Quantum Search Method, Device and System for Transmission Section Based on Topology-Specific Cut Set Search

Abstract

The present invention relates to the technical fields of power system optimal dispatch and quantum computing, and in particular to a quantum search method, device and system for transmission sections based on topology-specific cut set search. The key transmission section search method based on quantum approximate optimization first obtains a power grid model and a corresponding quantum circuit model by combining power grid operation data, performs quantum evolution and measurement on the quantum circuit model to obtain the quantum state result of the transmission section; sets the starting value e n0 and the ending value e n2 as well as the cut value margin e m ; makes e n1 traverse the numerical interval [e n2 , e n0 at a set interval, and for each e n1 , solve the quantum state result to obtain the expected cut value and the corresponding section result; screen the section results that meet the screening conditions as the optimal transmission section and output. The present invention is improved on the basis of the quantum approximate optimization algorithm, introduces a multi-stage topology-specific cut set search optimization strategy, and approximates the optimal solution of the quantum loss function in stages, thereby improving the accuracy of the quantum topology search result.

Description

Technical Field

[0001] The present invention relates to the technical fields of power system optimal dispatching and quantum computing, and in particular to a quantum search method, device and system for transmission sections based on topological specific cut set search. Background Art

[0002] With the continuous expansion of the scale of the new power system and the increasing complexity of the power grid architecture, the search for transmission sections of the power grid is a very crucial task. In the real-time monitoring and optimal dispatching of the power system, whether the transmission sections of the power grid can be quickly and accurately searched can directly affect the reliability and economy of the power system. The essence of the power system transmission section search problem is to search for cut sets containing a specific number of transmission lines from the power grid topology. When dealing with the optimization of transmission sections of large-scale power systems, specific traditional section search algorithms face problems such as high computational complexity, slow convergence speed, missed selection of sections, and huge search space. Topological specific cut set search is a typical NP-hard mathematical combinatorial optimization problem, which is a common mathematical problem in many industries.

[0003] As a new type of computing method, especially the quantum approximate optimization algorithm, quantum computing exhibits its unique advantages in a large amount of data storage and parallel computing, and has now been proven to have exponential acceleration ability for solving traditional combinatorial optimization problems. Some scholars have applied the quantum approximate optimization algorithm to the transmission section search problem, but this algorithm cannot accurately search for transmission sections with a specified number of lines in practical applications, and can only search for transmission sections near the minimum cut set, and the probability of observing the target transmission section with a specified number of transmission lines is relatively low.

[0004] When quantum algorithms are used to process large-scale and high-dimensional problems, the depth of quantum circuits has an important impact on the optimization results. If the depth is too large, it may lead to waste of quantum computing resources; if the depth is too small, it is difficult to converge to the accurate solution. Summary of the Invention

[0005] In order to overcome the defects of the existing quantum section search technology, such as the difficulty in determining the depth of quantum circuits, unstable convergence effect, and inaccurate sections, the present invention proposes a quantum search method for transmission sections based on topological specific cut set search, which can overcome the problem that the existing quantum search algorithms cannot determine the number of lines in the transmission section, and can make the target transmission section be observed with the highest probability.

[0006] A key transmission section search method based on quantum approximation optimization proposed by the present invention first obtains the power grid model and the corresponding quantum circuit model by combining the power grid operation data, performs quantum evolution and measurement on the quantum circuit model, and obtains the quantum state result of the transmission section;

[0007] Set the starting value e of the target cut value n0 and the ending value en2 and the cut value margin e m , e n0 > e n2 ; Let the target cut value e n1 traverse the numerical interval [e n2 , e n0 from large to small at a set interval. For each e n1 , solve the quantum state result to obtain the expected cut value and the corresponding cross-section result;

[0008] Screen the cross-section results that meet the screening conditions as the optimal transmission cross-section and output.

[0009] Preferably, the method for solving the quantum state result using the quantum algorithm is: combining with the optimization algorithm, taking minimizing the quantum loss function as the optimization goal, and calculating the expected cut value and the corresponding cross-section result.

[0010] Preferably, the quantum loss function C P (β, γ) is:

[0011] C P (β, γ) = <ψ P (β, γ)|Hc|ψ P (β, γ)>;

[0012] where P represents the quantum depth; |ψ P (β, γ)> is the quantum state result, <ψ P (β, γ)| is the conjugate transpose of the quantum state result |ψ P (β, γ)>, and β and γ represent the quantum parameters to be optimized; Hc represents the target Hamiltonian of the specific topological cut set search problem.

[0013] Preferably, the optimization algorithm uses the gradient descent method, and the formula is expressed as:

[0014] (β, γ) t = (β, γ) t-1 - ηg t ;

[0015] g t = [C p (β, γ) t - C p (β, γ) t-1 / [(β, γ) t - (β, γ) t-1 ;

[0016] where η is the iteration step size, and C p (β, γ) t-1 is the quantum loss function after the (t - 1)-th iteration, and C p (β, γ)t is the quantum loss function after the t-th iteration, (β, γ) t-1 are the quantum parameters to be optimized after the (t - 1)-th iteration, (β, γ) t are the quantum parameters to be optimized after the t-th iteration; g t is the gradient of the quantum loss function.

[0017] Preferably, the quantum circuit model is constructed as follows: Convert the nodes in the power grid model into qubits, and apply an H gate to each qubit respectively; then convert the lines in the power grid model into quantum parameter lines in quantum computing. In the quantum parameter lines, each line is represented by a CNOT-RZ-CNOT gate, and then apply an RX gate to each qubit; construct a quantum parameter line corresponding to the quantum depth after the H gate; finally, apply a quantum measurement gate to each qubit.

[0018] Preferably, the specific steps are as follows:

[0019] S1. Obtain the quantum depth, construct a quantum circuit model by combining the power grid model and the quantum depth, perform quantum evolution and measurement on the quantum circuit model, and obtain the quantum state result of the transmission section;

[0020] S2. Solve the quantum state result to obtain the expected cut value e and the corresponding section result;

[0021] S31. Judge whether it satisfies e n1 -e m ≤ e ≤ e n1 +e m ; The initial value of e n1 is e n0 ;

[0022] If not, go to S32;

[0023] If yes, let e n1 be updated to e n1 -1, and go to S33;

[0024] S32. Judge whether the iteration number t satisfies 1 ≤ t < t max ; t max is a set value;

[0025] If yes, let t be updated to t + 1, and then return to S2; if not, end;

[0026] S33. Judge whether it satisfies e n1 < e n2 ;

[0027] If yes, output all section results;

[0028] If not, go to step S31.

[0029] Preferably, when the quantum depth is not determined, in step S1, the quantum depth is iterated from an initial value, and the initial value of the quantum depth is greater than or equal to 1;

[0030] In step S32, if t > t max , then update the quantum depth p to p + 1, and then return to S1;

[0031] In step S33, if e n1 < e n2 , then determine whether |e - e n2 | ≤ e m ;

[0032] |e - e n2 | ≤ e m , then output the quantum depth p and the cross-section results of each stage;

[0033] |e - e n2 | > e m , then update the quantum depth p to p + 1, and then return to S1.

[0034] A key transmission section search device based on quantum approximate optimization proposed by the present invention includes:

[0035] A data input module, configured to obtain power grid operation data and establish a power grid model;

[0036] A quantum modeling module, connected to the data input module; the quantum modeling module is configured to construct a quantum circuit model by combining the input quantum depth and the power grid model;

[0037] A quantum solving module, connected to the quantum modeling module; the quantum solving module is configured to execute the key transmission section search method based on quantum approximate optimization to obtain a transmission section that meets the screening conditions.

[0038] A key transmission section search system based on quantum approximate optimization proposed by the present invention includes a memory and a processor. A computer program is stored in the memory, and the processor is connected to the memory. The processor is configured to execute the computer program to implement the key transmission section search method based on quantum approximate optimization.

[0039] A storage medium proposed by the present invention stores a computer program, and when the computer program is executed, it is used to implement the key transmission section search method based on quantum approximate optimization.

[0040] The advantages of the present invention are as follows:

[0041] (1) The transmission section quantum search method based on topological specific cut set search proposed by the present invention is an improvement on the quantum approximate optimization algorithm. It introduces a multi-stage topological specific cut set search optimization strategy to approximate the optimal solution of the quantum loss function in stages, improving the accuracy of the quantum topological search results.

[0042] (2) In the present invention, the target cut value and cut value margin of each stage can be adjusted according to actual needs, increasing the flexibility of the optimization process and better adapting to the transmission section search problems of power systems with different complexities.

[0043] (3) Aiming at the problem that the quantum circuit depth of the existing quantum section search technology is not easy to determine, the present invention proposes an adaptive quantum circuit depth adjustment mechanism, avoiding the problems of waste of quantum computing resources or poor convergence effect caused by using a fixed depth, and giving full play to the quantum evolution ability of the quantum circuit.

[0044] (4) The present invention introduces two convergence criteria. The first convergence criterion e n1 -e m ≤e≤e n1 +e m is used to judge whether the target cut value and cut value margin of the current stage are satisfied; the second convergence criterion |e - e n2 |≤e m is used to determine whether the overall optimization goal is achieved, ensuring the accuracy and effectiveness of the optimization results. The combination of the two convergence criteria can ensure the reliability and accuracy of the search method, and can quickly reach the convergence standard under limited quantum computing resources, especially suitable for the trend of the expansion of the power grid scale and the increase in complexity of the new power system.

[0045] (5) Based on the quantum approximate optimization algorithm, the present invention controls the optimization process of variational parameters and the quantum circuit depth to search for topological specific cut sets. This method can overcome the problem that the existing quantum search algorithm cannot determine the number of lines in the transmission section, and can make the target transmission section be observed with the highest probability, reducing the occupation and waste of quantum computing resources, and greatly improving the accuracy and stability of the power system quantum transmission section search algorithm.

[0046] (6) By setting the target cut value and cut value margin, adaptively adjusting the quantum circuit depth, and combining two key convergence criteria, it ensures to search for the optimal solution set of the power grid transmission section under limited quantum computing resources. The present invention can quickly and effectively search for the power grid transmission section, improving the real-time monitoring and optimal dispatching capabilities of the power system, and ensuring the safe and stable operation of the future power grid. Description of the Drawings

[0047] Figure 1Flow chart of the transmission section quantum search method based on topological specific cut set search of the present invention;

[0048] Figure 2 Detailed flow chart of the transmission section quantum search method based on topological specific cut set search at a specified quantum depth of the present invention;

[0049] Figure 3 Detailed flow chart of the transmission section quantum search method based on topological specific cut set search with an undetermined quantum depth of the present invention;

[0050] Figure 4 Schematic diagram of the quantum topological cut set search model with a quantum depth of P of the present invention;

[0051] Figure 5 Network topology diagram of the IEEE 14-node system of the present invention;

[0052] Figure 6 Line graph of the quantum depth and the number of iterations required for the example proposed by the present invention to reach the final target cut value;

[0053] Figure 7 Probability bar graph of the calculation results of a specific section of the quantum section search algorithm proposed in the example implementation of the present invention. Specific implementation mode

[0054] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0055] Refer to Figure 1 , Figure 2 , Figure 4 , a key transmission section search method based on quantum approximate optimization proposed in this implementation mode includes the following steps:

[0056] S1. Combine the power grid operation data to obtain the power grid model and the corresponding quantum circuit model, perform quantum evolution and measurement on the quantum circuit model, and obtain the quantum state result of the transmission section;

[0057] As Figure 4 shown, the quantum circuit model includes 2n + Pm + Pn quantum gates, where n is the number of nodes in the power grid model, m is the number of lines in the power grid model, and P is the set quantum depth;

[0058] The construction of the quantum circuit model includes:

[0059] First step, convert the nodes in the power grid model into qubits, with one-to-one correspondence between the qubits and the nodes in the power grid model;

[0060] Second step, apply an H gate to each qubit to transform it into a quantum superposition state as the initial quantum state;

[0061] Third step, convert the lines in the power grid model into quantum parameter lines in quantum computing. In the quantum parameter lines, each line is represented by a CNOT-RZ-CNOT gate, and then apply an RX gate to each qubit; thus, a quantum parameter line composed of m CNOT-RZ-CNOT gates and n RX gates is obtained. Repeat to construct P quantum parameter lines, thereby generating Pm CNOT-RZ-CNOT gates and Pn RX gates;

[0062] Fourth step, apply a quantum measurement gate to each qubit, thereby completing a quantum circuit model containing n H gates, Pm CNOT-RZ-CNOT gates, Pn RX gates, and n quantum measurement gates, as specifically shown in Figure 4 shown.

[0063] The quantum circuit model is measured through the quantum measurement gate, and the obtained quantum state result is denoted as |ψ P (β,γ)>, where P is the quantum depth, β is the quantum parameter to be optimized in the RX gate, and γ is the quantum parameter to be optimized in the RZ gate;

[0064] S2. Combine the quantum state result |ψ P (β,γ)> of the transmission section obtained by measurement with a classical optimization algorithm, with the goal of minimizing the quantum loss function C p (β,γ), calculate the expected cut value e, and divide the nodes in the quantum power grid model into set A and set B;

[0065] Specifically, the classical optimization algorithm can select the Adam optimization algorithm (Adam), Newton's method, or the gradient descent method, etc.

[0066] The calculation formula (1) of the quantum loss function C p (β,γ) is as follows:

[0067] (1);

[0068] In the formula: P represents the quantum depth; <ψ P (β,γ)| is the right vector of the quantum state result |ψ P (β,γ)>, that is, the conjugate transpose; β and γ represent the quantum parameters to be optimized; Hc represents the target Hamiltonian of the specific topological cut set search problem; E is the set of lines in the power grid model, and e rj is the connection line between node r and node j; σr Z Denotes the Pauli Z matrix operator corresponding to node r, σ j Z Denotes the Pauli Z matrix operator corresponding to node j.

[0069] For the convenience of explaining the meaning inside the expression, the following simplification operations shown in formulas (2) and (3) are made:

[0070] (2);

[0071] (3);

[0072] The concise formula (4) obtained from formulas (1), (2), and (3) is as follows:

[0073] (4);

[0074] In the formula: <ψ P (β,γ)| is the ket of |ψ P (β,γ)>, that is, the conjugate transpose; m is the number of lines; C rj To represent the node r and the node j The binary value of the distribution set relationship between them; C rj = 1, indicating that the node r and the node j Are assigned to the same set; C rj = -1, indicating that node r and node j are assigned to different sets; after the assignment iteration of each node is completed, the nodes with an assignment of 1 are classified into set A, and the nodes with an assignment of -1 are classified into set B; e is the expected cut value to be solved;

[0075] It should be noted that the expected cut value e is the topological cut value of the power network, and the classical optimization algorithm is used to minimize the expected size to satisfy the convergence criterion.

[0076] In this embodiment, the calculation formula (5) for optimizing the quantum parameters β and γ using the classical optimization algorithm of gradient descent is as follows:

[0077] (β,γ) t =(β,γ) t-1 -ηg t (5);

[0078] In the formula: t is the number of iterations, the initial value is t = 1, and the maximum number of iterations is t max t max The size can be set by itself; η is the iteration step size; g tis the gradient of the quantum loss function. When the power grid is too complex and the number of quantum gates in the quantum circuit is excessive, the gradient g of the quantum loss function C p (β,γ) t is very difficult to solve. Therefore, the difference formula (6) can be used to simply calculate the gradient magnitude.

[0079] g t =[C p (β,γ) t -C p (β,γ) t-1 / [(β,γ) t -(β,γ) t-1 (6);

[0080] where C p (β,γ) t-1 is the quantum loss function after the (t - 1)-th iteration, C p (β,γ) t is the quantum loss function after the t-th iteration, (β,γ) t-1 is the quantum parameter to be optimized after the (t - 1)-th iteration, and (β,γ) t is the quantum parameter to be optimized after the t-th iteration. Specifically, β is the quantum parameter to be optimized in the RX gate, and γ is the quantum parameter to be optimized in the RZ gate.

[0081] S3. Set the starting value e n0 and the ending value e n2 of the target cut value, as well as the cut value margin e m , where e n0 > e n2 ; Let e n1 traverse the numerical interval [e n2 , e n0 in descending order at a set interval, and loop through step S2 to list the cross-section results corresponding to each expected cut value e; The cross-section result is essentially a topological cut set, that is, the nodes are divided into set A and set B.

[0082] Specifically, e n0 , e n2 and e m can all be set manually, and can be set specifically in combination with experience and task requirements. For example, e n0 can be specifically set as the maximum number of connected lines on the power transmission cross-section, e n2 can be specifically set as the minimum number of connected lines on the power transmission cross-section, and e m can take values in the interval (0, 1).

[0083] S4. Screen the cross-section results that meet the screening conditions as the optimal power transmission cross-section;

[0084] The screening conditions are the set electrical partition properties. For example: (1) The number of nodes in two electrical partitions should be greater than or equal to the set first threshold, and the first threshold takes values from the set {3, 4, 5};

[0085] (2) The number of connection lines between electrical partitions should be less than or equal to the set second threshold, and the second threshold takes values from the set {3, 4, 5};

[0086] (3) There is connectivity within two electrical partitions;

[0087] (4) The connection lines between electrical partitions have direction consistency.

[0088] Specifically, when the P value is determined, step S3 includes the following steps.

[0089] S31. Determine whether it satisfies e n1 -e m ≤e≤e n1 +e m ; where e n1 is the target cut value, e n2 is the final value of the target cut value, e m is the cut value margin; e n2 and e m are both set constants, and the initial value of e n1 is the set constant e n0 , e n0 is greater than e n2 ;

[0090] If not, go to step S32;

[0091] If yes, let e n1 be updated to e n1 -1, and go to step S33;

[0092] S32. Determine whether the iteration number t satisfies 1≤t<t max ; t max is the set maximum iteration number;

[0093] If yes, let t be updated to t + 1, and then go to step S2 to recalculate the expected cut value e;

[0094] If not, output that the quantum depth is insufficient;

[0095] S33. Determine whether it satisfies e n1 <e n2 ;

[0096] If yes, output all cross-section results, that is, the node segmentation sets corresponding to each expected cut value e;

[0097] If not, go to step S31;

[0098] Refer to Figure 3 , when the quantum depth P is not determined, the quantum depth can also be iterated simultaneously to determine the quantum depth P and search for the quantum cross-section at the same time. At this time, the quantum depth p can be iterated starting from 1; that is:

[0099] In step S1, a quantum circuit model is constructed based on the quantum depth p, which includes n H gates, pm CNOT-RZ-CNOT gates, pn RX gates, and n quantum measurement gates;

[0100] Step S3 specifically includes the following steps:

[0101] S31': Determine whether it satisfies e n1 -e m ≤e≤e n1 +e m ; where e n1 is the target cut value, e n2 is the final value of the target cut value, e m is the cut value margin; e n2 and e m are both set constants, and the initial value of e n1 is the set constant e n0 , e n0 is greater than e n2 ;

[0102] No, then go to step S32';

[0103] Yes, then let e n1 be updated to e n1 -1, and turn to step S33';

[0104] S32': Determine whether the iteration number t satisfies 1≤t<t max ;

[0105] Yes, then let t be updated to t + 1, and then turn to step S2 to recalculate the expected cut value e;

[0106] No, then let the quantum depth p be updated to p + 1, and then return to step S1 to reconstruct the quantum circuit model;

[0107] S33': Determine whether it satisfies e n1 <e n2 ;

[0108] Yes, then turn to step S34';

[0109] No, then turn to step S31';

[0110] S34': Determine whether it satisfies |e - e n2 |≤e m ;

[0111] If so, record the quantum depth p as the quantum depth target value P, and output the cross-section results of each stage.

[0112] If not, update the quantum depth p to p + 1, and then return to step S1 to reconstruct the quantum circuit model.

[0113] In this way, when searching for the transmission cross-section, if the quantum depth target value P is determined, search for the transmission cross-section according to the following; Figure 1 If the quantum depth target value P is not determined, search for the transmission cross-section according to the following; Figure 2 Search for the transmission cross-section.

[0114] It should be noted that when implementing the transmission cross-section search shown below, the cross-section results output in step S34' can include only the cross-section results corresponding to the e value solved under the last p value; or can include all the cross-section results corresponding to the e values generated during the p value iteration process. Figure 2 The following combines specific embodiments to verify the above-mentioned quantum search method for transmission cross-sections based on topological specific cut set search.

[0115] In this embodiment, the IEEE 14-node system is used for verification. The network topology diagram of the IEEE 14-node system is as shown in

[0116] shown, which includes 14 nodes and 20 lines; the nodes include generator nodes and load nodes, and the lines include transmission lines and transformer branches. Figure 5 In this embodiment, the initial value of the quantum depth P is set to 1. During the subsequent iteration process, the quantum depth value will increase adaptively with the action of the convergence criterion; the maximum value t of the optimization iteration times

[0117] is 200 times; the cut value margin e max is 0.2; the target cut value e m is 0.2; the initial value e of the target cut value n1 is set to 4; the final value e of the target cut value n0 is set to 1; the quantum topological cut set results of each quantum measurement sampling are set to 10,000; both the first threshold and the second threshold are set to 4. n2 In this embodiment, when implementing the method shown in

[0118] it is found that when the quantum depth iterates to 6, |e - e Figure 3 | ≤ e n2 is achieved for the first time. During this process, the line graph of the quantum depth and the iteration times is as shown in m shown, and the line graph of the quantum depth and the iteration times is as shown in Figure 6As shown; it can be seen that when the quantum depth p < 6, the algorithm cannot converge; this is because a lower quantum depth cannot provide sufficient gradient information, and the quantum evolution is too simple to reach the complexity required by the target expectation, resulting in the algorithm not being able to converge. Therefore, it is necessary to increase the value of p.

[0119] When p = 6, from Figure 6 it can be seen that the required number of iterations changes and no longer stays at the maximum number of iterations. Only 80 iterations are required to complete the quantum evolution process; when p is greater than 6, although the evolution process can also be successfully completed, it will increase the number of quantum gates in the quantum circuit model, bringing higher computational costs and more complex circuit designs.

[0120] In this embodiment, the quantum depth target value P = 6 is set, and then the Figure 2 method shown is implemented. At this time, the number of quantum gates required for the entire quantum circuit model is 2n + Pm + Pn = 2×14 + 6×20 + 6×14 = 232. After solving the quantum bit string |ψ P (β,γ)>, all the cross-section results obtained are shown in Table 1.

[0121] Table 1 Number of quantum power transmission cross-section results at each stage

[0122] ;

[0123] In this embodiment, 10,000 quantum state results are sampled at each stage, and the probability histogram of the calculation results of a specific cross-section is as Figure 7 shown, that is, among the 10,000 quantum state results searched in the first stage (e n1 = 4), 4426 satisfy the cross-section cut value of 4. The algorithm proposed in the present invention, according to two convergence criteria (e n1 -e m ≤e≤e n1 +e m and |e - e n2 |≤e m ), searches cyclically multiple times, greatly improving the effective probability of the cross-section results at each stage and enhancing the search accuracy.

[0124] In this embodiment, through the screening conditions (i.e., the nature of electrical partitions), the partition methods that meet the conditions are shown in Table 2.

[0125] The electrical partition properties set in this embodiment are: "the number of nodes in two electrical partitions should be greater than or equal to the first threshold 4 and the number of connecting lines between electrical partitions should be less than or equal to the second threshold 4" and "the two electrical partitions are internally connected and the connecting lines between electrical partitions have direction consistency".

[0126] Table 2 Cross-section results of the IEEE14 node system and power grid partition situation

[0127] ;

[0128] In Table 2, l i-j represents the connection line between node i and node j.

[0129] Of course, for those skilled in the art, the present invention is not limited to the details of the above exemplary embodiments, but also includes the same or similar structures that can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

[0130] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0131] The technologies, shapes, and structures not detailedly described in the present invention are all well-known technologies.

Claims

1. A critical transmission section search method based on quantum approximate optimization, characterized in that First, obtain the power grid model by combining power grid operation data, construct a quantum circuit model corresponding to the power grid model, perform quantum evolution and measurement on the quantum circuit model, and obtain the quantum state results of the transmission section; Set the starting value e of the target cut value n0 and the ending value e n2 as well as the cut value margin e m , e n0 > e n2 ; Let the target cut value e n1 traverse the numerical interval [e n2 , e n0 from large to small at set intervals. For each e n1 , solve the quantum state result to obtain the expected cut value and the corresponding cross-section result; Screen the section results that meet the screening conditions as the optimal transmission section and output; The section search method is as follows: S1. Obtain the quantum depth, construct a quantum circuit model by combining the power grid model and the quantum depth, perform quantum evolution and measurement on the quantum circuit model, and obtain the quantum state results of the transmission section; S2. Solve the quantum state results to obtain the expected cut value e and the corresponding section results; S31. Determine whether e satisfies n1 -e m ≤e≤e n1 +e m ; e n1 The initial value of is e n0 ; If not, go to S32; If so, let e n1 be updated to e n1 - 1, and go to S33; S32. Determine whether the iteration count t satisfies 1 ≤ t < t max ; t max is a set value; If yes, update t to t + 1, and then return to S2; if not, end; S33. Determine whether e is satisfied n1 < e n2 ; If yes, output all section results; If not, go to step S31.

2. The key transmission section search method based on quantum approximate optimization according to claim 1, wherein The method for solving the quantum state results using the quantum algorithm is: combine the optimization algorithm, and take minimizing the quantum loss function as the optimization objective to calculate the expected cut value and the corresponding section results.

3. The key transmission section search method based on quantum approximate optimization according to claim 2, wherein the quantum Loss function C P (β,γ) is as follows: C P (β,γ) = <ψ P (β,γ)|Hc|ψ P (β,γ)>; where P represents the quantum depth; |ψ P (β,γ)> is the quantum state result, <ψ P (β,γ)| is the conjugate transpose of the quantum state result |ψ P (β,γ)>, β and γ represent the quantum parameters to be optimized; Hc represents the target Hamiltonian of a specific topological cut set search problem.

4. The key transmission section search method based on quantum approximate optimization according to claim 2, characterized in that, The optimization algorithm uses the gradient descent method, and the formula is expressed as: (β,γ) t =(β,γ) t-1 -ηg t ; g t =[C p (β,γ) t -C p (β,γ) t-1 / [(β,γ) t -(β,γ) t-1 ; where η is the iteration step size, C p (β,γ) t-1 is the quantum loss function after the (t - 1)-th iteration, C p (β,γ) t is the quantum loss function after the t-th iteration, (β,γ) t-1 are the quantum parameters to be optimized after the (t - 1)-th iteration, (β,γ) t are the quantum parameters to be optimized after the t-th iteration; g t is the gradient of the quantum loss function.

5. The key transmission section search method based on quantum approximate optimization according to claim 1, characterized in that The construction method of the quantum circuit model is: convert the nodes in the power grid model into qubits, and apply an H gate to each qubit; then convert the lines in the power grid model into quantum parameter lines in quantum computing. In the quantum parameter lines, each line is represented by a CNOT-RZ-CNOT gate, and then apply an RX gate to each qubit; construct a quantum parameter line corresponding to the quantum depth after the H gate; finally, apply a quantum measurement gate to each qubit.

6. The key transmission section search method based on quantum approximate optimization according to any one of claims 1-5, characterized in that, When the quantum depth is not determined, in step S1, the quantum depth is iterated from the initial value, and the initial value of the quantum depth is greater than or equal to 1; In step S32, if t > t max , then update the quantum depth p to p + 1, and then return to S1; In step S33, if e n1 < e n2 , then it is determined whether |e - e n2 | ≤ e m ; |e - e n2 | ≤ e m , then output the quantum depth p and the cross-section results of each stage; |e-e n2 |>e m , then update the quantum depth p to p + 1, and then return to S1.

7. A key transmission section search device based on quantum approximate optimization, including: A data input module for obtaining power grid operation data and establishing a power grid model; A quantum modeling module connected to the data input module; The quantum modeling module is used to construct a quantum circuit model by combining the input quantum depth and the power grid model; A quantum solution module connected to the quantum modeling module; the quantum solution module is used to execute the key transmission section search method based on quantum approximate optimization described in any one of claims 1-6 to obtain a transmission section that meets the screening conditions.

8. A critical transmission section search system based on quantum approximate optimization, characterized in that, It includes a memory and a processor. A computer program is stored in the memory, and the processor is connected to the memory. The processor is used to execute the computer program to implement the key transmission section search method based on quantum approximate optimization described in any one of claims 1-6.

9. A storage medium, characterized in that, A computer program is stored, and when the computer program is executed, it is used to implement the key transmission section search method based on quantum approximate optimization described in any one of claims 1-6.

Images