Quantum entanglement link selection method based on Epsilone-greedy strategy

Abstract

The invention discloses a quantum entanglement link selection method based on an Epsilone-greedy strategy, and the method comprises the steps: firstly setting each initial parameter in a quantum entanglement link selection process, operating a network benchmark test to obtain the initial fidelity of all entanglement links, recording and updating a fidelity set, and finally carrying out the iteration based on the Epsilone-greedy strategy to obtain a quantum entanglement link selection result. And determining a link with the highest average fidelity between the node A and the node B according to an iteration result, and completing quantum entanglement link selection. According to the scheme, an Epsilone-greedy strategy in reinforcement learning is utilized, selection and fidelity estimation of the quantum link are optimized through the balance of exploration and utilization, and link fidelity estimation is updated according to a network benchmark test result, so that future link selection is perfected, the optimal quantum link can be quickly identified, and the reliability of the quantum link is improved. And accurate fidelity estimation can be provided under relatively low quantum resource consumption.

Description

Technical Field

[0001] The invention belongs to the field of quantum networks, and in particular relates to a quantum entanglement link selection method based on an Epsilon-greedy strategy. Background Art

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

[0003] Quantum networks consist of quantum nodes and quantum links connecting these nodes. To achieve long-distance qubit transmission, people rely on quantum entanglement, a phenomenon in which 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 can easily decoherent due to interactions with the environment. For example, the generated entangled pairs may not be perfectly entangled, and the attenuation of the physical link and imperfect entanglement exchange operations may cause damage in 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. Typically, people use fidelity to quantify the quality of an entangled 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 the entangled link before transmitting important quantum information. Traditional methods estimate the fidelity of all entangled links uniformly, which is not only resource-intensive but also inefficient. Therefore, there is an urgent need for a method that can effectively identify the best quantum entangled links and estimate their fidelity while reducing quantum resource consumption. Summary of the invention

[0004] In view of the above problems, the object of the present invention is to provide a quantum entanglement link selection method based on the Epsilon-greedy strategy.

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

[0006] A quantum entanglement link selection method based on Epsilon-greedy strategy includes the following steps:

[0007] Step 1, setting the initial parameters in the quantum entanglement link selection process;

[0008] Step 2: Repeat Run the network benchmark to get the initial fidelity of all entangled links, record and update the fidelity set ;

[0009] 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 results, and complete the quantum entanglement link selection.

[0010] Compared with the prior art, the present invention has the following beneficial effects:

[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 between exploration and utilization, and updates the link fidelity estimation according to the network benchmark test results, thereby improving future link selection;

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

[0013] The present invention is further described below in conjunction with specific implementation modes. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of the quantum entanglement link selection method based on the Epsilon-greedy strategy of the present invention.

[0015] Figure 2 The figure is a schematic diagram of the network benchmark test flow of the present invention.

[0016] Figure 3 This is a quantum network topology diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] Example

[0018] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an kind" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" 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 arrangement, numerical expressions and numerical values ​​of the parts and steps set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to the actual proportional relationship. The technology, method and equipment known to those of ordinary skill in the relevant field may not be discussed in detail, but in appropriate cases, the technology, method and equipment should be considered as a part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so that once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0021] The main goal of the present invention is to effectively estimate the fidelity of an established entangled link. The method of the present invention is based on a method called network benchmarking, which can measure the average fidelity of a quantum entangled link. However, network benchmarking is designed to measure a single quantum link. In the presence of multiple links of unknown fidelity (a common scenario in quantum communication), network benchmarking needs to be applied separately to each link, resulting in a fairly high cost. In practical applications, only a few high-fidelity quantum links need to be selected to transmit quantum information. Accurate fidelity estimates of low-fidelity links are unnecessary, and the corresponding benchmarking costs are 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 accurate fidelity estimates of the required high-quality links can be effectively obtained and quantum resources can be saved.

[0022] In order to solve the problem of link selection and fidelity estimation, the present invention formulates it as the best arm identification problem, which is a classic sequential decision task in the K-arm bandit. The K-arm bandit is a very well-known framework with wide applications in various fields, such as crowd sensing, opportunistic channel access, and social networks. Specifically, each arm corresponds to an entangled link in a link set, and each arm is associated with an unknown random reward representing the fidelity of the link. Network benchmarking methods require multiple transmissions of quantum bits through entangled links to obtain fidelity estimates, which means that rewards or feedback can only be obtained after pulling the arms multiple times. The goal of the present invention is to identify the link with the highest fidelity from the set of entangled links and obtain its fidelity estimate 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. The algorithm optimizes future operations based on past benchmark results. In order to evaluate the algorithm of the present invention, an embodiment of the present application simulates a quantum network such as Figure 3 As shown, Figure 3 In the figure, A and B represent quantum routers, i.e., node A and node B, respectively. The left side of node A and the right side of node B are quantum communication terminals. The solid line in the figure represents the quantum link, and the dotted line represents the entangled link. The embodiment of the present application abstracts the irrelevant network topology between node A and node B, and only assumes that there are multiple entangled links, which are connected by multiple entangled links associated with different noise levels. The goal of the present invention is to determine the link with the highest fidelity. The embodiment of the present application uses different noise models and link fidelity distributions for simulation. The results confirm that the algorithm significantly reduces the quantum resource consumption cost of the total benchmark test compared with existing methods.

[0023] Combination Figure 1 and Figure 2 , a quantum entanglement link selection method based on Epsilon-greedy strategy, comprising the following steps:

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

[0025] The set of bounce times for the network benchmark test to be performed by Node A and Node 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 ;

[0026] in, 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 original network benchmark test method, the number of repetitions is ,set up , Network benchmark test repetitions ;

[0027] Step 2: Repeat Run the network benchmark to get the initial fidelity of all entangled links, record and update the fidelity set ;

[0028] Among them, the link selection rules for network benchmark testing are:

[0029]

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

[0031] 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:

[0032] 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;

[0033] Step 3-2: Prepare the quantum state at node A ;

[0034] 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 ;

[0035] 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 ;

[0036] 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 ;

[0037] 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;

[0038] 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 :

[0039] Through regression model Fitting data to calculate fidelity ;

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

[0041] 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 ;

[0042] 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.

[0043] 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 between exploration and utilization, and updates the link fidelity estimation according to the network benchmark test results, thereby improving future link selection. It can not only quickly identify the best quantum link, but also provide accurate fidelity estimation at a lower quantum resource consumption.

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

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

[0046] 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 ;

[0047] 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.

[0048] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0049] Step 1, setting the initial parameters in the quantum entanglement link selection process;

[0050] Step 2: Repeat Run the network benchmark to get the initial fidelity of all entangled links, record and update the fidelity set ;

[0051] 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 results, and complete the quantum entanglement link selection.

[0052] The present invention also provides a computer storable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0053] Step 1, setting the initial parameters in the quantum entanglement link selection process;

[0054] Step 2: Repeat Run the network benchmark to get the initial fidelity of all entangled links, record and update the fidelity set ;

[0055] 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 results, and complete the quantum entanglement link selection.

[0056] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A quantum entanglement link selection method based on Epsilon-greedy strategy, characterized in that: The following steps are involved: Step 1, setting the initial parameters in the quantum entanglement link selection process; 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 results, and complete the quantum entanglement link selection.

2. The method for selecting a quantum entanglement link based on the Epsilon-greedy strategy according to claim 1, characterized in that: The parameters in the quantum entanglement link selection process in step 1 include: The set of bounce times for the network benchmark test to be performed by Node A and Node 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 .

3. The quantum entanglement link selection method based on the Epsilon-greedy strategy according to claim 2 is characterized in that: The iteration based on the Epsilon-greedy strategy in step 3 is specifically as follows: 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 transferred between node A and node B, and a random quantum gate is applied after each transfer. 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.

4. The method for selecting a quantum entanglement link based on the Epsilon-greedy strategy according to claim 3, 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.

5. The method for selecting a quantum entanglement link based on an Epsilon-greedy strategy according to claim 3, characterized in that: The link selection rule for the network benchmark test in step 2 is: 。 6. A quantum entanglement link selection system based on Epsilon-greedy strategy, 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.

7. 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 5 are implemented.

8. 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 5 are implemented.

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