A Quantum Entanglement Link Selection Method Based on the Epsilon-greedy Strategy

Abstract

The present invention discloses a method for selecting a quantum entanglement link based on the Epsilon-greedy strategy. First, various initial parameters in the process of selecting a quantum entanglement link are set, and the initial fidelity of all entanglement links is obtained by running a network benchmark test. The fidelity set is recorded and updated. Finally, iteration is performed based on the Epsilon-greedy strategy, and the link with the highest average fidelity between node A and node B is determined according to the iteration result, thus completing the selection of the quantum entanglement link. The solution of the present invention utilizes the Epsilon-greedy strategy in reinforcement learning to optimize the selection of quantum links and the estimation of fidelity through the balance of exploration and exploitation, updates the link fidelity estimation according to the network benchmark test results, so as to improve future link selection, not only can quickly identify the best quantum link, but also can provide accurate fidelity estimation under lower quantum resource consumption.

Description

Technical Field

[0001] The present invention belongs to the field of quantum networks, and particularly relates to a method for selecting quantum entanglement links based on the Epsilon - greedy strategy. Background Art

[0002] Quantum computing is more efficient than traditional computing in solving certain types of problems. For example, the security of public - key cryptosystems depends on the computational difficulty of integer factorization and discrete logarithm problems, while quantum computers can effectively crack these problems using Shor's algorithm. However, quantum computing technology is still in its infancy, and only small - scale quantum computers will be available in the foreseeable future. To overcome such limitations, people use quantum networks to connect many small - scale quantum computers to form a distributed processing system, similar to the cloud computing system of classical computers.

[0003] A quantum network consists of quantum nodes and quantum links connecting these nodes. To achieve long - distance qubit transmission, people rely on quantum entanglement, which is a phenomenon where multiple qubits are correlated and the state of a single qubit cannot be described independently of other qubits. Quantum entanglement is considered an important resource for transmitting quantum information. Once two quantum nodes share an entangled pair, they can transmit quantum information to each other through a process called quantum teleportation, regardless of their distance. However, due to the fragility of quantum information, qubits are easily decohered due to interactions with the environment. For example, the generated entangled pairs may not be perfectly entangled, and the attenuation of physical links and imperfect entanglement - swapping operations may cause damage during the long - distance entanglement establishment process. As a result, the established end - to - end entanglement may not be in the desired state and cannot be used for reliable quantum information transmission. Usually, people use fidelity to quantify the quality of an entanglement link (i.e., a pair of entangled qubits). The value of fidelity ranges from 0 to 1, and it measures the degree to which a quantum channel preserves quantum information. It is crucial to explicitly verify the quality of an entanglement link before transmitting important quantum information. Traditional methods estimate the fidelity of all entanglement links uniformly, which is not only resource - consuming but also inefficient. Therefore, there is an urgent need for a method that can effectively identify the best quantum entanglement link, estimate its fidelity, and reduce quantum resource consumption. Summary of the Invention

[0004] Aiming at the above problems, the purpose of the present invention is to provide a method for selecting quantum entanglement links based on the Epsilon - greedy strategy.

[0005] The specific technical solution for achieving the purpose of the present invention is as follows:

[0006] A method for selecting quantum entanglement links based on the Epsilon - greedy strategy, comprising the following steps:

[0007] Step 1: Set the initial parameters in the quantum entanglement link selection process;

[0008] Step 2: With the number of repetitions Run the network benchmark test to obtain the initial fidelity of all entanglement links, record and update the fidelity set ;

[0009] Step 3: Iterate based on the Epsilon-greedy strategy, and determine the link with the highest average fidelity between node A and node B according to the iteration result to complete the quantum entanglement link selection.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0011] The solution of the present invention utilizes the Epsilon-greedy strategy in reinforcement learning to optimize the selection of quantum links and the estimation of fidelity through the balance of exploration and exploitation, and updates the link fidelity estimation according to the network benchmark test results, thereby improving future link selection;

[0012] The present invention mainly focuses on testing the link with the highest known fidelity of quantum resources, while still using a small part of quantum resources to test links with lower fidelity. This method can not only quickly identify the best quantum link, but also provide accurate fidelity estimation with lower consumption of quantum resources.

[0013] The following further describes the present invention in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic flowchart of the quantum entanglement link selection method based on the Epsilon-greedy strategy of the present invention.

[0015] Figure 2 It is a schematic flowchart of the network benchmark test of the present invention.

[0016] Figure 3 It is a quantum network topology diagram in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Embodiment

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0019] As shown in this application and the claims, unless the context clearly indicates otherwise, the words "a", "an", "one", and / or "the" are not specific to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0020] Unless otherwise specifically stated, the relative arrangements of the components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of this application. At the same time, it should be understood that, for the sake of convenience of description, the dimensions of the various parts shown in the drawings are not drawn in actual proportional relationships. Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and devices should be regarded as part of the authorization specification. In all the examples shown and discussed here, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, further discussion thereof is not required in subsequent drawings.

[0021] The main objective of the present invention is to effectively estimate the fidelity of an established entanglement link. The method of the present invention is based on a method called network benchmarking, which can measure the average fidelity of a quantum entanglement link. However, network benchmarking is designed to measure a single quantum link. In the presence of multiple links with unknown fidelities (a common scenario in quantum communication), it is necessary to apply network benchmarking separately to each link, resulting in a relatively high cost. In practical applications, only a few high-fidelity quantum links are needed to transmit quantum information. An accurate fidelity estimate of low-fidelity links is unnecessary, and the corresponding benchmarking cost is actually a waste and can be partially saved. Therefore, it is necessary to identify high-fidelity links from a set of unknown links as early as possible so that an accurate fidelity estimate of the required high-quality links can be effectively obtained and quantum resources can be saved.

[0022] To solve the problems of link selection and fidelity estimation, the present invention formulates them as the best arm identification problem, which is a classic sequential decision-making task in the K-armed bandit. The K-armed bandit is a very famous framework with extensive applications in various fields, such as crowd sensing, opportunistic channel access, and social networks. Specifically, each arm corresponds to an entangled link in the link set, and each arm is associated with an unknown random reward representing the link fidelity. The network benchmarking method needs to transmit qubits multiple times through the entangled links to obtain the fidelity estimation, which means that the reward or feedback can only be obtained after pulling the arm multiple times. The objective of the present invention is to identify the link with the highest fidelity from the set of entangled links and obtain its fidelity estimation while consuming as few quantum resources as possible. To achieve this goal, the present invention designs a quantum entangled link selection algorithm based on the Epsilon-Greedy strategy. This algorithm optimizes future operations based on past benchmarking results. To evaluate the algorithm of the present invention, the embodiments of this application simulate a quantum network as Figure 3 as shown, Figure 3 in which, A and B respectively represent quantum routers, that is, node A and node B. The quantum communication terminals are on the left side of node A and the right side of node B. The solid lines in the figure represent quantum links, and the dashed lines represent entangled links; the embodiments of this application abstract away the irrelevant network topologies between node A and node B, only assuming that there are multiple entangled links and connecting through multiple entangled links related to different noise levels. The objective of the present invention is to determine the link with the highest fidelity. The embodiments of this application use different noise models and link fidelity distributions for simulation. The results confirm that, compared with the existing methods, this algorithm significantly reduces the quantum resource consumption cost of the total benchmarking.

[0023] Combined with Figure 1 and Figure 2 , a quantum entangled link selection method based on the Epsilon-greedy strategy includes the following steps:

[0024] Step 1, set the initial parameters in the quantum entangled link selection process, including:

[0025] The set of bounce times of the network benchmarking to be performed by node A and node B , the number of repetitions in the benchmarking , the number of repetitions of the network test subroutine in the quantum entangled link selection iteration process , the total quantum resource consumption ;

[0026] Among them, , in this embodiment, a large amount of quantum resources will not be spent in the initialization process to uniformly estimate the fidelity of all links. Compared with the number of repetitions of the original network benchmarking method, set The number of repetitions of the network benchmark test ;

[0027] Step 2: Run the network benchmark test with the number of repetitions to obtain the initial fidelity of all entangled links, record and update the fidelity set ;

[0028] Among them, the link selection rule of the network benchmark test is:

[0029]

[0030] In this embodiment , that is, in the iterative process, the algorithm of the present invention tests the link with the highest current average fidelity with a probability of 90%, and at the same time randomly tests the links in the set with a probability of 10%;

[0031] Step 3: Iterate based on the Epsilon - greedy strategy, and determine the link with the highest average fidelity between node A and node B according to the iteration result to complete the selection of quantum entanglement links:

[0032] Step 3 - 1: Generate a random number , and judge whether the random number is less than the set greedy parameter . If , then randomly select a link from the entangled link set and denote it as entangled link i. Otherwise, select the link with the highest current fidelity in the entangled link set and denote it as entangled link i;

[0033] Step 3 - 2: Prepare a quantum state on node A;

[0034] Step 3 - 3: Apply a random quantum gate to the quantum state on node A to generate a new quantum state ; Transmit the quantum state on node A to node B through entangled link i, and after receiving the quantum state, node B obtains the quantum state ;

[0035] Step 3 - 4: Apply a random quantum gate to the received quantum state on node B to obtain a new quantum state ; Transmit the quantum state on node B to node A through entangled link i, and after receiving the quantum state, node A obtains ;

[0036] Step 3 - 5: Repeat Step 3 - 3 and Step 3 - 4 for a total of m times Each time, a quantum state is transmitted between node A and node B, and a random quantum gate is applied respectively after each transmission. and ;

[0037] Step 3-6: Apply the inverse operation at node A , use a two-element to measure the quantum state at node A and record the measurement result , where E is a positive operator, POVM is the positive operator measurement function;

[0038] Step 3-7: For each bounce number m, repeat the number of tests times, calculate the average result of each test , collect the test data for different m values , calculate the fidelity by fitting :

[0039] Calculate the fidelity by fitting the data through the regression model ; ;

[0040] where is the initial purity of the quantum state.

[0041] Step 3-8: Use the fidelity as the average fidelity of link i, update the total quantum resource consumption , and store the average fidelity of link i into the fidelity set ;

[0042] Step 3-9: Repeat steps 3-1 to 3-9 until the set number of iterations C is reached, and based on the maximum value in the fidelity set at this time as the best link.

[0043] The solution of the present invention uses the Epsilon-greedy strategy in reinforcement learning to optimize the selection of quantum links and the estimation of fidelity through the balance of exploration and exploitation, updates the link fidelity estimation according to the network benchmark test results, so as to improve the future link selection, which can not only quickly identify the best quantum link, but also provide an accurate fidelity estimation with lower quantum resource consumption.

[0044] The present invention also provides a quantum entanglement link selection system based on the Epsilon-greedy strategy, including the following modules:

[0045] Parameter setting module: used to set each initial parameter in the quantum entanglement link selection process;

[0046] Network benchmarking module: used to run the network benchmarking with a repetition count to obtain the initial fidelity of all entangled links, record and update the fidelity set ;

[0047] Iterative optimization module: used to perform iterations based on the Epsilon-greedy strategy, and determine the link with the highest average fidelity between node A and node B according to the iteration results, thus completing the selection of quantum entanglement links.

[0048] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0049] Step 1: Set each initial parameter in the process of quantum entanglement link selection;

[0050] Step 2: Run the network benchmarking with a repetition count to obtain the initial fidelity of all entangled links, record and update the fidelity set ;

[0051] Step 3: Perform iterations based on the Epsilon-greedy strategy, and determine the link with the highest average fidelity between node A and node B according to the iteration results, thus completing the selection of quantum entanglement links.

[0052] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0053] Step 1: Set each initial parameter in the process of quantum entanglement link selection;

[0054] Step 2: Run the network benchmarking with a repetition count to obtain the initial fidelity of all entangled links, record and update the fidelity set ;

[0055] Step 3: Perform iterations based on the Epsilon-greedy strategy, and determine the link with the highest average fidelity between node A and node B according to the iteration results, thus completing the selection of quantum entanglement links.

[0056] The above embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A quantum entanglement link selection method based on Epsilon-greedy strategy, characterized in that: The following steps are involved: Step 1: Set the initial parameters in the quantum entanglement link selection process, including the number of bounces of the network benchmark test to be performed by nodes A and B. , the number of repetitions in the benchmark , the number of repetitions of the network test subroutine in the iterative process of quantum entanglement link selection , Total quantum resource consumption ; Step 2: Repeat Run the network benchmark to get the initial fidelity of all entangled links, record and update the fidelity set ; Step 3: Iterate based on the Epsilon-greedy strategy, determine the link with the highest average fidelity between node A and node B according to the iteration result, and complete the quantum entanglement link selection: Step 3-1: Generate random numbers , determine the random number Is it less than the set greed parameter? ,if , then from the entangled link set A link is randomly selected as entangled link i, otherwise, the entangled link set is selected The link with the highest current fidelity is recorded as entangled link i; Step 3-2: Prepare the quantum state at node A ; Step 3-3: On node A, the quantum state Applying random quantum gates , generating a new quantum state ; The quantum state on node A is transmitted to node B through the entanglement link i. After node B receives the quantum state, it obtains the quantum state ; Step 3-4: Receive the quantum state at node B Applying random quantum gates , and obtain a new quantum state ; The quantum state on node B is transmitted to node A through the entanglement link i. After node A receives the quantum state, it obtains ; Step 3-5: Repeat steps 3-3 and 3-4 m times. , each time the quantum state is transmitted between node A and node B, and a random quantum gate is applied after each transmission. and ; Step 3-6: Apply the inverse operation at node A , using two elements at node A Measure the quantum state and record the measurement results , where E is a positive operator, POVM is the positive operator measurement function; Step 3-7: For each number of bounces m, repeat the test times times, calculating the average result of each test , collect test data for different m values , the fidelity is calculated by fitting ; Step 3-8: Utilizing Fidelity As the average fidelity of link i , and update the total quantum resource consumption , and the average fidelity of link i Deposit to Fidelity Collection ; Step 3-9, repeat steps 3-1 to 3-9 until the set number of iterations C is reached. Based on the fidelity set at this time The maximum value among them is taken as the best link.

2. The method for selecting a quantum entanglement link based on the Epsilon-greedy strategy according to claim 1, characterized in that: Fidelity of steps 3-7 for: Through regression model Fitting data to calculate fidelity ; in is the initial purity of the quantum state.

3. The quantum entanglement link selection method based on the Epsilon-greedy strategy according to claim 1 is characterized in that: The link selection rule for the network benchmark test in step 2 is: 。 4. A quantum entanglement link selection system based on Epsilon-greedy strategy, used to execute the method of claim 1, characterized in that: Includes the following modules: Parameter setting module: used to set the initial parameters in the quantum entanglement link selection process; Network benchmark module: used to test the network with the number of repetitions Run the network benchmark to get the initial fidelity of all entangled links, record and update the fidelity set ; Iterative optimization module: used to iterate based on the Epsilon-greedy strategy, determine the link with the highest average fidelity between node A and node B according to the iteration results, and complete the quantum entanglement link selection.

5. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer storable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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