Control and dynamic supervision system of quantum state transmission channel

Abstract

The control and dynamic supervision system for the quantum state transmission channel comprises a quantum state generation module, a quantum channel transmission control module, a quantum state verification and supervision module, a dynamic scheduling module and a visualization module, the quantum channel transmission control module is used for adjusting the state of a quantum channel and enhancing the transmission performance of the quantum channel, the quantum state verification and supervision module is used for verifying a transmitted quantum state and supervising channel transmission of the quantum state, and the dynamic scheduling module is used for dynamically adjusting resource allocation and channel use. And the visualization module is used for providing an intuitive graphical user interface. The quantum neural network algorithm for improving data coding is provided to enhance the transmission performance of the quantum channel through entanglement operation, the improved quantum communication dynamic resource allocation algorithm based on the quantum channel is provided to dynamically allocate the resources of the quantum channel, and a better scheme is provided for a control and dynamic supervision system of the quantum state transmission channel.

Description

Technical Field

[0001] The invention relates to the field of quantum communication transmission and dynamic resource allocation, and specifically to a control and dynamic supervision system for a quantum state transmission channel. Background Art

[0002] Quantum communication transmission technology is a communication technology that uses the principles of quantum mechanics to achieve information transmission, including quantum bit encoding, quantum state transmission and quantum measurement process. In quantum communication, information is transmitted not through classical bits, but through quantum bit states, and information is usually encoded in the form of quantum bits. Different quantum bit encoding schemes will affect the stability and security of transmission. Therefore, the quantum state transmission channel must be able to maintain and transmit quantum states to ensure the security and integrity of information. Quantum entanglement, quantum communication protocols (such as BBM92 protocol, BB84 protocol), quantum sensing and quantum network structure are usually used in quantum communication transmission to enhance the security and stability of quantum communication transmission.

[0003] Dynamic resource allocation technology is a technology that dynamically allocates quantum bits and quantum communication resources to meet the communication needs between different nodes in the communication network. It dynamically adjusts the resource allocation scheme to adapt to the real-time changes in the network and ensures efficient quantum information transmission. In the communication network, resource allocation is the flexible allocation of resources based on real-time needs and network status to meet the needs of different users and applications. Double Q learning is a technology in the field of reinforcement learning, which is used to train intelligent agents to make the best decisions under different states. Applying it to resource allocation problems can enable the system to adapt to changes in a dynamic environment. Lyapunov optimization theory is a mathematical method commonly used in stability analysis and optimization problems of dynamic systems, which can transform some non-convex problems into continuous deterministic optimization problems. Dynamic resource allocation technology also includes time division multiplexing (TDM), frequency division multiplexing (FDM) and dynamic spectrum allocation. In addition, it also involves reinforcement learning technologies such as Q learning, deep Q network and double Q learning algorithm to better dynamically allocate quantum communication resources. Summary of the invention

[0004] In view of the above problems, the present invention aims to provide a control and dynamic supervision system for a quantum state transmission channel.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a control and dynamic supervision system for a quantum state transmission channel, comprising a quantum state generation module, a quantum channel transmission control module, a quantum state verification and supervision module, a dynamic scheduling module, and a visualization module. The quantum state generation module is used to generate traditional binary bit data into a specific quantum state. The quantum channel transmission control module comprises a quantum channel control unit and a quantum state transmission control unit. The quantum channel control unit is used to control the state and adjust the parameters of the channel. The quantum state transmission control unit proposes a quantum neural network algorithm for improving data encoding to enhance the transmission performance of the quantum channel. The quantum state verification and supervision module comprises a quantum state verification unit and a quantum state supervision unit. The quantum state verification unit is used to verify the integrity of the quantum state and detect the error rate of the quantum state. The quantum state supervision unit is used to supervise the entire quantum state transmission process in real time and record the quantum state transmission parameters. The dynamic scheduling module proposes an improved quantum communication dynamic resource allocation algorithm based on the quantum channel to dynamically adjust the quantum transmission resource allocation and channel usage. The visualization module is used to provide an intuitive graphical user interface to display the overall status, connection status and important parameters of the system.

[0006] Furthermore, the quantum state generation module initializes the quantum bits and places them in a specific quantum state. It transforms the initial quantum bits into the desired quantum state by using quantum gate operations, and generates the target quantum state by precisely controlling the interaction between the quantum bits.

[0007] Furthermore, the quantum channel control unit is used to manage and control the opening, closing and switching operations of the quantum channel according to actual communication needs, so as to dynamically adjust the state of the quantum channel and adjust the parameters of the quantum channel to meet the requirements of different communication tasks.

[0008] Furthermore, the quantum state transmission control unit selects specific quantum states for transmission according to communication requirements, and proposes a quantum neural network algorithm for improved data encoding to enhance the transmission performance of the quantum channel by generating, optimizing and monitoring entangled states, thereby achieving efficient and secure quantum communication.

[0009] Furthermore, the quantum neural network algorithm for improving data encoding is as follows: Assume that the column vector is the input layer of the quantum neural network, is the hidden layer of the quantum neural network, is the output layer of the quantum neural network, and Where N is the number of neurons, a1 is the value of the first neuron in the input layer, a2 is the value of the second neuron in the input layer, and a N is the value of the Nth neuron in the input layer, b1 is the value of the first neuron in the hidden layer, b2 is the value of the second neuron in the hidden layer, and b Nis the value of the Nth neuron in the hidden layer, c1 is the value of the first neuron in the output layer, c2 is the value of the second neuron in the output layer, and c N is the value of the Nth neuron in the output layer, (·) T is a transposition operation. Each layer in the input layer, hidden layer and output layer is connected by a weight matrix W. W1 is the weight matrix between the input layer and the first hidden layer. The quantum state information received by the first hidden layer is in, is the quantum state information received by the first hidden layer. After the quantum state information is nonlinearly transformed in the hidden layer, it is output as Then pass through the output layer and finally output Then according to the triplet quantum coherence entanglement resource, the l+1 qubit state is Among them, |S> is a multi-bit quantum entangled state, anc is an auxiliary quantum bit, |0> anc The auxiliary quantum bit anc is in the ground state |0>, |0...0> 1...l For l target qubits in the ground state |0>, |1> anc The auxiliary quantum bit is in the excited state |1>, |1...1> 1...l For l target quantum bits in the excited state |1>, in order to make the quantum entangled state more complex to enhance the security of quantum communication, the entanglement gate CONT gate is added to improve the quantum bit state, that is, Among them, |S′> is the improved multi-bit quantum entangled state, CONT 0,1 To control the gate between bit 0 and bit 1, CONT 0,2 To control the gate between bit 0 and bit 2, CONT 0,l To control the gate acting between the 0th bit and the 1st bit, |0> anc The auxiliary quantum bit anc is in the ground state |0>, |1> anc The auxiliary quantum bit anc is in the excited state |1>, |0>1 is the first bit in the ground state |0>, |0>2 is the second bit in the ground state |0>, |0> l = |0> for the first bit in the ground state, |1>1 for the first bit in the excited state |1>, |1>2 for the second bit in the excited state |1>, |1> l The lth bit is in the excited state |1>;

[0010] In order to improve the computational efficiency of quantum coding, quantum bit and quantum superposition state coding is proposed to realize parallel computing of quantum coding. Specifically, the coding of each quantum bit is in, is the quantum bit encoding of the jth individual in population algebra t, t is the population algebra, is the genotype of the first quantum gene in population algebra t, is the genotype of the second quantum gene in population algebra t, is the genotype of the mth quantum gene in population algebra t, is the phenotype of the first quantum gene in population algebra t, is the phenotype of the second quantum gene in population algebra t, is the phenotype of the mth quantum gene in population generation t, then the quantum population of generation t is expressed as Among them, Q(t) is the quantum population, is the first quantum state in the tth generation, is the second quantum state in the tth generation, is the nth quantum state in the tth generation, m is the quantum number, n is the population size, and the quantum bit uses the quantum gate to perform matrix transformation to complete the population evolution. The quantum bit operation uses the improved quantum rotation gate, that is, R(θ) is an improved quantum rotation gate, where θ is the rotation angle, i is the imaginary unit, and e (·) For exponential operations, the encoding process of each quantum bit is improved to Among them, PE is the quantum bit population evolution matrix, quantum crossover is based on the full interference of quantum coherence properties to perform crossover operations, and each quantum bit in the quantum bit population needs to perform crossover operations. In the process of quantum mutation, a probabilistic phase flip operator is proposed as a quantum mutation operator to achieve update and optimization, that is, Among them, U(ω) is a probabilistic phase-flipping quantum mutation operator, ω is the mutation probability, φ is the phase, and the improved data coding quantum neural network algorithm proposes to improve the quantum bit state by adding an entanglement gate to change the complexity of the quantum entangled state, enhance the security of quantum communication, and improve the quantum rotation gate to perform quantum coding population evolution, realize parallel computing of quantum coding, improve the computing efficiency of quantum coding, and realize the improved data coding quantum neural network algorithm to enhance the transmission performance of the quantum channel.

[0011] Furthermore, the quantum state verification unit verifies the consistency of the quantum states of the sender and receiver by comparing them, evaluates the fidelity loss of the quantum state during the communication process by measuring the fidelity between the transmitted quantum state and the original state, and records the verification results of the quantum state to verify the integrity of the quantum state and detect the error rate of the quantum state.

[0012] Furthermore, the quantum state supervision unit evaluates the efficiency and reliability of the transmission channel by monitoring the properties of the quantum state, optimizes the performance of quantum state transmission by adjusting the parameters of the transmission channel in real time, realizes real-time supervision of the entire quantum state transmission process and records the quantum state transmission parameters.

[0013] Furthermore, the dynamic scheduling module proposes an improved quantum communication dynamic resource allocation algorithm based on quantum channels to dynamically adjust quantum transmission resource allocation and channel usage, monitor and analyze the usage of various resources in the quantum transmission process in real time, and perform intelligent resource allocation based on real-time data. It also reasonably arranges the execution order of tasks according to the actual network status, actual task requirements, task priorities, and task characteristics, coordinates the use of resources between different tasks, and optimizes the performance and reliability of the entire quantum communication system through intelligent resource allocation and task scheduling.

[0014] Furthermore, the improved quantum communication dynamic resource allocation algorithm based on quantum channel is as follows: Assume that the communication node is Node = {n1, n2, ..., n z}, where Node is the communication node set, n1 is the first communication node, n2 is the second communication node, and n z is the zth communication node, assuming that in the pth communication node n p There is a data queue in the t time slot. The length of the data queue is L t , reaches communication node n in time slot t p The amount of data is D t , communication node n p The amount of data sent is S t , then the communication node n p The dynamic data queue length changes to L t+1 =max{L t -S t ,0}+D t , where L t+1 is the communication node n at time slot t+1 p The dynamic data queue length, L t is the communication node n at time slot t p The dynamic data queue length, S t is the communication node n at time slot t p The amount of data sent, Dt, is the amount of data arriving at communication node n at time slot t p The amount of data is proposed, and the Lyapunov function is proposed to convert the resource allocation objective function into an optimization problem for solution. The Lyapunov function is Among them, f(·) is the Lyapunov function, γ is the neighborhood, is the neighborhood state at time slot t, is the first neighborhood component of time slot t, is the second neighborhood component of time slot t, is the Zth neighborhood component of time slot t, and e(·) is the exponential operation;

[0015] After the Lyapunov function transforms the resource allocation objective function into an optimization problem, the reward function for dynamic allocation of communication resources is set, that is, Among them, r(π) is the reward function, π is the resource allocation strategy, μ is the normalization factor, Tmax is the maximum completion time of quantum bit data transmission, is the lower limit of the maximum completion time of quantum bit data transmission, e(·) is an exponential operation, and the cumulative reward is Among them, AR is the cumulative reward, r t is the reward at time slot t, r t+1 is the reward at time slot t+1, r t+2 is the reward at time slot t+2, r t+NN is the reward at time slot t+NN, γ is the discount factor, and n is a time integer between [0, NN]. Then, the resource allocation strategy is trained through the dual Q value network. The dual Q value network training process includes target Q network update and local Q network update. The dual Q value network training process is as follows: The target Q network update process is Q′1(s′, a′)=r+γmax a′ Q1(s, a), where Q′1(s′, a′) is the updated target Q value function, i.e., the target Q value of taking action a′ in the new state s′, Q1(s, a) is the Q value function of the target Q network, i.e., the target Q value of taking action a in state s, r is the reward obtained after taking action a in state s, γ is the discount factor, s′ is the new state obtained after taking action a, a′ is the best scheduling action in the scheduling strategy under the new state s′, and the local Q network update process is Q′2(s′, a′)=(1-α)Q2(s, a)+α[r+γmax a′ Q1(s′, a′)], where Q′2(s′, a′) is the updated local Q value function, i.e., the local Q value of taking action a′ in the new state s′, Q2(s, a) is the Q value function of the local Q network, i.e., the local Q value of taking action a in the state s, α is the learning rate that controls the update step of the Q value in each iteration, and in order to improve the stability of the training process, the Huber loss function is proposed for network training to improve the stability of the dual Q value network. The loss function of the dual Q value network training is Among them, loss is the loss function, NS is the number of samples, Huber(·) is the Huber loss function, and the improved quantum communication dynamic resource allocation algorithm based on quantum channel transforms the resource allocation objective function by proposing an improved Lyap unov function, and then proposes an improved reward function to train the dual Q value network. Finally, the Huber loss function is proposed to train the network to improve the stability of the training process, and realize the dynamic resource allocation of quantum communication transmission by the improved quantum communication dynamic resource allocation algorithm based on quantum channel.

[0016] Furthermore, the visualization module interacts with the system through a visualization interface to display the real-time status of dynamic scheduling, allowing users to understand the system's dynamic scheduling strategy at any time, while displaying the utilization of quantum communication resources to improve system efficiency. In addition, it also provides users with historical performance statistics and analysis functions, and displays the performance history data of the transmission channel through charts to help users understand the long-term trends and bottleneck problems of the system, and displays various information of the quantum communication system in a graphical form, providing users with a convenient and intuitive interactive interface.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] 1. Propose an improved data coding quantum neural network algorithm to enhance the transmission performance of quantum channels. The innovation of the present invention lies in that the improved data coding quantum neural network algorithm proposes to add an entanglement gate to improve the quantum bit state to change the complexity of the quantum entangled state, enhance the security of quantum communication, and improve the quantum rotation gate to perform quantum coding population evolution, realize parallel computing of quantum coding, improve the computing efficiency of quantum coding, and realize the improved data coding quantum neural network algorithm to enhance the transmission performance of quantum channels;

[0019] 2. An improved quantum communication dynamic resource allocation algorithm based on quantum channels is proposed to dynamically allocate resources for quantum communication transmission. The innovation of the present invention lies in that the improved quantum communication dynamic resource allocation algorithm based on quantum channels transforms the resource allocation objective function by proposing an improved Lyapunov function, then proposes an improved reward function for dual Q-value network training, and finally proposes a Huber loss function to train the network to improve the stability of the training process, thereby realizing the dynamic resource allocation of quantum communication transmission by the improved quantum communication dynamic resource allocation algorithm based on quantum channels. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The invention is further illustrated by the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0021] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, 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.

[0023] The control and dynamic supervision system of the quantum state transmission channel is characterized in that it includes a quantum state generation module, a quantum channel transmission control module, a quantum state verification and supervision module, a dynamic scheduling module, and a visualization module. The quantum state generation module is used to generate traditional binary bit data into a specific quantum state. The quantum channel transmission control module includes a quantum channel control unit and a quantum state transmission control unit. The quantum channel control unit is used for state control and parameter adjustment of the channel. The quantum state transmission control unit proposes a quantum neural network algorithm for improving data encoding to enhance the transmission performance of the quantum channel. The quantum state verification and supervision module includes a quantum state verification unit and a quantum state supervision unit. The quantum state verification unit is used to verify the integrity of the quantum state and detect the error rate of the quantum state. The quantum state supervision unit is used to supervise the entire quantum state transmission process in real time and record the quantum state transmission parameters. The dynamic scheduling module proposes an improved quantum communication dynamic resource allocation algorithm based on the quantum channel to dynamically adjust the quantum transmission resource allocation and channel use. The visualization module is used to provide an intuitive graphical user interface to display the overall status, connection status and important parameters of the system.

[0024] Preferably, the quantum state generation module initializes the quantum bits and places them in a specific quantum state. By using quantum gate operations, the initial quantum bits are transformed into the desired quantum state, which is usually achieved by applying appropriate pulses and magnetic fields. The target quantum state is produced by precisely controlling the interaction between the quantum bits. The generated quantum state is ready for transmission in the quantum channel, which involves adjusting the quantum bit state to suit the specific requirements of the quantum channel.

[0025] Preferably, the quantum channel control unit is used to manage and control the opening, closing and switching operations of the quantum channel according to actual communication needs, so as to dynamically adjust the state of the quantum channel and adjust the parameters of the quantum channel, including the frequency, polarization and time delay of the photon state, to meet the requirements of different communication tasks.

[0026] Preferably, the quantum state transmission control unit selects a specific quantum state for transmission according to communication requirements, and proposes a quantum neural network algorithm for improving data encoding to enhance the transmission performance of the quantum channel by generating, optimizing and monitoring entangled states, so as to ensure that the generation of quantum states complies with communication protocols and system requirements, and realize efficient and secure quantum communication.

[0027] Specifically, the quantum neural network algorithm for improving data encoding is as follows: Assume that the column vector is the input layer of the quantum neural network, is the hidden layer of the quantum neural network, is the output layer of the quantum neural network, and Where N is the number of neurons, a1 is the value of the first neuron in the input layer, a2 is the value of the second neuron in the input layer, and a N is the value of the Nth neuron in the input layer, b1 is the value of the first neuron in the hidden layer, b2 is the value of the second neuron in the hidden layer, and b N is the value of the Nth neuron in the hidden layer, c1 is the value of the first neuron in the output layer, c2 is the value of the second neuron in the output layer, and c N is the value of the Nth neuron in the output layer, (·) T is a transposition operation. Each layer in the input layer, hidden layer and output layer is connected by a weight matrix W. W1 is the weight matrix between the input layer and the first hidden layer. The quantum state information received by the first hidden layer is in, is the quantum state information received by the first hidden layer. After the quantum state information is nonlinearly transformed in the hidden layer, it is output as Then pass through the output layer and finally output Then according to the triplet quantum coherence entanglement resource, the l+1 qubit state is Among them, |S> is a multi-bit quantum entangled state, anc is an auxiliary quantum bit, |0> anc The auxiliary quantum bit anc is in the ground state |0>, |0...0> 1...l For l target qubits in the ground state |0>, |1> anc The auxiliary quantum bit is in the excited state |1>, |1...1> 1...l For l target quantum bits in the excited state |1>, in order to make the quantum entangled state more complex to enhance the security of quantum communication, the entanglement gate CONT gate is added to improve the quantum bit state, that is, Among them, |S′> is the improved multi-bit quantum entangled state, CONT 0,1 To control the gate between bit 0 and bit 1, CONT 0,2To control the gate between bit 0 and bit 2, CONT 0,l To control the gate acting between the 0th bit and the 1st bit, |0> anc The auxiliary quantum bit anc is in the ground state |0>, |1> anc The auxiliary quantum bit anc is in the excited state |1>, |0>1 is the first bit in the ground state |0>, |0>2 is the second bit in the ground state |0>, |0> l = |0> for the first bit in the ground state, |1>1 for the first bit in the excited state |1>, |1>2 for the second bit in the excited state |1>, |1> l The lth bit is in the excited state |1>;

[0028] In order to improve the computational efficiency of quantum coding, quantum bit and quantum superposition state coding is proposed to realize parallel computing of quantum coding. Specifically, the coding of each quantum bit is in, is the quantum bit encoding of the jth individual in population algebra t, t is the population algebra, is the genotype of the first quantum gene in population algebra t, is the genotype of the second quantum gene in population algebra t, is the genotype of the mth quantum gene in population algebra t, is the phenotype of the first quantum gene in population algebra t, is the phenotype of the second quantum gene in population algebra t, is the phenotype of the mth quantum gene in population generation t, then the quantum population of generation t is expressed as Among them, Q(t) is the quantum population, is the first quantum state in the tth generation, is the second quantum state in the tth generation, is the nth quantum state in the tth generation, m is the quantum number, n is the population size, and the quantum bit uses the quantum gate to perform matrix transformation to complete the population evolution. The quantum bit operation uses the improved quantum rotation gate, that is, R(θ) is an improved quantum rotation gate, where θ is the rotation angle, i is the imaginary unit, and e (·) For exponential operations, the encoding process of each quantum bit is improved to Among them, PE is the quantum bit population evolution matrix, quantum crossover is based on the full interference of quantum coherence properties to perform crossover operations, and each quantum bit in the quantum bit population needs to perform crossover operations. In the process of quantum mutation, a probabilistic phase flip operator is proposed as a quantum mutation operator to achieve update and optimization, that is, Among them, U(ω) is a probabilistic phase-flipping quantum mutation operator, ω is the mutation probability, φ is the phase, and the improved data coding quantum neural network algorithm proposes to improve the quantum bit state by adding an entanglement gate to change the complexity of the quantum entangled state, enhance the security of quantum communication, and improve the quantum rotation gate to perform quantum coding population evolution, realize parallel computing of quantum coding, improve the computing efficiency of quantum coding, and realize the improved data coding quantum neural network algorithm to enhance the transmission performance of the quantum channel.

[0029] Preferably, the quantum state verification unit verifies the consistency of the quantum states of the transmitting and receiving ends by comparing them, detects whether there are any errors in the transmission process, measures the fidelity between the transmitted quantum state and the original state, evaluates the fidelity loss of the quantum state during the communication process, thereby providing information about the channel quality, and records the verification results of the quantum state to verify the integrity of the quantum state and detect the error rate of the quantum state.

[0030] Preferably, the quantum state supervision unit evaluates the efficiency and reliability of the transmission channel by monitoring the properties of the quantum state, such as entanglement, phase and amplitude, and optimizes the performance of quantum state transmission by adjusting the parameters of the transmission channel in real time, including adjusting the intensity of the photon source, changing the transmission path and adjusting the demodulator settings, thereby achieving real-time supervision of the entire quantum state transmission process and recording the quantum state transmission parameters.

[0031] Preferably, the dynamic scheduling module proposes an improved quantum communication dynamic resource allocation algorithm based on quantum channels to dynamically adjust quantum transmission resource allocation and channel usage, monitor and analyze in real time the usage of various resources in the quantum transmission process, including quantum channels, quantum bits and quantum gates, perform intelligent resource allocation based on real-time data, and reasonably arrange the execution order of tasks according to the actual network status, actual task requirements, task priorities and task characteristics, coordinate the use of resources between different tasks, and optimize the performance and reliability of the entire quantum communication system through intelligent resource allocation and task scheduling.

[0032] Specifically, the improved quantum communication dynamic resource allocation algorithm based on quantum channel is as follows: Assume that the communication node is Node = {n1, n2, ..., n z}, where Node is the communication node set, n1 is the first communication node, n2 is the second communication node, and n z is the zth communication node, assuming that in the pth communication node n p There is a data queue in the t time slot. The length of the data queue is L t , reaches communication node n in time slot t p The amount of data is D t , communication node n p The amount of data sent is St , then the communication node n p The dynamic data queue length changes to L t+1 =max{L t -S t ,0}+D t , where L t+1 is the communication node n at time slot t+1 p The dynamic data queue length, L t is the communication node n at time slot t p The dynamic data queue length, S t is the communication node n at time slot t p The amount of data sent, D t is the node n that arrives at time slot t p The amount of data, the queue length will not be negative, so max{L t -S t , 0} to ensure that the queue length will not become negative, and the Lyapunov function is proposed to convert the resource allocation objective function into an optimization problem for solution. The Lyapunov function is Among them, f(·) is the Lyapunov function, γ is the neighborhood, is the neighborhood state at time slot t, is the first neighborhood component of time slot t, is the second neighborhood component of time slot t, is the Zth neighborhood component of time slot t, e (·) is the exponential operation;

[0033] After the Lyapunov function transforms the resource allocation objective function into an optimization problem, the reward function for dynamic allocation of communication resources is set, that is, Among them, r(π) is the reward function, π is the resource allocation strategy, μ is the normalization factor, T max is the maximum completion time of quantum bit data transmission, is the lower limit of the maximum completion time of quantum bit data transmission, e (·) is an exponential operation, and the cumulative reward is Among them, AR is the cumulative reward, r t is the reward at time slot t, r t+1 is the reward at time slot t+1, r t+2 is the reward at time slot t+2, r t+NNis the reward at time slot t+NN, γ is the discount factor, n is the time integer between [0,NN], and then the resource allocation strategy is trained through the dual Q value network. The dual Q value network training process includes the target Q network update and the local Q network update. The dual Q value network training process is as follows: The target Q network update process is Q′1(s′,a′)=r+γmax a′ Q1(s,a), where Q′1(s′,a′) is the updated target Q value function, i.e., the target Q value of taking action a′ in the new state s′, Q1(s,a) is the Q value function of the target Q network, i.e., the target Q value of taking action a in state s, r is the reward obtained after taking action a in state s, γ is the discount factor, s′ is the new state obtained after taking action a, a′ is the best scheduling action in the scheduling strategy under the new state s′, and the local Q network update process is Q′2(s′,a′)=(1-α)Q2(s,a)+α[r+γmax a′ Q1(s′,a′)], where Q′2(s′,a′) is the updated local Q value function, i.e., the local Q value of taking action a′ in the new state s′, Q2(s,a) is the Q value function of the local Q network, i.e., the local Q value of taking action a in the state s, α is the learning rate that controls the update step of the Q value in each iteration, and in order to improve the stability of the training process, the Huber loss function is proposed for network training to improve the stability of the dual Q value network. The loss function of the dual Q value network training is Among them, loss is the loss function, NS is the number of samples, Huber(·) is the Huber loss function, and the improved quantum communication dynamic resource allocation algorithm based on quantum channel transforms the resource allocation objective function by proposing an improved Lyapunov function, and then proposes an improved reward function to train the dual Q value network. Finally, the Huber loss function is proposed to train the network to improve the stability of the training process, and realize the dynamic resource allocation of quantum communication transmission by the improved quantum communication dynamic resource allocation algorithm based on quantum channel.

[0034] Preferably, the visualization module interacts with the system through a visualization interface, such as manually adjusting parameters, starting and stopping tasks, and displaying the real-time status of dynamic scheduling, including the allocation of quantum bits and the scheduling progress information of tasks, so that users can understand the dynamic scheduling strategy of the system at any time, and at the same time display the utilization of quantum communication resources, including transmission bandwidth, quantum bit status and transmission delay, to improve system efficiency. In addition, it also provides users with historical performance statistics and analysis functions, displays the performance history data of the transmission channel through charts, helps users understand the long-term trend and bottleneck problems of the system, and displays various information of the quantum communication system in a graphical form, providing users with a convenient and intuitive interactive interface.

[0035] A control and dynamic supervision system for a quantum state transmission channel is proposed, which is used for dynamic allocation of communication resources in the process of quantum communication transmission. A control and supervision system is provided for the quantum state transmission channel through the integration of a quantum state generation module, a quantum channel transmission control module, a quantum state verification and supervision module, a dynamic scheduling module and a visualization module. An improved data coding quantum neural network algorithm is proposed to enhance the transmission performance of the quantum channel. The innovation of the present invention lies in that the improved data coding quantum neural network algorithm improves the quantum bit state by adding an entanglement gate to change the complexity of the quantum entangled state, thereby enhancing the security of quantum communication, and improves the quantum rotating gate to perform quantum coding population evolution, thereby realizing parallel computing of quantum coding and improving the computing efficiency of quantum coding, thereby realizing the improved data coding quantum neural network algorithm to enhance the transmission performance of the quantum channel. An improved quantum communication dynamic resource allocation algorithm based on quantum channels is proposed to dynamically allocate resources for quantum communication transmission. The innovation of the present invention lies in that the improved quantum communication dynamic resource allocation algorithm based on quantum channels proposes an improved Lyapunov function for the resource allocation objective function. The problem is transformed, and then an improved reward function is proposed for dual Q value network training. Finally, the Huber loss function is proposed to train the network to improve the stability of the training process, and the dynamic resource allocation of quantum communication transmission based on the improved quantum communication dynamic resource allocation algorithm of the quantum channel is realized, and the working effect of the control and dynamic supervision system of the quantum state transmission channel is effectively improved, and a more comprehensive and accurate technical support is provided for the control and dynamic supervision system of the quantum state transmission channel, and a better decision support is provided for the control and dynamic supervision system of the safe, scientific and efficient quantum state transmission channel. At the same time, the present invention relates to quantum entanglement algorithm, data coding algorithm and neural network algorithm, which provide people with a convenient and efficient control and dynamic supervision system of the quantum state transmission channel, and can also consolidate the foundation for the development of other application fields. In the era of the heyday of quantum communication transmission and dynamic resource allocation, the integration of quantum communication transmission and dynamic resource allocation has laid a solid foundation for the development of multi-field integration, and can be applied to multiple industries and fields in the market, providing a new development direction for the integration of quantum communication transmission and dynamic resource allocation, and contributing important application value to the field of quantum communication.

[0036] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A control and dynamic supervision system for quantum state transmission channels, characterized in that: It includes a quantum state generation module, a quantum channel transmission control module, a quantum state verification and supervision module, a dynamic scheduling module, and a visualization module. The quantum state generation module is used to generate traditional binary bit data into a specific quantum state. The quantum channel transmission control module includes a quantum channel control unit and a quantum state transmission control unit. The quantum channel control unit is used for channel state control and parameter adjustment. The quantum state transmission control unit proposes an improved data encoding quantum neural network algorithm to enhance the transmission performance of the quantum channel. The quantum state verification and supervision module includes a quantum state verification unit and a quantum state supervision unit. The quantum state verification unit is used to verify the integrity of the quantum state and detect the error rate of the quantum state. The quantum state supervision unit is used to monitor the entire quantum state transmission process in real time and record the quantum state transmission parameters. The dynamic scheduling module proposes an improved quantum communication dynamic resource allocation algorithm based on quantum channels to dynamically adjust quantum transmission resource allocation and channel usage. The visualization module is used to provide an intuitive graphical user interface to display the overall status, connection status and important parameters of the system.

2. The control and dynamic supervision system of the quantum state transmission channel according to claim 1 is characterized in that: The quantum state generation module initializes the quantum bits and places them in a specific quantum state. It then transforms the initial quantum bits into the desired quantum state by using quantum gate operations, and generates the target quantum state by precisely controlling the interactions between the quantum bits.

3. The control and dynamic supervision system of the quantum state transmission channel according to claim 1 is characterized in that: The quantum channel control unit is used to manage and control the opening, closing and switching operations of the quantum channel according to actual communication needs, so as to dynamically adjust the state of the quantum channel and adjust the parameters of the quantum channel to meet the requirements of different communication tasks.

4. The control and dynamic supervision system of the quantum state transmission channel according to claim 1 is characterized in that: The quantum state transmission control unit selects specific quantum states for transmission according to communication requirements, and proposes a quantum neural network algorithm for improved data encoding to enhance the transmission performance of the quantum channel by generating, optimizing and monitoring entangled states, thereby achieving efficient and secure quantum communication.

5. The control and dynamic supervision system of the quantum state transmission channel according to claim 4 is characterized in that: The quantum neural network algorithm for improving data encoding is as follows: Assume that the column vector is the input layer of the quantum neural network, is the hidden layer of the quantum neural network, is the output layer of the quantum neural network, and Where N is the number of neurons, a1 is the value of the first neuron in the input layer, a2 is the value of the second neuron in the input layer, and a N is the value of the Nth neuron in the input layer, b1 is the value of the first neuron in the hidden layer, b2 is the value of the second neuron in the hidden layer, and b N is the value of the Nth neuron in the hidden layer, c1 is the value of the first neuron in the output layer, c2 is the value of the second neuron in the output layer, and c N is the value of the Nth neuron in the output layer, (·) T is a transposition operation. Each layer in the input layer, hidden layer and output layer is connected by a weight matrix W. W1 is the weight matrix between the input layer and the first hidden layer. The quantum state information received by the first hidden layer is in, is the quantum state information received by the first hidden layer. After the quantum state information is nonlinearly transformed in the hidden layer, it is output as Then pass through the output layer and finally output Then according to the triplet quantum coherence entanglement resource, the l+1 qubit state is Among them, |S> is a multi-bit quantum entangled state, anc is an auxiliary quantum bit, |0> anc The auxiliary quantum bit anc is in the ground state |0>, |0...0> 1...l For l target qubits in the ground state |0>, |1> anc The auxiliary quantum bit is in the excited state |1>, |1...1> 1...l For l target quantum bits in the excited state |1>, in order to make the quantum entangled state more complex to enhance the security of quantum communication, the entanglement gate CONT gate is added to improve the quantum bit state, that is, Among them, |S′> is the improved multi-bit quantum entangled state, CONT 0,1 To control the gate between bit 0 and bit 1, CONT 0,2 To control the gate between bit 0 and bit 2, CONT 0,l To control the gate acting between the 0th bit and the 1st bit, |0> anc The auxiliary quantum bit anc is in the ground state |0>, |1> anc The auxiliary quantum bit anc is in the excited state |1>, |0>1 is the first bit in the ground state |0>, |0>2 is the second bit in the ground state |0>, |0> l = |0> for the first bit in the ground state, |1>1 for the first bit in the excited state |1>, |1>2 for the second bit in the excited state |1>, |1> l The lth bit is in the excited state |1>; In order to improve the computational efficiency of quantum coding, quantum bit and quantum superposition state coding is proposed to realize parallel computing of quantum coding. Specifically, the coding of each quantum bit is in, is the quantum bit encoding of the kth individual in population algebra t, t is the population algebra, is the genotype of the first quantum gene in population algebra t, is the genotype of the second quantum gene in population algebra t, is the genotype of the mth quantum gene in population algebra t, is the phenotype of the first quantum gene in population algebra t, is the phenotype of the second quantum gene in population algebra t, is the phenotype of the mth quantum gene in population generation t, then the quantum population of generation t is expressed as Among them, Q(t) is the quantum population, is the first quantum state in the tth generation, is the second quantum state in the tth generation, is the nth quantum state in the tth generation, n is the population size, and the quantum bit uses the quantum gate to perform matrix transformation to complete the population evolution. The quantum bit operation uses the improved quantum rotation gate, that is, R(θ) is an improved quantum rotation gate, where θ is the rotation angle, i is the imaginary unit, and e (·) For exponential operations, the encoding process of each quantum bit is improved to Among them, PE is the quantum bit population evolution matrix, quantum crossover is based on the full interference of quantum coherence properties to perform crossover operations, and each quantum bit in the quantum bit population needs to perform crossover operations. In the process of quantum mutation, a probabilistic phase flip operator is proposed as a quantum mutation operator to achieve update and optimization, that is, Among them, U(ω) is a probabilistic phase-flipping quantum mutation operator, ω is the mutation probability, φ is the phase, and the improved data coding quantum neural network algorithm proposes to improve the quantum bit state by adding an entanglement gate to change the complexity of the quantum entangled state, enhance the security of quantum communication, and improve the quantum rotation gate to perform quantum coding population evolution, realize parallel computing of quantum coding, improve the computing efficiency of quantum coding, and realize the improved data coding quantum neural network algorithm to enhance the transmission performance of the quantum channel.

6. The control and dynamic supervision system of the quantum state transmission channel according to claim 1 is characterized in that: The quantum state verification unit verifies the consistency of the quantum states of the sender and receiver by comparing them, evaluates the fidelity loss of the quantum state during the communication process by measuring the fidelity between the transmitted quantum state and the original state, and records the verification results of the quantum state to verify the integrity of the quantum state and detect the error rate of the quantum state.

7. The control and dynamic supervision system of the quantum state transmission channel according to claim 1 is characterized in that: The quantum state supervision unit evaluates the efficiency and reliability of the transmission channel by monitoring the properties of the quantum state, optimizes the performance of quantum state transmission by adjusting the parameters of the transmission channel in real time, realizes real-time supervision of the entire quantum state transmission process and records the quantum state transmission parameters.

8. The control and dynamic supervision system of the quantum state transmission channel according to claim 1 is characterized in that: The dynamic scheduling module proposes an improved quantum communication dynamic resource allocation algorithm based on quantum channels to dynamically adjust quantum transmission resource allocation and channel usage, monitor and analyze the usage of various resources in the quantum transmission process in real time, and perform intelligent resource allocation based on real-time data. It also arranges the execution order of tasks reasonably according to the actual network status, actual task requirements, task priority and task characteristics, coordinates the use of resources between different tasks, and optimizes the performance and reliability of the entire quantum communication system through intelligent resource allocation and task scheduling.

9. The control and dynamic supervision system of the quantum state transmission channel according to claim 8 is characterized in that: The improved quantum communication dynamic resource allocation algorithm based on quantum channel is as follows: Assume that the communication node is Node = {n1, n2, ..., nz}, where Node is the communication node set, n1 is the first communication node, n2 is the second communication node, and n z is the zth communication node, assuming that in the pth communication node n p There is a data queue in the t time slot. The length of the data queue is L t , reaches communication node n in time slot t p The amount of data is D t , communication node n p The amount of data sent is S t , then the communication node n p The dynamic data queue length changes to L t+1 =max{L t -S t ,0}+D t , where L t+1 is the communication node n at time slot t+1 p The dynamic data queue length, L t is the communication node n at time slot t p The dynamic data queue length, S t is the communication node n at time slot t p The amount of data sent, D t is the node n that arrives at time slot t p The amount of data is proposed, and the Lyapunov function is proposed to convert the resource allocation objective function into an optimization problem for solution. The Lyapunov function is Among them, f(·) is the Lyapunov function, γ is the neighborhood, is the neighborhood state at time slot t, is the first neighborhood component of time slot t, is the second neighborhood component of time slot t, is the Zth neighborhood component of time slot t, e (·) is the exponential operation; After the Lyapunov function transforms the resource allocation objective function into an optimization problem, the reward function for dynamic allocation of communication resources is set, that is, Among them, r(π) is the reward function, π is the resource allocation strategy, μ is the normalization factor, T max is the maximum completion time of quantum bit data transmission, is the lower limit of the maximum completion time of quantum bit data transmission, e (·) is an exponential operation, and the cumulative reward is Among them, AR is the cumulative reward, r t is the reward at time slot t, r t+1 is the reward at time slot t+1, r t+2 is the reward at time slot t+2, r t+NN is the reward at time slot t+NN, γ is the discount factor, n is the time integer between [0,NN], and then the resource allocation strategy is trained through the dual Q value network. The dual Q value network training process includes the target Q network update and the local Q network update. The dual Q value network training process is as follows: The target Q network update process is Q′1(s′,a′)=r+γmax a′ Q1(s,a), where Q′1(s′,a′) is the updated target Q value function, i.e., the target Q value of taking action a′ in the new state s′, Q1(s,a) is the Q value function of the target Q network, i.e., the target Q value of taking action a in state s, r is the reward obtained after taking action a in state s, γ is the discount factor, s′ is the new state obtained after taking action a, a′ is the best scheduling action in the scheduling strategy under the new state a′, and the local Q network update process is Q′2(s′,a′)=(1-α)Q2(a,α)+α[r+γmax a′ Q1(s′,a′)], where Q′2(s′,a′) is the updated local Q value function, i.e., the local Q value of taking action a′ in the new state s′, Q2(s,a) is the Q value function of the local Q network, i.e., the local Q value of taking action a in the state s, α is the learning rate that controls the update step of the Q value in each iteration, and in order to improve the stability of the training process, the Huber loss function is proposed for network training to improve the stability of the dual Q value network. The loss function of the dual Q value network training is Among them, loss is the loss function, NS is the number of samples, Huber(·) is the Huber loss function, and the improved quantum communication dynamic resource allocation algorithm based on quantum channel transforms the resource allocation objective function by proposing an improved Lyapunov function, and then proposes an improved reward function to train the dual Q value network. Finally, the Huber loss function is proposed to train the network to improve the stability of the training process, and realize the dynamic resource allocation of quantum communication transmission by the improved quantum communication dynamic resource allocation algorithm based on quantum channel.

10. The control and dynamic supervision system of the quantum state transmission channel according to claim 1, characterized in that: The visualization module interacts with the system through a visualization interface, displaying the real-time status of dynamic scheduling, allowing users to understand the system's dynamic scheduling strategy at any time, while displaying the utilization of quantum communication resources to improve system efficiency. In addition, it also provides users with historical performance statistics and analysis functions, and displays the performance history data of the transmission channel through charts to help users understand the long-term trends and bottleneck problems of the system. It displays various information of the quantum communication system in a graphical form, providing users with a convenient and intuitive interactive interface.

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