A quantum network path selection and resource allocation method and system for achieving high throughput

Abstract

This invention discloses a quantum network path selection and resource allocation method and system for achieving high throughput. The method establishes a quantum network model, initializes global variables and network parameters, including at least the network topology, the number of requests, and the maximum capacity of an edge. The method then sets the initial capacity of each edge, adjusts the capacity based on the edge's fidelity, and removes edges with a fidelity less than 0.5. A path selection algorithm is designed to select the optimal path using the characteristics of quantum states. A resource allocation strategy is constructed based on quantum entanglement and quantum parallelism to dynamically allocate network resources. The effectiveness of the path selection and resource allocation method is verified using a quantum network simulator. This method optimizes the resource allocation sequence, reduces redundant operations during the resource allocation process, and thus reduces computing resource consumption.

Description

Technical Field

[0001] The present invention relates to the field of quantum communication and network technology, and in particular to a method and system for achieving high-throughput quantum network path selection and resource allocation. Background Art

[0002] With the rapid development of quantum technology, quantum networks are becoming an essential component of future communication networks. Quantum networks leverage technologies such as quantum entanglement, quantum teleportation, and quantum encryption to enable efficient and secure communication. However, the practical application of quantum networks faces numerous challenges, particularly in path selection and network resource allocation. Unlike traditional classical networks, quantum networks must simultaneously consider multiple factors, including the capacity limitations of quantum channels, quantum fidelity, entanglement generation rates, and the scarcity of quantum resources. These characteristics complicate path selection and resource allocation in quantum networks and pose new challenges to existing communication and security protocols. Existing quantum network path selection and resource allocation methods are mostly based on strategies from classical networks and fail to fully exploit the unique characteristics of quantum networks, such as quantum entanglement and quantum parallelism. Furthermore, existing methods often fail to fully consider network resource constraints and the need for dynamic adjustments, making it difficult to respond promptly and effectively to changing network conditions. Current research focuses on entanglement routing design in quantum networks, particularly path selection, which aims to maximize the satisfaction of entanglement requests between users. Beyond path selection, however, efficient entanglement resource allocation is also crucial for supporting concurrent entanglement requests between multiple source-destination (SD) pairs. Based on this, this paper proposes an efficient entanglement routing scheme. This scheme aims to satisfy entanglement connection requests while maximizing resource utilization, thereby improving the overall performance of quantum networks. By optimizing path selection and resource allocation strategies, our scheme can better handle concurrent multi-user requests in quantum networks, reduce network congestion, and improve communication security and reliability. Summary of the Invention

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

[0004] The technical solution of the present invention to achieve the above-mentioned purpose is that, further, in the above-mentioned method for quantum network path selection and resource allocation to achieve high throughput, the method for quantum network path selection and resource allocation includes the following steps:

[0005] Establish a quantum network model and initialize global variables and network parameters, including at least the network topology, number of requests, and maximum edge capacity;

[0006] Set the initial capacity of each edge, adjust the capacity according to the fidelity of the edge, and remove edges with fidelity less than 0.5;

[0007] Design a path selection algorithm to select the optimal path using the characteristics of quantum states;

[0008] Based on quantum entanglement and quantum parallelism, a resource allocation strategy is constructed to dynamically allocate network resources;

[0009] The effectiveness of the path selection and resource allocation methods is verified through a quantum network simulator.

[0010] Furthermore, in the above-mentioned method for achieving high-throughput quantum network path selection and resource allocation, adjusting the capacity according to the fidelity of the edges and removing edges with a fidelity less than 0.5 include:

[0011] The expression of edge fidelity is:

[0012] .

[0013] Furthermore, in the above-mentioned method for achieving high-throughput quantum network path selection and resource allocation, the resource allocation strategy is constructed based on quantum entanglement and quantum parallelism to dynamically allocate network resources, including:

[0014] The capacity constraint of the quantum channel on each edge indirectly considers the capacity constraint of the node by limiting the number of paths and traffic distribution on each edge. The quantum bit capacity constraint calculation equation is expressed as:

[0015] ;

[0016] The qubit capacity constraint calculation equation ensures that in the time slot In all SD pairs Selected Path Assign to node The total number of qubits does not exceed the qubit capacity of the node ;

[0017] The quantum channel capacity constraint calculation equation is expressed as:

[0018] ;

[0019] The quantum channel capacity constraint calculation equation ensures that In all SD pairs Selected Path Assign to edge The total number of quantum bits does not exceed the quantum channel capacity of this side .

[0020] Furthermore, in the above-mentioned method for achieving high-throughput quantum network path selection and resource allocation, the resource allocation strategy is constructed based on quantum entanglement and quantum parallelism, and network resources are dynamically allocated, further comprising:

[0021] Hypothetical Path The quantum bit allocation scheme on ,in Indicates that the path edge The number of qubits allocated on the path is calculated by multiplying the success probability of each edge on the path, as follows:

[0022] ;

[0023] in, Indicates on the edge Upper allocation When there are qubits, the probability of successful entanglement establishment is .

[0024] Furthermore, in the above-mentioned 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.

[0025] Furthermore, in the above-mentioned method for achieving high-throughput quantum network path selection and resource allocation, the determining factors of the objective function include the bottleneck capacity, quantum fidelity, and entanglement generation rate of the path:

[0026] The bottleneck capacity of the path represents the minimum edge capacity on the path, which determines the upper limit of the resource allocation of the path;

[0027] Quantum fidelity means that different links have different quantum fidelity, and the path selects the link with higher fidelity;

[0028] The entanglement generation rate indicates that different links have different entanglement generation rates. Path selection takes the entanglement generation rate into consideration, and links with higher entanglement generation rates are preferred.

[0029] The objective function expression is:

[0030] ;

[0031] in represents the average fidelity, represents the bottleneck capacity of the path and represents the average entanglement generation rate, and uses to indicate the relative weight of each metric.

[0032] Furthermore, in the above-mentioned method for achieving high-throughput quantum network path selection and resource allocation, the path selection algorithm includes a MOPA multi-objective path optimization algorithm, and the optimization steps include:

[0033] Initialize the path collection;

[0034] For each request, calculate the objective function value of the candidate path and perform normalization;

[0035] Select the path with the highest target value and no saturated edges as the optimal path;

[0036] Returns a set of confirmed paths and a set of request failures.

[0037] Furthermore, in the above-mentioned method for achieving high-throughput quantum network path selection and resource allocation, the resource allocation strategy includes a two-stage resource optimization algorithm, and the optimization steps include:

[0038] Initialize network throughput and traffic allocation for each request;

[0039] Phase 1: Maximize the number of successful requests;

[0040] For each request, check whether the path exceeds the edge capacity limit;

[0041] If it does not exceed the limit, mark it as a successful request; if it exceeds the limit, add the request ID to the request failure collection;

[0042] Phase 2: Allocate traffic based on remaining resources to optimize resource utilization;

[0043] For each request, traffic is allocated based on the remaining resources to ensure optimal resource utilization;

[0044] Returns a collection of failed requests, the total number of successful requests, and a collection of traffic distribution.

[0045] Furthermore, in the above-mentioned method for achieving high-throughput quantum network path selection and resource allocation, the quantum network simulator includes network topology generation, communication request generation, path selection and resource allocation, and performance evaluation:

[0046] The network topology generation is to generate different types of network topology structures, including at least square, triangle and hexagonal topologies;

[0047] Communication request generation is to randomly generate communication requests to simulate the communication needs in the actual network;

[0048] Path selection and resource allocation are performed based on the input algorithm;

[0049] Performance evaluation is to evaluate the performance of the algorithm, which at least includes throughput, weighted traffic and resource utilization indicators.

[0050] 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:

[0051] The quantum network model building module is used to build the quantum network model and initialize global variables and network parameters, including at least the network topology, number of requests, and maximum edge capacity;

[0052] 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 edges with a fidelity less than 0.5;

[0053] The optimal path selection module is used to design a path selection algorithm and select the optimal path using the characteristics of the quantum state;

[0054] Dynamic resource allocation module, which is used to build resource allocation strategies and dynamically allocate network resources based on quantum entanglement and quantum parallelism;

[0055] There is a message verification module for verifying the effectiveness of path selection and resource allocation methods through a quantum network simulator.

[0056] Its beneficial effects are to establish a quantum network model, initialize global variables and network parameters, including at least the 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 fidelity less than 0.5; design a path selection algorithm to select the optimal path using the characteristics of the quantum state; build a resource allocation strategy based on quantum entanglement and quantum parallelism to dynamically allocate network resources; and verify the effectiveness of the path selection and resource allocation methods through a quantum network simulator.

[0057] 1. By introducing the characteristics of quantum entanglement and quantum parallelism, the present invention enables quantum networks to process multiple entangled connection requests at the same time, significantly improving parallel processing capabilities. 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 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 suitable for application scenarios in quantum networks that require high throughput and real-time response, such as distributed quantum computing and quantum communication networks;

[0058] 2. This invention optimizes the resource allocation sequence by designing an efficient quantum network model and optimization algorithm, reducing redundant operations during the resource allocation process and thus reducing computing resource consumption. Compared with traditional resource allocation models, quantum network resource allocation significantly reduces energy consumption while maintaining high performance, meeting the quantum network's requirements for efficient and low-carbon operation.

[0059] 3. This invention, through the superposition and entanglement properties of quantum states, can capture high-dimensional features and complex relationships in quantum networks, extracting more accurate network state information. Furthermore, it can capture not only local network characteristics but also global network characteristics through quantum entanglement, improving the accuracy and adaptability of network resource allocation.

[0060] 4. The present invention demonstrates the advantages of high precision and high efficiency under resource-constrained conditions, significantly improving the success rate of network resource allocation tasks. It can also better balance the complexity of resource allocation and network performance, reducing the risk of resource allocation failure and improving network performance in complex environments.

[0061] 5. The method proposed in this invention has positive effects on multiple aspects of quantum networks, such as distributed quantum computing, quantum communication, and quantum sensing. In distributed quantum computing, it can efficiently allocate computing resources, optimize task scheduling, and improve the overall efficiency of computing systems. In quantum communication, this invention can enhance the real-time response capabilities of communication systems and strengthen the effectiveness of quantum communication. In quantum sensing, this invention can efficiently process large-scale sensor data, enabling more accurate environmental monitoring and prediction, and providing strong support for the operation of quantum networks.

[0062] Compared to existing technologies, this invention leverages the unique advantages of quantum computing to significantly improve resource allocation efficiency, reduce energy consumption, enhance network state awareness, and improve the precision and adaptability of resource allocation. These benefits enable this invention to demonstrate superior performance in a variety of quantum network applications, providing powerful technical support for future quantum network operation and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Various other advantages and benefits will become apparent to those skilled in the art by reading the following detailed description of the preferred embodiment.The accompanying drawings are only for the purpose of illustrating the preferred embodiment and are not to be considered as limiting the present invention.

[0064] Figure 1 Schematic diagram of a first embodiment of a method for quantum network path selection and resource allocation to achieve high throughput according to an embodiment of the present invention;

[0065] Figure 2This is a schematic diagram of a first embodiment of a quantum network path selection and resource allocation system for achieving high throughput according to an embodiment of the present invention;

[0066] Figure 3 Schematic diagram of initialization of quantum network path selection and resource allocation in a method for achieving high throughput in a quantum network path selection and resource allocation according to an embodiment of the present invention;

[0067] Figure 4 A schematic diagram of quantum network path selection and resource allocation for a method of achieving high throughput in a quantum network according to an embodiment of the present invention;

[0068] Figure 5 A schematic diagram of quantum information communication for quantum network path selection and resource allocation of a method for achieving high throughput quantum network path selection and resource allocation according to an embodiment of the present invention;

[0069] Figure 6 Figures 1 and 2 show experimental results of MOPA, the shortest path algorithm, and the greedy algorithm for a method for achieving high-throughput quantum network path selection and resource allocation in an embodiment of the present invention. (a) is a schematic diagram of the throughput of a square network topology, (b) is a schematic diagram of the throughput of a triangular network topology, (c) is a schematic diagram of the throughput of a hexagonal network topology, (d) is a schematic diagram of the weighted flow sum of a square network topology, (e) is a schematic diagram of the weighted flow sum of a triangular network topology, and (f) is a schematic diagram of the weighted flow sum of a hexagonal network topology.

[0070] Figure 7 Schematic diagram of the first comparison of the effects of TPROA, PF, and PS algorithms for a method for achieving high-throughput quantum network path selection and resource allocation in an embodiment of the present invention, wherein (a) is a schematic diagram of the throughput of a square network topology, (b) is a schematic diagram of the throughput of a triangular network topology, and (c) is a schematic diagram of the throughput of a hexagonal network topology.

[0071] Figure 8 This is a second comparative schematic diagram of the effects of TPROA, PF, and PS algorithms for a method for achieving high-throughput quantum network path selection and resource allocation in an embodiment of the present invention, (a) is a schematic diagram of the weighted traffic and sum of a square network topology; (b) is a schematic diagram of the weighted traffic and sum of a triangular network topology; (c) is a schematic diagram of the weighted traffic and sum of a hexagonal network topology; (d) is a schematic diagram of the resource allocation and scheduling performance of a square network topology; (e) is a schematic diagram of the resource allocation and scheduling performance of a triangular network topology; and (f) is a schematic diagram of the resource allocation and scheduling performance of a hexagonal network topology. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0073] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a," "an," "said," and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0074] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 As shown, a quantum network path selection and resource allocation method for achieving high throughput includes the following steps:

[0075] Step 101: Establish a quantum network model and initialize global variables and network parameters, including at least the network topology, number of requests, and maximum edge capacity.

[0076] Step 102: 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;

[0077] Specifically, the expression of edge fidelity in this embodiment is:

[0078] .

[0079] Step 103: Design a path selection algorithm to select the optimal path using the characteristics of the quantum state;

[0080] Specifically, the path selection algorithm in this embodiment utilizes the parallelism of quantum states and selects the optimal path by setting an objective function.

[0081] Step 104: Based on quantum entanglement and quantum parallelism, a resource allocation strategy is constructed to dynamically allocate network resources.

[0082] Specifically, the resource allocation strategy in this embodiment is based on the characteristics of quantum entanglement, and realizes dynamic allocation of resources through the superposition of quantum states and the generation of entangled states.

[0083] Specifically, in this embodiment, the quantum channel capacity constraint on each edge indirectly considers the node capacity constraint by limiting the number of paths and traffic distribution on each edge. The quantum bit capacity constraint calculation equation is expressed as:

[0084] ;

[0085] The qubit capacity constraint calculation equation ensures that in the time slot In all SD pairs Selected Path Assign to node The total number of qubits does not exceed the qubit capacity of the node ;

[0086] The quantum channel capacity constraint calculation equation is expressed as:

[0087] ;

[0088] The quantum channel capacity constraint calculation equation ensures that In all SD pairs Selected Path Assign to edge The total number of quantum bits does not exceed the quantum channel capacity of this side .

[0089] Hypothetical Path The quantum bit allocation scheme on ,in Indicates that the path edge The number of qubits allocated on the path is calculated by multiplying the success probability of each edge on the path, as follows:

[0090] ;

[0091] in, Indicates on the edge Upper allocation When there are qubits, the probability of successful entanglement establishment is .

[0092] Step 105: Verify the effectiveness of the path selection and resource allocation method through a quantum network simulator.

[0093] Specifically, the quantum network simulator in this embodiment can simulate the distribution of the quantum network and verify the effectiveness of the resource allocation and path selection methods.

[0094] Performance evaluation indicators include: Throughput (T): the number of entanglement requests that can be successfully processed in each time slot; Weighted Flow (W): the weighted sum of the product of the path length and flow of all paths; Resource Utilization (U): the average utilization of the capacity of all utilized links in the network.

[0095] Its beneficial effects are to establish a quantum network model, initialize global variables and network parameters, including at least the 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 fidelity less than 0.5; design a path selection algorithm to select the optimal path using the characteristics of the quantum state; build a resource allocation strategy based on quantum entanglement and quantum parallelism to dynamically allocate network resources; and verify the effectiveness of the path selection and resource allocation methods through a quantum network simulator.

[0096] 1. By introducing the characteristics of quantum entanglement and quantum parallelism, the present invention enables quantum networks to process multiple entangled connection requests at the same time, significantly improving parallel processing capabilities. 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 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 suitable for application scenarios in quantum networks that require high throughput and real-time response, such as distributed quantum computing and quantum communication networks;

[0097] 2. This invention optimizes the resource allocation sequence by designing an efficient quantum network model and optimization algorithm, reducing redundant operations during the resource allocation process and thus reducing computing resource consumption. Compared with traditional resource allocation models, quantum network resource allocation significantly reduces energy consumption while maintaining high performance, meeting the quantum network's requirements for efficient and low-carbon operation.

[0098] 3. This invention, through the superposition and entanglement properties of quantum states, can capture high-dimensional features and complex relationships in quantum networks, extracting more accurate network state information. Furthermore, it can capture not only local network characteristics but also global network characteristics through quantum entanglement, improving the accuracy and adaptability of network resource allocation.

[0099] 4. The present invention demonstrates the advantages of high precision and high efficiency under resource-constrained conditions, significantly improving the success rate of network resource allocation tasks. It can also better balance the complexity of resource allocation and network performance, reducing the risk of resource allocation failure and improving network performance in complex environments.

[0100] 5. The method proposed in this invention has positive effects on multiple aspects of quantum networks, such as distributed quantum computing, quantum communication, and quantum sensing. In distributed quantum computing, it can efficiently allocate computing resources, optimize task scheduling, and improve the overall efficiency of computing systems. In quantum communication, this invention can enhance the real-time response capabilities of communication systems and strengthen the effectiveness of quantum communication. In quantum sensing, this invention can efficiently process large-scale sensor data, enabling more accurate environmental monitoring and prediction, and providing strong support for the operation of quantum networks.

[0101] Compared to existing technologies, this invention leverages the unique advantages of quantum computing to significantly improve resource allocation efficiency, reduce energy consumption, enhance network state awareness, and improve the precision and adaptability of resource allocation. These benefits enable this invention to demonstrate superior performance in a variety of quantum network applications, providing powerful technical support for future quantum network operation and management.

[0102] The above is an introduction to an embodiment of a method for quantum network path selection and resource allocation to achieve high throughput. Figure 2 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:

[0103] The quantum network model building module is used to build the quantum network model and initialize global variables and network parameters, including at least the network topology, number of requests, and maximum edge capacity;

[0104] 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 edges with a fidelity less than 0.5;

[0105] The optimal path selection module is used to design a path selection algorithm and select the optimal path using the characteristics of the quantum state;

[0106] Dynamic resource allocation module, which is used to build resource allocation strategies and dynamically allocate network resources based on quantum entanglement and quantum parallelism;

[0107] There is a message verification module for verifying the effectiveness of path selection and resource allocation methods through a quantum network simulator.

[0108] Specifically, the present invention also includes the following implementations:

[0109] This paper investigates how to select an efficient entanglement exchange path for any source-destination (SD) request. Specifically, we address the path selection problem in entanglement routing, assuming known network topology and link capacity. In quantum networks, due to differences in link capacity, fidelity, and transmission success rate, paths will perform differently in satisfying requests. Therefore, when selecting a path, we must not only consider link capacity and entanglement generation rate, but also comprehensively consider multiple factors, including link capacity, entanglement fidelity, and transmission success rate, to maximize the success rate of the request.

[0110] Quantum networks face a unique set of challenges. Initialization imperfections, quantum decoherence, and uneven distribution of edge capacity make path selection crucial in quantum communication. Path selection not only directly impacts link quality but also profoundly influences subsequent quantum resource allocation. To establish an entangled connection between source and destination nodes, qubits must be allocated to each node along the selected path.

[0111] Hypothetical Path The quantum bit allocation scheme on ,in Indicates that the path edge The number of qubits allocated on the path. Then, the entanglement success rate on the entire path can be calculated by multiplying the success probability of each edge on the path, as follows:

[0112] (1);

[0113] in, Indicates on the edge Upper allocation When qubits are present, the probability of successful entanglement establishment for that edge is . This shows that the quantum entanglement generation rate has a significant impact on the quality of the entanglement connection. It is important to note that although we assume that each source-destination node (SD pair) initiates only one entanglement connection request, it is straightforward to extend this model to the case where a single SD pair initiates multiple entanglement connection requests. In this case, each entanglement connection request can be treated as an independent SD pair, with each SD pair initiating an entanglement connection request.

[0114] To this end, this study introduces an optimization function and proposes the Quantum Multi-Objective Path Optimization Algorithm (MOPA). By evaluating and sorting candidate paths, the optimal path is selected under the condition of meeting the link capacity constraint, thereby improving the accuracy of path selection and the overall efficiency of the network. We collect the path information set for each request. For requests along Identified path Each edge of , the information entry Add to path information set The tuple is as follows:

[0115] ;

[0116] in is the path length, Is the path Middle side The order of Is the path The flow rate (before flow distribution, it is initially set to 0).

[0117] The specific steps of path selection are as follows: First, determine the candidate paths. Then, the optimization objective function is applied to calculate the target value of each path and normalize it. The specific form of the objective function depends on the specific requirements of the application. In this paper, we focus on the impact of path selection on traffic distribution and link quality, using the average fidelity , Path bottleneck capacity and the average entanglement generation rate and use To express the relative weight of each metric. The objective function can be expressed as

[0118] (5);

[0119] By applying the weighted sum method, we are able to calculate the comprehensive fitness value of each path and sort the paths according to these fitness values. In this process, we pay special attention to those paths with the highest fitness values ​​and without any saturated edges on the path, and select them as the optimal paths. This method takes into account multiple factors in the quantum network, so that it can select the most appropriate path in a complex network environment, improve the success rate of requests and the overall performance of the network. On the other hand, in the scenario of high-concurrency long-distance entangled resource connection requests, there is often a significant gap between the available entangled link resources of the quantum network and the resource requirements of the actual 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 the nodes by limiting the number of paths and traffic distribution on each edge. The quantum bit capacity constraint can be expressed as:

[0120] (2);

[0121] This equation ensures that in the time slot In all SD pairs Selected Path Assign to node The total number of qubits does not exceed the qubit capacity of the node .

[0122] Similarly, the quantum channel capacity constraint can be expressed as:

[0123] (3);

[0124] This inequality ensures that in the time slot In all SD pairs Selected Path Assign to edge The total number of quantum bits does not exceed the quantum channel capacity of this side .

[0125] Based on these challenges, this paper will focus on optimizing path selection strategies and allocating entanglement resources to improve network performance under limited network resource conditions.

[0126] To address this issue, we propose the Quantum Two-Phase Resource Optimization Algorithm (TPROA). This algorithm first maximizes network throughput, then determines resource allocation based on the remaining link resources, ultimately achieving overall resource optimization. The algorithm flow is as follows:

[0127] First, we initialize the network throughput and traffic distribution for each request Next, perform phased traffic distribution: In the first phase, for each In the request, we will conduct a round based on The traffic distribution of the policy is checked and all edges on the request path are checked to see if they exceed the capacity limit. For request paths that do not exceed the edge capacity, we mark them as successful requests; for requests that exceed the edge capacity, we mark them as failed requests. Add to request failure set , so that the second phase skips these requests and updates the corresponding edge attributes.

[0128] In the second phase, we redistribute remaining resources along the edges to optimize traffic distribution along the path and ensure efficient resource utilization. This gradual optimization strategy not only ensures the rational utilization of link capacity but also improves the overall performance of the quantum network, enabling the network to better adapt to complex traffic demands and achieving both fair and efficient resource allocation.

[0129] Specifically, the present invention also includes:

[0130] like Figure 3 、 Figure 4 、 Figure 5 As shown in FIG, a method for achieving high throughput quantum network path selection and resource allocation in an embodiment of the present invention includes the following steps:

[0131] 1. Establishing a quantum network model

[0132] The quantum network model includes quantum nodes, quantum channels, and network topology. The performance parameters of quantum nodes include processing power and storage capacity; the transmission characteristics of quantum channels include channel capacity and transmission delay; and the connection relationship of the network topology describes the connection method and path between nodes.

[0133] 2. Real-time monitoring of quantum network resource status and communication needs

[0134] The network monitoring module collects detailed information about network resource usage and communication requests in real time, providing a basis for resource allocation and path selection. The monitoring module can dynamically obtain the real-time status of the network, including but not limited to link capacity, node load, and priority of communication requests;

[0135] 3. Design path selection algorithm

[0136] like Figure 6 (ac) and Figure 6 The (df) in the figure shows a designed path selection algorithm that uses the characteristics of quantum states to select the optimal path. The performance comparisons of the MOPA algorithm with two baseline algorithms (the shortest path algorithm and the greedy algorithm) in terms of throughput and weighted traffic sum are shown for different network topologies (square, triangle, and hexagonal) and request scales:

[0137] Its purpose is to verify the effectiveness and superiority of the designed path selection algorithm in quantum networks by comparing and analyzing the performance of different algorithms, provide a scientific basis for quantum network path selection, and thus improve the overall performance of quantum networks.

[0138] Specifically, Figure 6 Figures (ac) show that when the number of requests is small (50-100), the performance difference between the MOPA algorithm and the baseline algorithms is not significant. However, as the number of requests increases, the throughput of the MOPA algorithm significantly exceeds that of the two baseline algorithms. This result demonstrates that the MOPA algorithm can better adapt to complex network topologies when handling high request loads, thereby improving throughput. Figure 6The figure (df) in Figure 3 compares the weighted traffic sum of the MOPA algorithm and two baseline algorithms in terms of maximizing throughput under the same network scale and topology. Weighted traffic sum is an important indicator of network resource utilization efficiency and reflects the effectiveness of the path selection strategy. This shows that under high request loads, the MOPA algorithm achieves a higher weighted traffic sum, indicating that its path selection strategy outperforms the baseline algorithms in terms of resource allocation efficiency and robustness.

[0139] By utilizing the parallelism of quantum states, the optimal path is selected through a quantum search algorithm. The path selection algorithm needs to comprehensively consider the following factors:

[0140] Path length: Select a path with a shorter path length to reduce communication delay.

[0141] Fidelity: Select a path with higher fidelity to improve communication quality.

[0142] Entanglement generation rate: Select a path with a higher entanglement generation rate to reduce communication delay.

[0143] The specific steps of the path selection algorithm are as follows:

[0144] Initialize the path collection.

[0145] For each request, the target value of the candidate path is calculated and normalized.

[0146] The path with the highest target value and no saturated edges is selected as the optimal path.

[0147] Returns a set of confirmed paths and a set of request failures.

[0148] Build resource allocation strategies based on quantum entanglement and quantum parallelism;

[0149] like Figure 7 Figures (a to c) show a resource allocation strategy based on quantum entanglement and quantum parallelism, dynamically allocating network resources. The results show the impact of different resource allocation strategies on throughput and compare the performance of the proposed TPROA algorithm with two common methods: Progressive Filling (PF) and Proportional Share (PS).

[0150] Its purpose is to verify the effectiveness and superiority of the proposed TPROA algorithm in quantum networks by comparing and analyzing the performance of different resource allocation strategies, provide a scientific basis for quantum network resource allocation, and thus improve the overall performance of quantum networks.

[0151] Specifically, the PF algorithm achieves fair resource allocation by gradually increasing the flow rate on each path. During each computation interval, the flow rate on all paths increases by 1 until the capacity of an edge is fully utilized (i.e., saturated). Once an edge is saturated, the paths passing through that edge stop increasing their flow rate, and this process continues until all paths can no longer increase their flow rate. The PS algorithm, on the other hand, achieves fair resource utilization by allocating edge capacity proportionally. For each edge, the algorithm allocates the capacity proportionally to each path based on the edge's capacity and the weights of all paths utilizing that edge. Path weights are determined by the power of their length: shorter paths have higher weights and, therefore, a higher potential capacity allocation. Although the three algorithms exhibit minimal differences in final throughput, the results are similar due to the use of the same optimization method in the path selection phase. However, the differences in resource allocation methods lead to slight performance differences. The resource allocation method proposed in this study first performs a round of PF to preliminarily allocate resources, followed by further refined allocations based on the remaining resources. This method satisfies requests while rationally utilizing network resources and avoiding resource waste, thereby improving overall performance.

[0152] exist Figure 7 In Figures (ac), we can see that the TPROA algorithm exhibits relatively stable throughput performance under different network topologies (square, triangle, and hexagonal), and its throughput advantage is particularly evident under high request loads. This demonstrates that the TPROA algorithm is better able to adapt to complex network environments and effectively handle large-scale requests. Compared with the PF and PS algorithms, the TPROA algorithm can more accurately balance traffic across paths during resource allocation, avoiding performance bottlenecks caused by uneven resource allocation, thereby achieving higher network throughput and providing strong support for the efficient operation of quantum networks.

[0153] By utilizing the superposition of quantum states and the generation of entangled states, dynamic resource allocation is achieved, improving resource utilization and communication efficiency. The resource allocation strategy needs to consider the following factors:

[0154] like Figure 8 (ac) and Figure 8 The (df) in the figure shows a resource allocation strategy based on quantum entanglement and quantum parallelism to dynamically allocate network resources. The results show in detail the impact of different resource allocation strategies on weighted traffic and resource utilization. The goal is to verify the effectiveness and superiority of the proposed TPROA algorithm in quantum networks 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 quantum networks.

[0155] Figure 8Figures (ac) in the figure specifically compare the weighted traffic sum of the TPROA algorithm with two common methods, PF and PS, under different network topologies (square, triangle, and hexagonal). The weighted traffic sum is the weighted sum of the product of the path length and traffic of all paths. This metric comprehensively considers 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 within the network. Experimental results show that the TPROA algorithm achieves the optimal weighted traffic sum overall, especially when processing large-scale requests. In contrast, the weighted traffic sum of the PF and PS methods grows more slowly as the request scale increases, indicating that the TPROA algorithm can more efficiently utilize network resources and avoid 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 resource allocation efficiency and can significantly improve the performance of quantum networks under high load conditions.

[0156] Further, Figure 8 (df) in the figure shows the resource utilization comparison of the three resource allocation methods, TPROA, PF, and PS, under different request scales. Resource utilization refers to the average utilization of the capacity of all utilized links in the network and is an important indicator for measuring resource allocation efficiency. As the request scale increases, the resource utilization of the three methods generally increases. However, the TPROA algorithm achieves the best resource utilization overall, especially when processing large-scale requests, showing a stronger scheduling performance advantage. 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 their capacity limits, while other links are not fully utilized, thus affecting the overall resource utilization. In contrast, the TPROA algorithm can better balance the load of each link by dynamically adjusting resource allocation, ensuring the efficient utilization of network resources, and further improving the stability and reliability of the quantum network.

[0157] Edge capacity limit: Ensures that the traffic allocated to each edge does not exceed its capacity to avoid request failures due to overload.

[0158] Path feasibility: Prioritize feasible paths, that is, all edges on the path can meet the traffic requirements of the current request.

[0159] Request priority: Consider the priority of requests and give priority to high-priority requests to improve the overall service quality.

[0160] Verify the effectiveness of path selection and resource allocation methods using a quantum network simulator;

[0161] The quantum network simulator can simulate the dynamic changes of quantum networks and verify the effectiveness and superiority of the proposed method. The simulator can simulate different network topologies, communication requests, and resource allocation strategies to evaluate the performance of the method.

[0162] 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 to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention 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 comprises the following steps: Establish a quantum network model and initialize global variables and network parameters, including at least the network topology, number of requests, and maximum edge capacity; Set the initial capacity of each edge, adjust the capacity according to the fidelity of the edge, and remove edges with fidelity less than 0.5; A path selection algorithm is designed to select an optimal path using the characteristics of quantum states. The path selection algorithm utilizes the parallelism of quantum states to select the optimal path by setting an objective function. The determinants of the objective function include the bottleneck capacity, quantum fidelity, and entanglement generation rate of the path. 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. The quantum fidelity indicates that the quantum fidelity of different links is different, and the path selects the link with higher fidelity. The entanglement generation rate indicates that the entanglement generation rate of different links is different, and the path selection takes the entanglement generation rate into consideration, with the link with higher entanglement generation rate being preferentially selected. The objective function expression is: ； in represents the average fidelity, represents the bottleneck capacity of the path and represents the average entanglement generation rate, and uses To express the relative weight of each metric; Based on quantum entanglement and quantum parallelism, a resource allocation strategy is constructed to dynamically allocate network resources; The effectiveness of the path selection and resource allocation methods is verified through a quantum network simulator.

2. A method for achieving high throughput quantum network path selection and resource allocation according to claim 1, characterized in that: The resource allocation strategy is constructed based on quantum entanglement and quantum parallelism to dynamically allocate network resources, including: The capacity constraint of the quantum channel on each edge indirectly considers the capacity constraint of the node by limiting the number of paths and traffic distribution on each edge. The quantum bit capacity constraint calculation equation is expressed as: ； The qubit capacity constraint calculation equation ensures that in the time slot In all SD pairs Selected Path Assign to node The total number of qubits does not exceed the qubit capacity of the node ; The quantum channel capacity constraint calculation equation is expressed as: ； The quantum channel capacity constraint calculation equation ensures that In all SD pairs Selected Path Assign to edge The total number of quantum bits does not exceed the quantum channel capacity of this side .

3. The method for achieving high throughput quantum network path selection and resource allocation according to claim 1, wherein: The method of constructing a resource allocation strategy based on quantum entanglement and quantum parallelism and dynamically allocating network resources also includes: Hypothetical Path The quantum bit allocation scheme on ,in Indicates that the path edge The number of qubits allocated on the path is calculated by multiplying the success probability of each edge on the path, as follows: ； in, Indicates on the edge Upper allocation When there are qubits, the probability of successful entanglement establishment is .

4. A method for achieving high throughput quantum network path selection and resource allocation according to 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 collection; For each request, calculate the objective function value of the candidate path and perform normalization; Select the path with the highest target value and no saturated edges as the optimal path; Returns a set of confirmed paths and a set of request failures.

5. The method for achieving high-throughput quantum network path selection and resource allocation according to claim 1, wherein: The resource allocation strategy includes a two-stage resource optimization algorithm, and the optimization steps include: Initialize network throughput and traffic allocation for each request; Phase 1: Maximize the number of successful requests; For each request, check whether the path exceeds the edge capacity limit; If it does not exceed the limit, mark it as a successful request; if it exceeds the limit, add the request ID to the request failure collection; Phase 2: Allocate traffic based on remaining resources to optimize resource utilization; For each request, traffic is allocated based on the remaining resources to ensure optimal resource utilization; Returns a collection of failed requests, the total number of successful requests, and a collection of traffic distribution.

6. A method for achieving high throughput quantum network path selection and resource allocation according to claim 1, characterized in that: 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, triangle and hexagonal topologies; Communication request generation is to randomly generate communication requests to simulate the communication needs in the actual network; Path selection and resource allocation are performed based on the input algorithm; Performance evaluation is to evaluate the performance of the algorithm, which at least includes throughput, weighted traffic and resource utilization indicators.

7. 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: The quantum network model building module is used to build the quantum network model and initialize global variables and network parameters, including at least the network topology, number of requests, and maximum edge capacity; 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 edges with a fidelity less than 0.5; An optimal path selection module is used to design a path selection algorithm that uses the characteristics of quantum states to select the optimal path. The path selection algorithm uses the parallelism of quantum states to select the optimal path by setting an objective function. The determinants of the objective function include the bottleneck capacity, quantum fidelity, and entanglement generation rate of the path. The bottleneck capacity of the path indicates that the minimum edge capacity on the path determines the upper limit of the path's resource allocation. Quantum fidelity indicates that the quantum fidelity of different links is different, and the path selects the link with higher fidelity. The entanglement generation rate indicates that the entanglement generation rate of different links is different, and the path selection takes into account the entanglement generation rate, and the link with higher entanglement generation rate is preferentially selected. The objective function expression is: ； in represents the average fidelity, represents the bottleneck capacity of the path and represents the average entanglement generation rate, and uses To express the relative weight of each metric; Dynamic resource allocation module, which is used to build resource allocation strategies and dynamically allocate network resources based on quantum entanglement and quantum parallelism; There is a message verification module for verifying the effectiveness of path selection and resource allocation methods through a quantum network simulator.

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