Multi-quantum bit cooperative control microwave power system
Through multi-objective optimization and collaboratively controlled microwave power systems, the problems of high resource consumption and imprecise entanglement feature extraction in traditional quantum computing have been solved, achieving efficient collaborative work of quantum bits and improved system performance.
Patent Information
- Application Number
- CN202510945786.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-09
- Publication Date
- 2025-10-10
AI Technical Summary
In traditional quantum computing systems, quantum bits operate independently and lack flexibility, and cannot be adjusted dynamically, resulting in high consumption of computing resources and difficulty in expanding to large-scale systems. In addition, the extraction of entanglement features is not precise enough, which limits the improvement of system performance.
A multi-objective optimization method is adopted to construct an adjacency matrix and a hybrid optimization model through data acquisition, pulse generation, strategy generation, quantum compression and strategy control unit. The variational quantum algorithm and graph neural network are used to generate microwave pulse sequences and power allocation strategies. Combined with the tensor network compression algorithm and the matrix product state method, the coordinated control of multiple quantum bits is realized.
It improves the overall performance of the quantum computing system, optimizes gate operation speed and power consumption, reduces data storage requirements, while retaining key entangled information, and realizes efficient collaborative work of multiple quantum bits.
Smart Images
Figure CN120764708A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of quantum bit cooperative control, and in particular to a microwave power system for cooperative control of multiple quantum bits. Background Art
[0002] Traditional methods typically focus on a single objective (such as maximizing fidelity or minimizing power consumption) while ignoring other factors (such as gate speed). This approach limits the overall performance improvement of quantum computing systems. Traditional methods often use static optimization strategies, that is, the optimization objectives and constraints are set at the beginning and then executed unchanged. This approach lacks flexibility and cannot be dynamically adjusted according to changes in the state of the quantum bits. The quantum bits in traditional methods are usually controlled independently, and the operation of each quantum bit does not consider the coupling and synergy between other quantum bits. In traditional methods, quantum state data is usually large, with high storage and computational overhead. In addition, traditional methods do not extract the entanglement characteristics of quantum states with sufficient precision and may not be able to effectively compress and utilize this data. As the number of quantum bits increases, traditional methods are generally difficult to expand to larger-scale quantum computing systems, which may require a large amount of computing resources and complex adjustments. Summary of the Invention
[0003] The purpose of the present invention is to provide a microwave power system with cooperative control of multiple quantum bits to solve the above problems.
[0004] The present invention is achieved through the following technical solutions: A multi-qubit cooperatively controlled microwave power system comprising: A data acquisition unit, the data acquisition unit being configured to acquire initial quantum state data of multiple quantum bits, wherein the initial quantum state data includes fidelity, gate speed, and power consumption of the multiple quantum bits, and construct an adjacency matrix representing the coupling strength between the quantum bits based on the initial quantum state data; a pulse generation unit, configured to perform a weighted function fusion on the fidelity, gate speed, and power consumption to obtain a multi-objective optimization function, obtain constraints of the multi-objective optimization function based on the adjacency matrix to obtain a hybrid optimization model, and iteratively train the hybrid optimization model based on a variational quantum algorithm to obtain a microwave pulse sequence; a strategy generation unit, the strategy generation unit being configured to extract phase features of the microwave pulse sequence based on a graph neural network to obtain the phase features of the microwave pulse sequence, generate a pulse phase adjustment strategy based on the phase features, deploy a lightweight control model based on an FPGA, and map the phase of the pulse phase adjustment strategy to power based on the lightweight control model to obtain a microwave power allocation strategy; a quantum compression unit, configured to adjust the multi-qubits based on the microwave power allocation strategy to obtain optimized quantum state data of the multi-qubits, compress and reconstruct the optimized quantum state data based on a tensor network compression algorithm to obtain compressed quantum state data, and extract entanglement entropy characteristics of the compressed quantum state data based on a matrix product state method; A strategy control unit is used to adjust the microwave power allocation strategy based on the entanglement entropy characteristics to obtain a second microwave power allocation strategy, and to collaboratively control the multiple quantum bits based on the second microwave power allocation strategy.
[0005] Preferably, constructing an adjacency matrix characterizing the coupling strength between quantum bits based on the initial quantum state data includes: Constructing element values of the adjacency matrix based on the fidelity, gate speed, and power consumption of the multi-qubit; The magnitude of the element value represents the coupling strength of the multi-qubit, and the positive or negative value of the element value represents the interaction type of the multi-qubit, where a positive number represents mutual attraction and a negative number represents mutual repulsion.
[0006] Preferably, the fidelity, gate speed and power consumption are subjected to a weighted fusion function to obtain a multi-objective optimization function, including: The fidelity, gate speed and power consumption are weighted based on preset weight coefficients to obtain weighted fidelity, weighted gate speed and weighted power consumption; The weighted fidelity, weighted gate speed and weighted power consumption are functionally fused to obtain the multi-objective optimization function.
[0007] Preferably, obtaining the constraint conditions of the multi-objective optimization function based on the adjacency matrix to obtain a hybrid optimization model includes: Obtaining a constraint relationship of the multi-qubit based on element values of the adjacency matrix, wherein the constraint relationship includes interaction coupling strength and type of the multi-qubit; The constraint relationship is used as a constraint condition of the multi-objective optimization function to obtain the hybrid optimization model.
[0008] Preferably, iteratively training the hybrid optimization model based on a variational quantum algorithm to obtain a microwave pulse sequence comprises: Initializing parameters of the variational quantum algorithm, wherein the parameters include the initial state of the multi-qubit, parameters of the quantum gate, and the number of iterations; Using the hybrid optimization model as the objective function of the variational quantum algorithm, and iteratively optimizing the objective function based on the parameters to obtain optimized quantum bit states and quantum gate parameters; The microwave pulse sequence is obtained based on the optimized quantum bit state and quantum gate parameters.
[0009] Preferably, a lightweight control model is deployed based on FPGA, including: Describing the lightweight control model based on a hardware description language to obtain control logic recognizable by the FPGA; The control logic is loaded into FPGA to obtain the lightweight control model.
[0010] Preferably, mapping the phase of the pulse phase adjustment strategy to power based on the lightweight control model to obtain a microwave power allocation strategy includes: Converting the phase value of the pulse phase adjustment strategy into a power value based on the phase-frequency mapping relationship of the lightweight control model; The microwave pulse sequence is controlled based on the power value to obtain the microwave power allocation strategy.
[0011] Preferably, extracting the entanglement entropy characteristics of the compressed quantum state data based on a matrix product state method includes: performing matrix product state decomposition on the compressed quantum state data to obtain a plurality of matrix product state components; The entanglement entropy of the matrix product state component is calculated to obtain the entanglement entropy characteristic.
[0012] Preferably, adjusting the microwave power allocation strategy based on the entanglement entropy characteristic to obtain a second microwave power allocation strategy includes: Evaluating the entanglement entropy feature based on a preset entanglement entropy threshold; If the entanglement entropy characteristic exceeds the entanglement entropy threshold, the microwave power allocation strategy is adjusted to obtain the second microwave power allocation strategy.
[0013] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. Through multi-objective optimization, the present invention can optimize gate operation speed and reduce power consumption as much as possible while ensuring computing quality, thereby improving the overall performance of the quantum computing system; 2. The present invention globally optimizes multiple qubits through collaborative control, enabling more efficient collaborative work between qubits, thereby improving the performance of overall quantum computing; 3. The present invention uses a tensor network compression algorithm to compress and reconstruct the optimized quantum state data, and extracts the entanglement entropy characteristics based on the matrix product state method. This method effectively reduces the storage and processing requirements of quantum data while retaining key entanglement information. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 A schematic diagram of the system architecture of the overall system in an embodiment of the present invention; The reference numerals represent: 1-data acquisition unit, 2-pulse generation unit, 3-strategy generation unit, 4-quantum compression unit, 5-strategy control unit. DETAILED DESCRIPTION
[0015] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the examples and accompanying drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention. It should be noted that the present invention is already in the actual development and use stage.
[0016] Example 1, as Figure 1 As shown, the present invention proposes a multi-qubit cooperatively controlled microwave power system, comprising: Data acquisition unit 1, which is used to collect initial quantum state data of multiple quantum bits, wherein the initial quantum state data includes the fidelity, gate speed and power consumption of the multiple quantum bits, and construct an adjacency matrix representing the coupling strength between quantum bits based on the initial quantum state data; Pulse generation unit 2, which is used to perform function weighted fusion on fidelity, gate speed, and power consumption to obtain a multi-objective optimization function, obtain constraints of the multi-objective optimization function based on an adjacency matrix to obtain a hybrid optimization model, and iteratively train the hybrid optimization model based on a variational quantum algorithm to obtain a microwave pulse sequence; Strategy generation unit 3, which is used to extract phase features of the microwave pulse sequence based on a graph neural network to obtain the phase features of the microwave pulse sequence, generate a pulse phase adjustment strategy based on the phase features, deploy a lightweight control model based on an FPGA, and map the phase of the pulse phase adjustment strategy to power based on the lightweight control model to obtain a microwave power allocation strategy; Quantum compression unit 4, which is used to adjust multiple quantum bits based on a microwave power allocation strategy to obtain optimized quantum state data of the multiple quantum bits, compress and reconstruct the optimized quantum state data based on a tensor network compression algorithm to obtain compressed quantum state data, and extract entanglement entropy characteristics of the compressed quantum state data based on a matrix product state method; The strategy control unit 5 is used to adjust the microwave power allocation strategy based on the entanglement entropy characteristics to obtain a second microwave power allocation strategy, and to coordinately control multiple quantum bits based on the second microwave power allocation strategy.
[0017] In the present invention, the initial quantum state data refers to the state data of the quantum bit collected at the beginning of quantum computing. These data include the fidelity, gate speed and power consumption of the quantum bit, which are used for subsequent optimization and control of the quantum bit; fidelity is a measure of the similarity between the quantum state and the ideal quantum state. High fidelity means that the state of the quantum bit is closer to the expected ideal state, which is a key indicator for measuring the accuracy of quantum computing; gate speed refers to the time to perform a quantum gate operation. The faster the quantum gate speed, the higher the overall efficiency of the quantum computing process; power consumption refers to the energy consumed by the quantum bit when performing an operation; the adjacency matrix represents the coupling strength between quantum bits, that is, the interaction relationship between two quantum bits. Its element values represent the degree of interaction between different quantum bits; the hybrid optimization model refers to a combination of multiple optimization methods (such as classical algorithms and quantum algorithms) to optimize the control of quantum bits. Here, it is based on the adjacency matrix of the quantum bits and considers the coupling relationship of the quantum bits for optimization; the variational quantum algorithm refers to a quantum algorithm that combines quantum computing and classical optimization methods. The variational quantum algorithm processes complex quantum states through the quantum computing part, while the classical computing part optimizes parameters; the microwave pulse sequence refers to a pulse sequence designed to realize quantum gate operations, which is used to manipulate the state of the quantum bit; the phase characteristic refers to the state of the quantum bit being affected by the phase of the microwave pulse. By extracting the phase characteristics of the microwave pulse sequence, the control of the quantum bit can be analyzed and optimized; the pulse phase adjustment strategy refers to a strategy designed based on phase characteristics, which is used to precisely control the phase of the microwave pulse to ensure that the quantum bit evolves in the desired manner; FPGA refers to a programmable hardware device that can implement customized logical functions according to needs.In quantum computing, FPGA is used to implement fast real-time control and optimize the adjustment of microwave pulses; lightweight control model refers to a control model designed with low computational burden, which can quickly respond and adjust the state of quantum bits under the premise of ensuring accuracy; optimized quantum state data refers to the best state data of quantum bits obtained by optimizing microwave pulse sequences and power allocation strategies, and the optimized quantum state data reflects the best execution state in quantum computing; tensor network is a mathematical structure used to represent high-dimensional data and process quantum state data; compressed quantum state data refers to quantum bit state data obtained through tensor network compression algorithms, which usually has higher efficiency in storage and computation; matrix product state method refers to a numerical method for representing quantum states, especially suitable for large-scale quantum system computation, which represents quantum states through matrix multiplication and can efficiently handle complex characteristics such as quantum entanglement; entanglement entropy feature refers to a measure of the degree of entanglement of a quantum system, and the entanglement entropy feature reflects the complexity of the interaction between quantum bits, which is a key factor in optimizing quantum computing performance; cooperative control refers to a control strategy that coordinates multiple quantum bits, and by optimizing the control parameters of multiple quantum bits, it can ensure their cooperative work during the calculation process, improving the overall efficiency and stability of the system.
[0018] The embodiment includes constructing an adjacency matrix representing the coupling strength between quantum bits based on initial quantum state data, including: Constructing element values of the adjacency matrix based on the fidelity, gate speed, and power consumption of multiple quantum bits; Wherein, the size of the element value represents the coupling strength of the multiple quantum bits, and the positive and negative of the element value represents the interaction type of the multiple quantum bits, positive number represents mutual attraction, and negative number represents mutual repulsion.
[0019] In an optional embodiment, the fidelity, gate speed, and power consumption are functionally weighted and fused to obtain a multi-objective optimization function, including: Respectively weighting the fidelity, gate speed, and power consumption based on preset weight coefficients to obtain weighted fidelity, weighted gate speed, and weighted power consumption; Functionally fusing the weighted fidelity, weighted gate speed, and weighted power consumption to obtain a multi-objective optimization function.
[0020] It should be noted that the preset weight coefficient refers to a weight value set in advance, which is used to represent the relative importance of each target; function fusion refers to the process of combining multiple target functions into a composite function. In multi-objective optimization, it is often necessary to fuse weighted targets (such as weighted fidelity, weighted gate speed and weighted power consumption) into a comprehensive target function. This process is achieved through weighted summation and weighted average in order to consider multiple optimization targets at the same time; the multi-objective optimization function refers to a function that combines multiple targets into an optimization target. In quantum computing, multiple factors such as fidelity, gate speed and power consumption need to be optimized simultaneously. Through function fusion, multiple weighted targets are combined into a function, and the optimization algorithm can find the optimal solution on this function. This function is usually constructed according to different target functions and their weight coefficients.
[0021] In an optional embodiment, the constraint condition of the multi-objective optimization function is obtained based on the adjacency matrix to obtain a hybrid optimization model, including: The constraint relationship of the multiple qubits is obtained based on the element value of the adjacency matrix, wherein the constraint relationship includes the interaction coupling strength and type of the multiple qubits; The constraint relationship is taken as the constraint condition of the multi-objective optimization function to obtain the hybrid optimization model.
[0022] It should be noted that the coupling strength refers to the strength of the interaction between two qubits. In quantum computing, the coupling between qubits determines the degree of their interaction in quantum operations. Strong coupling means that the states of two qubits are more closely related, while weak coupling means that their mutual influence is smaller.
[0023] In an optional embodiment, the hybrid optimization model is iteratively trained based on a variational quantum algorithm to obtain a microwave pulse sequence, including: The parameters of the variational quantum algorithm are initialized, wherein the parameters include the initial state of the multiple qubits, the parameters of the quantum gate and the number of iterations; The hybrid optimization model is taken as the objective function of the variational quantum algorithm, and the objective function is iteratively optimized based on the parameters to obtain the optimized qubit state and quantum gate parameters; The microwave pulse sequence is obtained based on the optimized qubit state and quantum gate parameters.
[0024] It should be noted that the initial state of multiple quantum bits refers to the fact that in quantum computing, the initial state of the quantum bit is usually a standard ground state. This initial state is the starting point of the variational quantum algorithm and determines the quantum information of the quantum bit at the beginning; the parameters of the quantum gate refer to the quantum gate in the variational quantum algorithm used to operate on the quantum bit. There are different types of quantum gates (such as rotation gates, controlled NOT gates, etc.), and the operation of each quantum gate is controlled by a set of parameters (such as rotation angle, amplitude, etc.).
[0025] In an optional embodiment, deploying a lightweight control model based on FPGA includes: Describe the lightweight control model based on the hardware description language to obtain the control logic that can be recognized by FPGA; The control logic is loaded into the FPGA to obtain a lightweight control model.
[0026] In an optional embodiment, the phase of the pulse phase adjustment strategy is mapped to power based on the lightweight control model to obtain a microwave power allocation strategy, including: The phase value of the pulse phase adjustment strategy is converted into a power value based on the phase-frequency mapping relationship of the lightweight control model; The microwave pulse sequence is controlled based on the power value to obtain a microwave power allocation strategy.
[0027] It should be noted that the phase-frequency mapping relationship refers to a mapping method that establishes the relationship between phase value and frequency value. In quantum control, microwave pulses usually control quantum bits by adjusting the phase (i.e., the fluctuation angle of the pulse) and frequency (i.e., the frequency of the fluctuation). The role of the phase-frequency mapping relationship is to convert the adjustment of the pulse phase into an adjustable power value, which can accurately control the behavior of the quantum bit and ensure that the execution of the quantum gate meets expectations. The microwave power allocation strategy refers to how to allocate power in different microwave pulses to achieve the goal of precise control of the quantum bit. The power distribution of microwave pulses directly affects the behavior of the quantum bit. Therefore, formulating a reasonable power allocation strategy is an important task in quantum computing. This strategy is usually based on the operational requirements of the quantum bit, combined with the phase value and frequency adjustment to determine the power required for each pulse to optimize the calculation results.
[0028] In an optional embodiment, extracting the entanglement entropy characteristics of the compressed quantum state data based on a matrix product state method includes: Performing matrix product state decomposition on the compressed quantum state data to obtain a plurality of matrix product state components; The entanglement entropy of the matrix product state components is calculated to obtain the entanglement entropy characteristics.
[0029] It should be noted that the matrix product state components refer to each matrix obtained in the matrix product state decomposition, which together constitute the complete matrix product state. In the matrix product state representation, the quantum state is represented as the product of multiple matrices, and each matrix corresponds to the partial state of each subsystem in the quantum system.
[0030] In an optional embodiment, adjusting the microwave power allocation strategy based on the entanglement entropy characteristic to obtain a second microwave power allocation strategy includes: Evaluating the entanglement entropy feature based on a preset entanglement entropy threshold; If the entanglement entropy characteristic exceeds the entanglement entropy threshold, the microwave power allocation strategy is adjusted to obtain a second microwave power allocation strategy.
[0031] It should be noted that the preset entanglement entropy threshold is a predetermined value used to measure the degree of entanglement in the quantum system. When the entanglement entropy exceeds this threshold, it usually indicates that the system is highly entangled and requires further processing or adjustment. The entanglement entropy characteristic is quantitative information extracted from the quantum system that describes the degree of entanglement. The second microwave power allocation strategy refers to a new power allocation scheme obtained after adjusting the preliminary microwave power allocation strategy. This strategy optimizes the evolution of the quantum state and improves the performance of the quantum system by adjusting the power of the microwave signal when the entanglement entropy characteristic exceeds the threshold. Through the second microwave power allocation strategy, the system can better adapt to quantum operations and achieve the desired quantum computing effect.
[0032] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A microwave power system for multi-qubit cooperative control, characterized in that: include: A data acquisition unit (1), the data acquisition unit (1) is used to acquire initial quantum state data of multiple quantum bits, wherein the initial quantum state data includes the fidelity, gate speed and power consumption of the multiple quantum bits, and construct an adjacency matrix representing the coupling strength between quantum bits based on the initial quantum state data; A pulse generating unit (2) is used for performing function weighted fusion on the fidelity, gate speed and power consumption to obtain a multi-objective optimization function, obtaining constraint conditions of the multi-objective optimization function based on the adjacency matrix to obtain a hybrid optimization model, and iteratively training the hybrid optimization model based on a variational quantum algorithm to obtain a microwave pulse sequence; A strategy generation unit (3), the strategy generation unit (3) is used to extract phase features of the microwave pulse sequence based on a graph neural network to obtain the phase features of the microwave pulse sequence, generate a pulse phase adjustment strategy based on the phase features, deploy a lightweight control model based on an FPGA, and map the phase of the pulse phase adjustment strategy to power based on the lightweight control model to obtain a microwave power allocation strategy; A quantum compression unit (4), the quantum compression unit (4) is used to adjust the multi-qubit based on the microwave power allocation strategy to obtain optimized quantum state data of the multi-qubit, compress and reconstruct the optimized quantum state data based on a tensor network compression algorithm to obtain compressed quantum state data, and extract entanglement entropy characteristics of the compressed quantum state data based on a matrix product state method; A strategy control unit (5) is used to adjust the microwave power allocation strategy based on the entanglement entropy characteristics to obtain a second microwave power allocation strategy, and to perform cooperative control on the multiple quantum bits based on the second microwave power allocation strategy.
2. A multi-qubit cooperatively controlled microwave power system according to claim 1, characterized in that: Constructing an adjacency matrix representing the coupling strength between quantum bits based on the initial quantum state data, including: Constructing element values of the adjacency matrix based on the fidelity, gate speed, and power consumption of the multi-qubit; The magnitude of the element value represents the coupling strength of the multi-qubit, and the positive or negative value of the element value represents the interaction type of the multi-qubit, where a positive number represents mutual attraction and a negative number represents mutual repulsion.
3. A multi-qubit cooperatively controlled microwave power system according to claim 2, characterized in that: Performing a weighted fusion function on the fidelity, gate speed, and power consumption to obtain a multi-objective optimization function, including: The fidelity, gate speed and power consumption are weighted based on preset weight coefficients to obtain weighted fidelity, weighted gate speed and weighted power consumption; The weighted fidelity, weighted gate speed and weighted power consumption are functionally fused to obtain the multi-objective optimization function.
4. A multi-qubit cooperatively controlled microwave power system according to claim 3, characterized in that: Obtaining the constraint conditions of the multi-objective optimization function based on the adjacency matrix to obtain a hybrid optimization model, including: Obtaining a constraint relationship of the multi-qubit based on element values of the adjacency matrix, wherein the constraint relationship includes interaction coupling strength and type of the multi-qubit; The constraint relationship is used as a constraint condition of the multi-objective optimization function to obtain the hybrid optimization model.
5. The multi-qubit cooperatively controlled microwave power system according to claim 3, characterized in that: The hybrid optimization model is iteratively trained based on a variational quantum algorithm to obtain a microwave pulse sequence, including: Initializing parameters of the variational quantum algorithm, wherein the parameters include the initial state of the multi-qubit, parameters of the quantum gate, and the number of iterations; Using the hybrid optimization model as the objective function of the variational quantum algorithm, and iteratively optimizing the objective function based on the parameters to obtain optimized quantum bit states and quantum gate parameters; The microwave pulse sequence is obtained based on the optimized quantum bit state and quantum gate parameters.
6. A multi-qubit cooperatively controlled microwave power system according to claim 5, characterized in that: Deploy lightweight control models based on FPGA, including: Describing the lightweight control model based on a hardware description language to obtain control logic recognizable by the FPGA; The control logic is loaded into FPGA to obtain the lightweight control model.
7. The multi-qubit cooperatively controlled microwave power system according to claim 5, characterized in that: Mapping the phase of the pulse phase adjustment strategy to power based on the lightweight control model to obtain a microwave power allocation strategy includes: Converting the phase value of the pulse phase adjustment strategy into a power value based on the phase-frequency mapping relationship of the lightweight control model; The microwave pulse sequence is controlled based on the power value to obtain the microwave power allocation strategy.
8. The multi-qubit cooperatively controlled microwave power system according to claim 3, characterized in that: Extracting the entanglement entropy characteristics of the compressed quantum state data based on a matrix product state method includes: performing matrix product state decomposition on the compressed quantum state data to obtain a plurality of matrix product state components; The entanglement entropy of the matrix product state component is calculated to obtain the entanglement entropy characteristic.
9. The multi-qubit cooperatively controlled microwave power system according to claim 8, characterized in that: Adjusting the microwave power allocation strategy based on the entanglement entropy characteristic to obtain a second microwave power allocation strategy includes: Evaluating the entanglement entropy feature based on a preset entanglement entropy threshold; If the entanglement entropy characteristic exceeds the entanglement entropy threshold, the microwave power allocation strategy is adjusted to obtain the second microwave power allocation strategy.