task scheduling, and improve the overall efficiency of the computing system. In terms of quantum communication, the present invention can improve the real-time response ability of the communication system and enhance the effect of quantum communication. In terms of quantum sensing, the present invention can efficiently process large-scale sensing data, achieve more accurate environmental monitoring and prediction, and provide strong support for the operation of quantum networks.

[0016] Compared with the prior art, by introducing the unique advantages of quantum computing, the present invention significantly improves the resource allocation efficiency, reduces the energy consumption, enhances the network state awareness ability, and improves the accuracy and adaptability of resource allocation. These beneficial effects enable the present invention to exhibit excellent performance in various application scenarios of quantum networks, providing strong technical support for the future operation and management of quantum networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention.

[0018] Figure 1 Schematic diagram of the first embodiment of a method for quantum network path selection and resource allocation to achieve high throughput in the embodiments of the present invention; Figure 2 Schematic diagram of the first embodiment of a system for quantum network path selection and resource allocation to achieve high throughput in the embodiments of the present invention; Figure 3 Schematic diagram of the initialization of quantum network path selection and resource allocation of a method for quantum network path selection and resource allocation to achieve high throughput in the embodiments of the present invention; Figure 4 Schematic diagram of quantum network path selection and resource allocation of a method for quantum network path selection and resource allocation to achieve high throughput in the embodiments of the present invention; Figure 5 Schematic diagram of quantum information communication of quantum network path selection and resource allocation of a method for quantum network path selection and resource allocation to achieve high throughput in the embodiments of the present invention; Figure 6 Experimental result graphs of MOPA and the shortest path algorithm and greedy algorithm for a method for quantum network path selection and resource allocation to achieve high throughput in the embodiments of the present invention, where (a) is a schematic diagram of throughput of a square network topology, (b) is a schematic diagram of throughput of a triangular network topology; (c) is a schematic diagram of throughput of a hexagonal network topology; (d) is a schematic diagram of weighted traffic sum of a square network topology; (e) is a schematic diagram of weighted traffic sum of a triangular network topology; (f) is a schematic diagram of weighted traffic sum of a hexagonal network topology; Figure 7 First comparison schematic diagram of the effects of TPROA and PF and PS algorithms for a method for quantum network path selection and resource allocation to achieve high throughput in the embodiments of the present invention, where (a) is a schematic diagram of throughput of a square network topology, (b) is a schematic diagram of throughput of a triangular network topology; (c) is a schematic diagram of throughput of a hexagonal network topology; Figure 8This is the second comparison diagram of the effects of the TPROA, PF, and PS algorithms for a method of quantum network path selection and resource allocation to achieve high throughput in an embodiment of the present invention. (a) is a schematic diagram of the weighted traffic sum of a square network topology; (b) is a schematic diagram of the weighted traffic sum of a triangular network topology; (c) is a schematic diagram of the weighted traffic sum of a hexagonal network topology; (d) is a schematic diagram of the resource allocation scheduling performance of a square network topology; (e) is a schematic diagram of the resource allocation scheduling performance of a triangular network topology; (f) is a schematic diagram of the resource allocation scheduling performance of a hexagonal network topology. Detailed implementation manners

[0019] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] Those skilled in the art of the present technology can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups.

[0021] The present invention will be specifically described below with reference to the accompanying drawings. As Figure 1 shown, a method for quantum network path selection and resource allocation to achieve high throughput, the quantum network path selection and resource allocation method includes the following steps: Step 101: Establish a quantum network model, initialize global variables and network parameters, including at least network topology structure, number of requests, and maximum capacity of edges; Step 102: Set the initial capacity of each edge, adjust the capacity according to the fidelity of the edge, and remove the edges with a fidelity less than 0.5; Specifically, in this embodiment, the expression of the fidelity on the edge is: .

[0022] Step 103: Design a path selection algorithm, and select the optimal path by using the characteristics of quantum states; Specifically, in this embodiment, the path selection algorithm uses the parallelism of quantum states to select the optimal path by setting an objective function.

[0023] Step 104: Based on quantum entanglement and quantum parallelism, construct a resource allocation strategy to dynamically allocate network resources; Specifically, in this embodiment, the resource allocation strategy is based on the characteristics of quantum entanglement, and through the superposition of quantum states and the generation of entangled states, dynamic allocation of resources is achieved.

[0024] Specifically, in this embodiment, the constraint on the quantum channel capacity of each edge indirectly considers the capacity constraint of the node by restricting the number of paths and traffic allocation on each edge. The calculation equation of the qubit capacity constraint is expressed as: ; The qubit capacity constraint calculation equation ensures that in the time slot the total number of qubits allocated to the paths selected by all SD pairs does not exceed the qubit capacity of the node to which the paths are allocated; ; The calculation equation of the quantum channel capacity constraint is expressed as: ; The quantum channel capacity constraint calculation equation ensures that in the time slot the total number of qubits allocated to the paths selected by all SD pairs does not exceed the quantum channel capacity of the edge to which the paths are allocated; .

[0025] Assume that the qubit allocation scheme on the path is where represents the number of qubits allocated to the edge of the path . Then the entanglement success rate of the entire path is calculated by multiplying the success probabilities of each edge on the path. The formula is as follows: ; where represents the success probability of establishing entanglement of the edge when qubits are allocated to the edge .

[0026] Step 105: Verify the effectiveness of the path selection and resource allocation method through a quantum network simulator.

[0027] Specifically, in this embodiment, the quantum network simulator can simulate the distribution of the quantum network and verify the effectiveness of the resource allocation and path selection methods.

[0028] Performance evaluation metrics include: Throughput (T): the number of entanglement requests that can be successfully processed per time slot; Weighted Flow (W): the weighted sum of the product of the path lengths and traffic of all paths; Utilization (U): the average utilization degree of all utilized link capacities in the network.

[0029] Its beneficial effects are as follows: establishing a quantum network model, initializing global variables and network parameters, including at least network topology, number of requests, and maximum capacity of edges; setting the initial capacity of each edge, adjusting the capacity according to the fidelity of the edge, and removing edges with a fidelity less than 0.5; designing a path selection algorithm to select the optimal path using the characteristics of quantum states; constructing a resource allocation strategy based on quantum entanglement and quantum parallelism to dynamically allocate network resources; verifying the effectiveness of the path selection and resource allocation methods through a quantum network simulator.

[0030] 1. By introducing the characteristics of quantum entanglement and quantum parallelism, the present invention enables the quantum network to process multiple entanglement connection requests simultaneously, significantly improving the parallel processing ability. Compared with traditional network resource allocation methods, the quantum resource allocation strategy adopted by the present invention reduces the complexity of resource allocation and greatly improves the resource allocation efficiency. At the same time, due to quantum parallelism, the present invention can complete complex resource allocation and large-scale request processing tasks in a shorter time, and is applicable to application scenarios in quantum networks that require high throughput and real-time response, such as distributed quantum computing and quantum communication networks; 2. By designing an efficient quantum network model and optimization algorithm, the present invention optimizes the sequence of resource allocation, reduces redundant operations in the resource allocation process, and thus reduces the consumption of computing resources. Compared with traditional resource allocation models, quantum network resource allocation significantly reduces energy consumption while maintaining high performance, meeting the requirements of quantum networks for efficient and low-carbon operation; 3. Through the superposition and entanglement characteristics of quantum states, the present invention can capture high-dimensional features and complex relationships in the quantum network, and extract more accurate network state information. At the same time, it can not only capture local network features, but also capture global network features through the entanglement characteristics of quantum states, improving the accuracy and adaptability of network resource allocation; 4. The present invention shows advantages of high precision and high efficiency under resource constraints, significantly improving the success rate in network resource allocation tasks, and being able to better balance the complexity of resource allocation and network performance, reducing the risk of resource allocation failure, and improving the performance of the network in complex environments; 5. The method proposed by the present invention plays a positive role in many aspects of quantum networks, such as distributed quantum computing, quantum communication, and quantum sensing. In terms of distributed quantum computing, it can efficiently allocate computing resources, optimize task scheduling, and improve the overall efficiency of the computing system. In terms of quantum communication, the present invention can enhance the real-time response ability of the communication system and strengthen the effect of quantum communication. In terms of quantum sensing, the present invention can efficiently process large-scale sensing data, achieve more accurate environmental monitoring and prediction, and provide strong support for the operation of quantum networks.

[0031] Compared with the prior art, by introducing the unique advantages of quantum computing, the present invention significantly improves the resource allocation efficiency, reduces energy consumption, enhances the network state perception ability, and improves the accuracy and adaptability of resource allocation. These beneficial effects enable the present invention to exhibit excellent performance in various application scenarios of quantum networks, providing powerful technical support for the future operation and management of quantum networks.

[0032] The above introduces the embodiments of a method for realizing high-throughput quantum network path selection and resource allocation of the present invention. Please refer to Figure 2 , in a system for realizing high-throughput quantum network path selection and resource allocation, the quantum network path selection and resource allocation system includes the following modules: A quantum network model establishment module, which is used to establish a quantum network model, initialize global variables and network parameters, including at least network topology structure, request quantity, and maximum capacity of edges; An edge capacity setting module, which is used to set the initial capacity of each edge, adjust the capacity according to the fidelity of the edge, and remove the edges with a fidelity less than 0.5; An optimal path selection module, which is used to design a path selection algorithm and select the optimal path by using the characteristics of quantum states; A resource dynamic allocation module, which is used to construct a resource allocation strategy based on quantum entanglement and quantum parallelism and dynamically allocate network resources; A message verification module, which is used to verify the effectiveness of the path selection and resource allocation method through a quantum network simulator.

[0033] Specifically, the present invention also includes the following implementation methods: In the present invention, how to select an effective entanglement swapping path for any source-destination (S-D) request. Specifically, we will solve the path selection problem in entanglement routing, assuming that the network topology and link capacity are known. In a quantum network, due to the differences in the capacity, fidelity, and transmission success rate of different links, the performance of a path when satisfying a request will be different. Therefore, when selecting a path, we not only need to consider the capacity and entanglement generation rate of the link, but also need to comprehensively consider multiple factors such as link capacity, entanglement fidelity, and transmission success rate to maximize the success rate of the request.

[0034] Quantum networks face a series of unique challenges: on the one hand, the non-perfection of the initialization process, the quantum decoherence effect, and the uneven distribution of edge capacities make path selection crucial in quantum communication. Path selection not only directly affects the link quality but also has a profound impact on subsequent quantum resource allocation. To establish an entangled connection between the source node and the destination node, qubits need to be allocated to each node on the selected path. Suppose the path The qubit allocation scheme on is , where represents the number of qubits allocated to the edge on the path . Then, the entanglement success rate of the entire path can be calculated by multiplying the success probabilities of each edge on the path, and the formula is as follows: (1); where represents the entanglement establishment success probability of the edge when qubits are allocated to the edge . It can be seen that the quantum entanglement generation rate has a non-negligible impact on the quality of the entangled connection. It should be noted that although we assume that only one entanglement connection request is initiated between each pair of source-destination nodes (SD pairs), it is straightforward to extend this model to the case where a single SD pair initiates multiple entanglement connection requests. In this case, we can regard each entanglement connection request as an independent SD pair, and each SD pair initiates one entanglement connection request.

[0035] For this reason, this study introduces an optimization function and proposes the QuantumMulti-ObjectivePathOptimizationAlgorithm (MOPA). By evaluating and ranking candidate paths, the optimal path is selected under the condition of satisfying the link capacity constraint, thereby improving the accuracy of path selection and the overall efficiency of the network. We collect each requested path in the path information set . For each edge along the path identified for the request , the information entry is added to the path information set , and its tuple is as follows: ; where is the path length, is the order of the edge in the path , is the path The traffic (initially set to 0 before traffic allocation).

[0036] The specific steps of path selection are as follows: First, determine a set of candidate paths for each request. Next, apply the optimization objective function to calculate the objective value of each path and normalize it. The specific form of the objective function is determined according to the specific requirements of the application. In this paper, we focus on the impact of path selection on traffic allocation and link quality, and use the average fidelity , the path bottleneck capacity , and the average entanglement generation rate , and use to represent the relative weights of each metric. The objective function can be expressed as: (5); By applying the weighted sum method, we can calculate the comprehensive fitness value of each path and sort the paths according to these fitness values. In this process, we particularly focus on the paths with the highest fitness values and no saturated edges on the path, and select them as the optimal paths. This method comprehensively considers various factors in the quantum network, enabling the selection of the most suitable paths in a complex network environment, improving the success rate of requests and the overall performance of the network. On the other hand, in the scenario of high-concurrency long-distance entanglement resource connection requests, there is often a significant gap between the available entanglement link resources in the quantum network and the actual resource requirements of the requests, which greatly limits the processing capacity of the quantum network under concurrent requests. This paper focuses on the constraints of the quantum channel capacity on each edge and indirectly considers the capacity constraints of nodes by restricting the number of paths and traffic allocation on each edge. The qubit capacity constraint can be expressed as: (2); This equation ensures that in time slot , the total number of qubits allocated to node by the paths selected by all SD pairs does not exceed the qubit capacity of this node .

[0037] Similarly, the quantum channel capacity constraint can be expressed as: (3); This inequality ensures that in time slot , the total number of qubits allocated to edge by the paths selected by all SD pairs does not exceed the quantum channel capacity of this edge .

[0038] Based on these challenges, this paper will focus on optimizing the path selection strategy and the allocation method of entanglement resources, so as to improve the network performance under the condition of limited network resources.

[0039] In response to this, we propose the Quantum·Two-Phase·Resource·Optimization·Algorithm (TPROA). This scheme first maximizes the network throughput, then determines the resource allocation scheme according to the remaining link resources, and finally realizes the overall resource optimization. The algorithm process is as follows: First, we initialize the network throughput and the traffic allocation for each request . Next, we perform phased traffic allocation: in the first phase, for each request not in the request failure set , we perform a round of traffic allocation based on the strategy and check whether all edges on the request path exceed the capacity limit. For the request paths whose edge capacities are not exceeded, we mark them as successful requests; for the requests whose edge capacities are exceeded, we add the requests to the request failure set so as to skip these requests in the second phase and update the corresponding edge attributes.

[0040] In the second phase, we perform reallocation based on the remaining resources on the edges to optimize the traffic allocation of the paths, ensuring the efficient use of resources. This gradually optimized strategy not only ensures the reasonable utilization of link capacities, but also improves the overall performance of the quantum network, enabling the network to better adapt to complex traffic demands, and achieving a double improvement in the fairness and efficiency of resource allocation.

[0041] Specifically, the present invention further includes: As shown in Figure 3 , Figure 4 , Figure 5 , a method for path selection and resource allocation in a quantum network with high throughput in an embodiment of the present invention includes the following steps; 1. Establish a quantum network model The quantum network model includes quantum nodes, quantum channels, and network topology. The performance parameters of quantum nodes include processing power, storage capacity, etc.; the transmission characteristics of quantum channels include channel capacity, transmission delay, etc.; the connection relationship of the network topology describes the connection mode and path between nodes; 2. Real-time monitor the resource status and communication requirements of the quantum network Through the network monitoring module, the usage of network resources and the detailed information of communication requests are collected in real time, providing a basis for resource allocation and path selection. The monitoring module can dynamically obtain the real-time state of the network, including but not limited to link capacity, node load, priority of communication requests, etc.; 3. Design a path selection algorithm As Figure 6 (a-c) and Figure 6 (d-f) show the designed path selection algorithm, which selects the optimal path by utilizing the characteristics of quantum states. The performance comparison of the MOPA algorithm with two benchmark algorithms (shortest path algorithm and greedy algorithm) in terms of throughput and weighted traffic sum is presented under different network topologies (square, triangle, hexagon) and request scale conditions: The purpose is to verify the effectiveness and superiority of the designed path selection algorithm in the quantum network by comparing and analyzing the performance of different algorithms, providing a scientific basis for quantum network path selection, and thus improving the overall performance of the quantum network.

[0042] Specifically, Figure 6 (a-c) shows that when the number of requests is small (50 - 100), the performance difference between the MOPA algorithm and the benchmark algorithms is not obvious. However, as the number of requests increases, the throughput of the MOPA algorithm is significantly higher than that of the two benchmark algorithms. This result indicates that the MOPA algorithm can better adapt to complex network topologies when dealing with high request loads, thereby improving throughput. Figure 6 (d-f) presents the comparison of the weighted traffic sum of the MOPA algorithm and the two benchmark algorithms when maximizing throughput under the same network scale and topology conditions. The weighted traffic sum is an important indicator to measure the utilization efficiency of network resources and reflects the effectiveness of the path selection strategy. It shows that under high request loads, the MOPA algorithm can achieve a higher weighted traffic sum, indicating that its path selection strategy is superior to the benchmark algorithms in terms of resource allocation efficiency and robustness.

[0043] Utilize the parallelism of quantum states to select the optimal path through a quantum search algorithm. The path selection algorithm needs to comprehensively consider the following factors: Path length: Select a path with a shorter length to reduce communication latency; Fidelity: Select a path with a higher fidelity to improve communication quality; Entanglement generation rate: Select a path with a higher entanglement generation rate to reduce communication latency.

[0044] The specific steps of the path selection algorithm are as follows: Initialize the path set; For each request, calculate the objective value of the candidate paths and perform normalization processing; Select the path with the highest target value and no saturated edges on the path as the optimal path; Return the determined path set and the request failure set.

[0045] 4. Based on quantum entanglement and quantum parallelism, construct a resource allocation strategy; As Figure 7 (a-c) shows the construction of a resource allocation strategy based on quantum entanglement and quantum parallelism to dynamically allocate network resources. It shows the impact of different resource allocation strategies on throughput and compares the performance of the TPROA algorithm proposed in this study with two common methods, PF (Progressive Filling) and PS (Proportional Share).

[0046] The purpose is to verify the effectiveness and superiority of the proposed TPROA algorithm in the quantum network by comparing and analyzing the performance of different resource allocation strategies, providing a scientific basis for quantum network resource allocation, and thus improving the overall performance of the quantum network.

[0047] Specifically, the PF algorithm achieves fair resource allocation by gradually increasing the path flow. In each calculation interval, the flow of all paths increases by 1 simultaneously until the capacity of a certain edge is fully occupied (i.e., saturated). Once the edge is saturated, the paths passing through this edge will stop increasing the flow, and this process continues until the flow on all paths can no longer increase. The PS algorithm realizes the rational utilization of resources by proportionally allocating the edge capacity. On each edge, the algorithm allocates the edge capacity to each path proportionally according to the capacity of the edge and the weights of all paths using this edge. The weight of a path is determined by the power of the path length. The shorter the path, the relatively higher its weight, and thus the relatively higher proportion of capacity it may be allocated. Although the differences in the final throughput of the three algorithms are not significant, due to the same optimization method adopted in the path selection stage, the results tend to be similar. However, the different resource allocation methods lead to slight performance differences. The resource allocation method in this study first performs a round of PF to initially allocate resources, and then further refines the allocation according to the remaining resources. This method can meet the request requirements while reasonably utilizing network resources, avoiding resource waste, and thus improving the overall performance.

[0048] In Figure 7In (a-c), it can be seen that the TPROA algorithm exhibits relatively stable throughput performance under different network topologies (square, triangle, hexagon). Especially under high request loads, its throughput advantage is more obvious. This indicates that the TPROA algorithm can better adapt to complex network environments, effectively handle large-scale requests. Compared with the PF and PS algorithms, the TPROA algorithm can more accurately balance the traffic of each path during the resource allocation process, avoid performance bottlenecks caused by uneven resource allocation, and thus achieve higher network throughput, providing strong support for the efficient operation of quantum networks.

[0049] By utilizing the superposition of quantum states and the generation of entangled states, dynamic resource allocation is realized, improving resource utilization and communication efficiency. The resource allocation strategy needs to consider the following factors: such as Figure 8 (a-c) and Figure 8 (d-f) show the construction of a resource allocation strategy based on quantum entanglement and quantum parallelism to dynamically allocate network resources. It details the impact of different resource allocation strategies on the weighted traffic sum and resource utilization. The aim is to verify the effectiveness and superiority of the proposed TPROA algorithm in quantum networks through comparative analysis of the performance of different resource allocation strategies, provide a scientific basis for quantum network resource allocation, and thereby enhance the overall performance of quantum networks.

[0050] Figure 8 (a-c) specifically shows the comparison of the TPROA algorithm with two common methods, PF and PS, in terms of the weighted traffic sum under different network topologies (square, triangle, hexagon). The weighted traffic sum is the weighted sum of the product of the path length and traffic of all paths. This metric comprehensively considers the path length and traffic distribution, reflecting the overall efficiency of network resource allocation. A higher weighted traffic sum indicates more reasonable resource allocation and better ability to meet communication requests in the network. The experimental results show that the TPROA algorithm achieves the optimal weighted traffic sum overall, especially when dealing with large-scale requests, its advantage is more significant. In contrast, the growth rate of the weighted traffic sum of the PF and PS methods is slower when the request scale increases, which indicates that the TPROA algorithm can utilize network resources more effectively, avoiding the limitations of the PF and PS methods in uneven resource allocation and handling large-scale requests. This result fully demonstrates the superiority of the TPROA algorithm in terms of resource allocation efficiency and can significantly improve the performance of quantum networks under high-load conditions.

[0051] Furthermore, Figure 8(d-f) show the comparison of resource utilization rates of three resource allocation methods, namely TPROA, PF, and PS, under different request scales. Resource utilization rate refers to the average utilization degree of all utilized link capacities in the network and is an important indicator to measure the efficiency of resource allocation. As the request scale increases, the resource utilization rates of the three methods generally increase. However, the TPROA algorithm achieves the optimal resource utilization rate overall. Especially when dealing with large-scale requests, it shows stronger scheduling performance advantages. It is worth noting that the PF algorithm shows significant fluctuations under medium-scale requests. This is because the PF algorithm may cause some links to quickly reach the capacity limit while other links are not fully utilized, thus affecting the overall resource utilization rate. In contrast, through dynamic adjustment of resource allocation, the TPROA algorithm can better balance the loads of each link, ensure the efficient use of network resources, and further improve the stability and reliability of the quantum network.

[0052] Edge capacity limit: Ensure that the traffic assigned to each edge does not exceed its capacity to avoid request failures caused by overloading.

[0053] Path feasibility: Prioritize the selection of feasible paths, that is, all edges on the path can meet the traffic requirements of the current request.

[0054] Request priority: Consider the priority of requests and give priority to satisfying high-priority requests to improve the overall service quality.

[0055] 5. Verify the effectiveness of the path selection and resource allocation methods through a quantum network simulator; The quantum network simulator can simulate the dynamic changes of the quantum network and verify the effectiveness and superiority of the proposed methods. The simulator can simulate different network topologies, communication requests, and resource allocation strategies to evaluate the performance of the methods.

[0056] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A method for quantum network path selection and resource allocation to achieve high throughput, characterized in that, The quantum network path selection and resource allocation method includes the following steps: Establish a quantum network model, initialize global variables and network parameters, including at least network topology structure, number of requests, and maximum capacity of edges; Set the initial capacity of each edge, adjust the capacity according to the fidelity of the edge, and remove the edges with a fidelity less than 0.5; Design a path selection algorithm to select the optimal path using the characteristics of quantum states; Based on quantum entanglement and quantum parallelism, construct a resource allocation strategy to dynamically allocate network resources; Verify the effectiveness of the path selection and resource allocation method through a quantum network simulator.

2. A method for quantum network path selection and resource allocation to achieve high throughput as claimed in claim 1, wherein The adjusting the capacity according to the fidelity of the edge and removing the edges with a fidelity less than 0.5 includes: The expression for the fidelity on the edge is: 。 3. A method for quantum network path selection and resource allocation to achieve high throughput as described in claim 1, characterized in that, The constructing a resource allocation strategy based on quantum entanglement and quantum parallelism to dynamically allocate network resources includes: The constraint on the quantum channel capacity of each edge, indirectly considering the capacity constraint of the node by restricting the number of paths and traffic allocation on each edge. The calculation equation for the qubit capacity constraint is expressed as: ； The quantum bit capacity constraint calculation equation ensures that in the time slot , all SD pairs for the selected path assigned to the node do not exceed the quantum bit capacity of the node ; The calculation equation for the quantum channel capacity constraint is expressed as: ； The quantum channel capacity constraint calculation equation ensures that in time slot , for all SD pairs , the selected path allocates the total number of qubits to the edge such that it does not exceed the quantum channel capacity of the edge .

4. A method for quantum network path selection and resource allocation to achieve high throughput, as described in claim 1, characterized in that The constructing a resource allocation strategy based on quantum entanglement and quantum parallelism to dynamically allocate network resources further includes: Hypothetical path The qubit allocation scheme on is where represents the number of qubits allocated on the edge of the path The entanglement success rate on the entire path is calculated by multiplying the success probabilities of each edge on the path, and the formula is as follows: ； Among them, indicates the successful probability of establishing entanglement of the edge when allocating qubits on the edge.

5. A method for quantum network path selection and resource allocation to achieve high throughput as described in claim 1, characterized in that, The path selection algorithm utilizes the parallelism of quantum states to select the optimal path by setting an objective function.

6. The method for quantum network path selection and resource allocation for achieving high throughput according to claim 5, characterized in that The determining factors of the objective function include the bottleneck capacity of the path, quantum fidelity, and entanglement generation rate: The bottleneck capacity of the path means that the minimum edge capacity on the path determines the upper limit of resource allocation for the path; Quantum fidelity indicates that the quantum fidelities of different links are different, and the path with a higher fidelity is selected; Entanglement generation rate indicates that the entanglement generation rates of different links are different, and the path selection considers the entanglement generation rate and preferentially selects the link with a higher entanglement generation rate; The expression of the objective function is: ； Among them represents the average fidelity, represents the path bottleneck capacity, and represents the average entanglement generation rate, and uses to represent the relative weight of each metric.

7. A method for quantum network path selection and resource allocation to achieve high throughput, as described in claim 1, characterized in that, The path selection algorithm includes the MOPA multi-objective path optimization algorithm, and the optimization steps include: Initialize the path set; For each request, calculate the objective function value of the candidate paths and perform normalization processing; Select the path with the highest objective value and no saturated edges on the path as the optimal path; Return the determined path set and the request failure set.

8. A method for quantum network path selection and resource allocation to achieve high throughput, as described in claim 1, characterized in that The resource allocation strategy includes a two-stage resource optimization algorithm, and the optimization steps include: Initialize the network throughput and the traffic allocation for each request; The first stage: maximize the number of successful requests; For each request, check whether the path exceeds the capacity limit of the edge; If not exceeded, mark it as a successful request; if exceeded, add the request ID to the request failure set; The second stage: perform traffic allocation according to the remaining resources to optimize resource utilization; For each request, perform traffic allocation according to the remaining resources to ensure optimal utilization of resources; Return the request failure set, the total number of successful requests, and the traffic allocation set.

9. A method for quantum network path selection and resource allocation to achieve high throughput as described in claim 1, characterized in that, The quantum network simulator includes network topology generation, communication request generation, path selection and resource allocation, and performance evaluation: The network topology generation is to generate different types of network topology structures, including at least square, triangular, and hexagonal topologies; The communication request generation is to randomly generate communication requests to simulate the communication requirements in an actual network; Path selection and resource allocation are based on the input algorithm for path selection and resource allocation; Performance evaluation is to evaluate the performance of the algorithm, including at least throughput, weighted traffic, and resource utilization metrics.

10. A quantum network path selection and resource allocation system for achieving high throughput, characterized in that, The quantum network path selection and resource allocation system includes the following modules: A quantum network model establishment module, which is used to establish a quantum network model, initialize global variables and network parameters, including at least network topology, number of requests, and maximum capacity of edges; An edge capacity setting module, which is used to set the initial capacity of each edge, adjust the capacity according to the fidelity of the edge, and remove the edges with a fidelity less than 0.5; An optimal path selection module, which is used to design a path selection algorithm and select the optimal path by using the characteristics of quantum states; A resource dynamic allocation module, which is used to construct a resource allocation strategy based on quantum entanglement and quantum parallelism and dynamically allocate network resources; A message verification module, which is used to verify the effectiveness of the path selection and resource allocation method through a quantum network simulator.

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