An optimization method, apparatus, and device for a quantum key distribution handshake protocol.

Abstract

This invention provides an optimization method, apparatus, and device for a quantum key distribution handshake protocol. By preparing standard quantum states and establishing a spin-staggered timing sequence, time-division multiplexing and parallel processing of multiple quantum channels are achieved. A pre-matched codebook stores historically successful handshake patterns, supporting fast pattern matching. The authentication and negotiation sub-processes are decoupled through a parallel verification architecture, allowing them to execute in parallel at different time phases. A handshake state matrix is ​​generated by fusing quantum signature tokens and key negotiation parameters, achieving a global representation of the handshake process. Success probability prediction is performed based on quantum entanglement correlation strength analysis, triggering a pre-generation mechanism for the candidate key pool. Quantum state weaving and fusion of keys are adjusted using local, non-local, and mixed entanglement distributions to generate enhanced keys. Finally, optimized distribution is completed by combining the spin-staggered timing sequence, achieving parallelization of authentication and negotiation, intelligent resource allocation, and real-time state monitoring.

Description

Technical Field

[0001] This invention relates to the field of quantum communication technology, and in particular to an optimization method, apparatus, and device for a quantum key distribution handshake protocol. Background Technology

[0002] Quantum key distribution (QKD) is a core technology in quantum communication, and its handshake protocol determines the efficiency and security of the entire key distribution process. Existing QKD handshake protocols primarily employ a sequential execution mode, with identity verification and parameter negotiation processes interdependent, resulting in long handshake delays that are difficult to meet the real-time requirements of high-speed quantum communication networks.

[0003] Furthermore, traditional handshake protocols lack efficient utilization of quantum channel time slots, failing to achieve multiplexing and intelligent scheduling, resulting in a waste of quantum resources. Simultaneously, existing schemes lack a state evaluation mechanism during the handshake process, unable to dynamically adjust processing strategies based on real-time conditions, leading to a sharp decline in system performance when channel quality fluctuates. Therefore, a new optimization method is urgently needed to improve the performance of quantum key distribution handshake protocols. Summary of the Invention

[0004] This invention provides an optimization method, apparatus, and device for quantum key distribution handshake protocols. It aims to achieve multi-channel parallel processing by introducing a spin staggered timing mechanism, establishing a pre-matched codebook to accelerate the handshake process, constructing a parallel verification architecture to separate authentication and negotiation processes, using a handshake state matrix for real-time state monitoring and prediction, and employing quantum entanglement distribution adjustment to optimize resource allocation, ultimately improving handshake efficiency and enhancing system robustness.

[0005] The first aspect of this invention proposes an optimization method for a quantum key distribution handshake protocol, comprising the following steps:

[0006] The entangled state distribution data of the quantum channel and the handshake delay record of the classical channel are collected. The qubit error rate is extracted based on the entangled state distribution data. Fluctuation analysis is performed on the qubit error rate to obtain the quantum noise mode. The detection time set is derived in reverse from the quantum noise mode.

[0007] The handshake delay record is used to separate the transmission delay component and the processing delay component. The transmission delay component and the processing delay component are mapped to spin vectors. The spin vectors are phase-modulated to form a spin-shaving timing sequence. A parallel verification architecture is established based on the spin-shaving timing sequence. The step of phase-modulating the spin vector to form the spin-shaving timing sequence includes: performing eigenstate decomposition on the spin vector to obtain an upper spin eigenstate, a lower spin eigenstate, and a superimposed spin eigenstate; establishing a forward phase modulation rule based on the upper spin eigenstate and an inverse phase modulation rule based on the lower spin eigenstate; and using the superimposed spin eigenstate to coordinate the forward phase modulation rule and the inverse phase modulation rule to form the spin-shaving timing sequence.

[0008] Multiple parallel execution windows are set according to the parallel verification architecture and the probe timing set. Pre-verification probe packets are sent through the parallel execution windows and responses are collected to obtain a verification response sequence. Verification feature codes are generated based on the verification response sequence. A pre-matching codebook is constructed using the verification feature codes.

[0009] The parallel verification architecture is used to construct an authentication sub-process and a negotiation sub-process. The authentication sub-process generates a quantum signature token, and the negotiation sub-process transmits key negotiation parameters. The quantum signature token and the key negotiation parameters are fused in real time to form a handshake state matrix.

[0010] The success probability value is obtained by performing handshake prediction analysis using the handshake state matrix and the pre-matched codebook. The key pre-generation mechanism is triggered together with the success probability value and the handshake state matrix. A preliminary key is prepared using the key pre-generation mechanism. A candidate key pool is constructed using the preliminary key.

[0011] The validity of the quantum signature token is verified to generate a verification result. Based on the verification result, the candidate key pool is activated to obtain a usable key. The usable key and the key negotiation parameters are fused into segmented quantum states according to the handshake state matrix to generate an enhanced key.

[0012] The final distribution key is generated using the enhanced key and the spin staggered timing, thus optimizing the quantum key distribution handshake protocol.

[0013] In some embodiments, extracting the qubit error rate based on the entangled state distribution data includes: performing quantum coherence reduction tracking on the entangled state distribution data to generate a coherence decay curve; identifying coherence loss nodes based on the coherence decay curve; and performing information entropy increase analysis using the coherence loss nodes to form the qubit error rate.

[0014] In some embodiments, generating a verification signature based on the verification response sequence includes: performing quantum measurement inverse reconstruction on the verification response sequence to obtain original quantum state information; extracting a quantum coherence fingerprint based on the original quantum state information; performing quantum state overlap analysis using the quantum coherence fingerprint to obtain overlap features; and quantum encoding the overlap features to form a verification signature.

[0015] In some embodiments, the step of fusing the quantum signature token and the key negotiation parameters in real time to form a handshake state matrix includes: performing quantum state analysis on the quantum signature token to obtain a token state vector; performing parameter vectorization processing on the key negotiation parameters to obtain a negotiation state vector; performing tensor product operation on the token state vector and the negotiation state vector to obtain a fused state matrix; and performing state normalization processing on the fused state matrix to form a handshake state matrix.

[0016] In some embodiments, the step of using the handshake state matrix and the pre-matched codebook to perform handshake prediction analysis to obtain a success probability value includes: extracting the quantum entanglement correlation strength from the handshake state matrix; performing long-range quantum state correlation analysis based on the quantum entanglement correlation strength to obtain a correlation probability distribution; and using the pre-matched codebook to perform quantum measurement prediction on the correlation probability distribution to obtain a success probability value.

[0017] In some embodiments, the step of fusing the available key and the key negotiation parameters into segmented quantum states according to the handshake state matrix to generate an enhanced key includes: establishing a quantum entanglement distribution adjustment based on the handshake state matrix, wherein the quantum entanglement distribution adjustment includes local entanglement distribution adjustment, non-local entanglement distribution adjustment, and hybrid entanglement distribution adjustment; using the local entanglement distribution adjustment and the non-local entanglement adjustment to perform segmented entanglement weaving on the available key to obtain an entangled key segment; using the hybrid entanglement distribution adjustment to perform segmented entanglement weaving on the key negotiation parameters to obtain an entangled parameter segment; and generating an enhanced key by fusing the quantum states of the entangled key segment and the entangled parameter segment.

[0018] In some embodiments, the step of performing a tensor product operation on the token state vector and the negotiation state vector to obtain a fused state matrix includes: performing a coordination analysis on the token state vector and the negotiation state vector to obtain strong coordination components and weak coordination components; performing a balance characteristic analysis based on the strong coordination components and weak coordination components to obtain a balance adjustment factor; constructing a coordination balance network using the balance adjustment factor; and performing a vector fusion operation based on the coordination balance network to form a fused state matrix.

[0019] A second aspect of the present invention provides an optimization apparatus for a quantum key distribution handshake protocol, comprising:

[0020] The data acquisition module is used to acquire entangled state distribution data of the quantum channel and handshake delay records of the classical channel. It extracts the qubit error rate based on the entangled state distribution data, performs fluctuation analysis on the qubit error rate to obtain the quantum noise mode, and derives the detection time set through the quantum noise mode.

[0021] An architecture construction module is used to separate transmission delay components and processing delay components using the handshake delay record, map the transmission delay components and the processing delay components to spin vectors, perform phase modulation on the spin vectors to form a spin-shaving timing sequence, and establish a parallel verification architecture based on the spin-shaving timing sequence. The step of performing phase modulation on the spin vectors to form the spin-shaving timing sequence includes: performing eigenstate decomposition on the spin vectors to obtain upper spin eigenstates, lower spin eigenstates, and superimposed spin eigenstates; establishing a forward phase modulation rule based on the upper spin eigenstates and an inverse phase modulation rule based on the lower spin eigenstates; and coordinating the forward and inverse phase modulation rules using the superimposed spin eigenstates to form the spin-shaving timing sequence.

[0022] The CAPTCHA module is used to set multiple parallel execution windows according to the parallel verification architecture and the probe timing set, send pre-verification probe packets through the parallel execution windows and collect responses to obtain a verification response sequence, generate verification feature codes based on the verification response sequence, and construct a pre-matching codebook using the verification feature codes.

[0023] The state matrix module is used to construct an authentication sub-process and a negotiation sub-process using the parallel verification architecture. The authentication sub-process generates a quantum signature token, and the negotiation sub-process transmits key negotiation parameters to be obtained. The quantum signature token and the key negotiation parameters are fused in real time to form a handshake state matrix.

[0024] The key pool construction module is used to perform handshake prediction analysis using the handshake state matrix and the pre-matched codebook to obtain a success probability value, trigger a key pre-generation mechanism together with the success probability value and the handshake state matrix, prepare a preliminary key with the help of the key pre-generation mechanism, and construct a candidate key pool using the preliminary key;

[0025] The key enhancement module is used to perform legality verification based on the quantum signature token to generate a verification result, activate the candidate key pool based on the verification result to obtain a usable key, and fuse the usable key with the key negotiation parameters according to the handshake state matrix in a segmented quantum state to generate an enhanced key.

[0026] The key distribution module is used to generate the final distribution key using the enhanced key and the spin staggered timing, thereby optimizing the quantum key distribution handshake protocol.

[0027] A third aspect of the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of an optimization method for a quantum key distribution handshake protocol disclosed in the first aspect.

[0028] The beneficial effects of this invention are reflected in the following points: First, by combining spin-shifted timing and parallel verification architecture, the handshake process is comprehensively optimized. The authentication and negotiation sub-processes are separated and executed in parallel on different time phases. Simultaneously, multi-channel staggered scheduling fully utilizes quantum channel resources, allowing processes that previously required sequential waiting to proceed simultaneously, significantly improving handshake efficiency. Second, the predictive analysis of the handshake state matrix and the candidate key pool mechanism enhance the intelligence of the handshake process. By extracting quantum entanglement correlation strength and calculating the success probability in real time, key pre-generation can be initiated before the handshake is completed, effectively reducing waiting time. The establishment of a pre-matched codebook allows the handshake process to quickly identify and adopt historical successful patterns, avoiding repeated exploration. Finally, the enhanced key generated using quantum entanglement distribution adjustment technology improves key security and functional integrity. Through local, non-local, and hybrid entanglement distribution adjustment, the available key is deeply integrated with the structured features of the handshake state matrix. The generated enhanced key not only retains the original key information but also embeds the quantum correlation characteristics of the handshake state, making the finally distributed key more resistant to interference and with state traceability.

[0029] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0030] The accompanying drawings illustrate specific examples of the technical solutions described in this invention and, together with the detailed embodiments, form part of the specification, serving to explain the technical solutions, principles, and effects of this invention.

[0031] Unless otherwise specified or defined, the same reference numerals in different figures represent the same or similar technical features, and different reference numerals may be used to represent the same or similar technical features.

[0032] Figure 1 This is a flowchart illustrating an optimized method for a quantum key distribution handshake protocol according to the present invention.

[0033] Figure 2 This is a structural block diagram of an optimized device for a quantum key distribution handshake protocol according to the present invention.

[0034] Figure 3This is a schematic diagram of the structure of a computer device according to the present invention. Detailed Implementation

[0035] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0036] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0037] The technical solutions of the embodiments of this application will be described below.

[0038] like Figure 1 As shown, this embodiment of the invention provides an optimization method for a quantum key distribution handshake protocol, including the following steps S110-S170:

[0039] Step S110: Collect entangled state distribution data of the quantum channel and handshake delay records of the classical channel; extract the qubit error rate based on the entangled state distribution data; perform fluctuation analysis on the qubit error rate to obtain the quantum noise mode; and deduce the detection time set in reverse through the quantum noise mode.

[0040] Specifically, quantum state tomography was used to collect the distribution information of Bell state pairs in a quantum channel, and the coincidence count rate of entangled photon pairs under different basis vectors was measured. The entangled state distribution data includes four Bell states ( The density matrix elements are used, each a 4×4 complex matrix. Diagonal elements represent the probability distribution of each quantum state, while off-diagonal elements reflect quantum coherence. The acquisition frequency is set to millions of entangled pairs per second, achieving a picosecond time resolution to ensure the capture of rapidly changing quantum dynamics. A high-precision clock synchronization system is deployed on the classical channel side to record the latency information of each stage of the handshake protocol, including initialization request latency, authentication latency, parameter negotiation latency, and acknowledgment response latency, with each latency value accurate to the microsecond level. Clock synchronization uses the Network Time Protocol (NTP), and through multiple round-trip latency measurements and compensation algorithms, the clock deviation between Alice and Bob is kept within hundreds of nanoseconds. During the entangled state acquisition process, environmental factors such as temperature drift, vibration interference, and magnetic field fluctuations are monitored, and a correlation model between environmental parameters and entanglement fidelity is established. The raw data undergoes outlier removal, missing value imputation, and format standardization to generate structured entangled state distribution data and classical channel handshake latency records.

[0041] In some embodiments, extracting the qubit error rate based on the entangled state distribution data includes: performing quantum coherence reduction tracking on the entangled state distribution data to generate a coherence decay curve; identifying coherence loss nodes based on the coherence decay curve; and performing information entropy increase analysis using the coherence loss nodes to form the qubit error rate.

[0042] Quantum coherence decay curves are generated by tracking the quantum coherence decline of entangled state distribution data. The coherence metric uses the l1 norm. Here, ρ is the density matrix of the quantum state, and ρ_ij represents the element in the i-th row and j-th column of the density matrix. The condition i≠j ensures that only off-diagonal elements are calculated, reflecting the coherence of the quantum superposition state. The tracking process continuously records the trajectory of the coherence value C(t) at intervals of hundreds of microseconds. Experimental observations show that the coherence follows an exponential decay law. Where C_0 is the initial coherence value, t is the evolution time, and T_2 is the transverse relaxation time (also known as the decoherence time), characterizing the duration of quantum coherence maintenance. In standard quantum channels, the T_2 value is typically on the order of milliseconds, with the specific value depending on the physical implementation of the channel and the level of environmental noise. The T_2 parameter is obtained by fitting the decay curve using the least squares method, and the fitting residual is used to evaluate the applicability of the model. The change in the slope of the curve reflects the dynamic characteristics of the decoherence process; abrupt changes in the slope often correspond to the moment when the quantum state is subjected to external perturbation. The coherence decay curve is divided into three typical regions: the initial slow decay segment corresponds to the natural evolution of the quantum state, the middle rapid decay segment reflects the dominant role of environmental decoherence, and the final plateau segment indicates that the system has reached a fully mixed state.

[0043] Coherence loss nodes are identified based on coherence decay curves, defined as the time point when the coherence value C(t) drops below 10% of the initial value C_0. The identification algorithm employs a sliding window detection mechanism with a window width of 10 sampling points. The average coherence value is calculated within each window. When the average value of three consecutive windows is below the threshold 0.1×C_0, the center time of the window is marked as a coherence loss node. Time intervals between adjacent loss points that are less than the window width are merged into a single loss interval, and the start time, end time, and duration of the interval are recorded. Statistical analysis shows that coherence loss exhibits a quasi-periodic pattern, with loss events mainly concentrated in specific phase intervals of quantum state transmission, which is related to the physical characteristics and modulation method of the quantum channel. The time interval distribution of loss nodes follows Poisson statistics, with the average interval inversely proportional to the channel's signal-to-noise ratio. Abnormal loss nodes are identified through statistical testing. These nodes deviate from the normal distribution pattern in their occurrence time, typically caused by sudden external electromagnetic interference or mechanical vibration of optical components. Finally, a time series set of coherence loss nodes is formed, containing complete information on both normal and abnormal loss nodes.

[0044] Information entropy increase analysis is performed using identified coherence loss nodes to quantitatively assess the degree of quantum information loss at each node. The formula for calculating von Neumann entropy is as follows: Where S represents the information entropy of the quantum state, Tr represents the trace operation of the matrix (the sum of the diagonal elements), ρ is the normalized density matrix, and log is the natural logarithm. At each lost node t_i, the quantum state entropy values ​​S_before and S_after are calculated before and after the node, respectively. The entropy increase is defined as... The entropy represents the amount of information loss at that moment. The entropy of the fully mixed state reaches its maximum value log(d), where d is the dimension of the quantum system. For a qubit d=2, the maximum entropy is log(2). Statistical analysis of the entropy increase data of all lost nodes reveals that its distribution exhibits a log-normal characteristic, and the distribution parameters are related to the noise characteristics of the channel. The qubit error rate is calculated using the entropy increase transformation formula: This formula, derived from quantum information theory, maps the entropy increase ΔS to the bit flip probability QBER, with a value range of [0, 0.5]. The calculated QBER time series includes the instantaneous error rate, the sliding window average error rate, and the cumulative error rate distribution, comprehensively characterizing the error properties of the quantum channel.

[0045] Fluctuation analysis of the qubit error rate (QBER) is performed to obtain quantum noise patterns. Detrended fluctuation analysis (DFA) is employed. First, the QBER sequence is divided into non-overlapping time windows of length n. Within each window, a polynomial is used to fit the local trend, and the detrended residual sequence is calculated. The fluctuation function F(n) is defined as the average root mean square of the residuals across all windows, and the relationship between F(n) and the window size n is investigated. Power-law scaling relationship is also discussed. The exponent α in the equation is called the Hurst exponent, which characterizes the long-range correlation of the time series: α = 0.5 corresponds to an uncorrelated random walk, α > 0.5 indicates positive correlation (persistence), and α < 0.5 indicates negative correlation (anti-persistence). Power spectral density analysis reveals the frequency domain characteristics of QBER fluctuations; the relationship between spectral density S(f) and frequency f follows a power law. The exponent β corresponds to the Hurst exponent in the time domain. The amplitude probability distribution of the fluctuations deviates significantly from the Gaussian distribution, exhibiting heavy-tailed characteristics, meaning that the probability of large fluctuation events is higher than expected by a normal distribution. The time-frequency joint analysis uses wavelet transform to decompose the QBER sequence into different time-frequency scales, identifying low-frequency drift components (reflecting slowly varying system parameters), mid-frequency oscillation components (corresponding to periodic environmental disturbances), and high-frequency noise components (mainly caused by the randomness of quantum measurements).

[0046] An adaptive quantum state measurement strategy is designed by back-deriving the optimal detection timing using quantum noise patterns. Quantum state detection is performed within a low-noise time window by utilizing the predictable components of the noise. First, a Fourier transform is performed on the noise patterns to extract the main noise frequency components f_1, f_2, ..., f_k and their corresponding amplitudes A_1, A_2, ..., A_k and phases φ_1, φ_2, ..., φ_k. A noise prediction model is then constructed. Where N_0 is the noise floor and t is the time variable, the summation iterates through all significant frequency components. Local minima of N(t) are found through numerical optimization; these moments correspond to noise valleys, suitable for high-fidelity quantum state measurements. The width W of the detection window is determined based on the coherence time T_2 of the quantum state, typically taking the value W = 0.1 × T_2 to ensure measurement is completed before significant coherence decay. The generation of the detection time set needs to satisfy multiple constraints: the interval between adjacent detection moments must be greater than the detector's recovery time; the detection frequency must satisfy the Nyquist sampling theorem to avoid information loss; and the detection moments must be aligned with the synchronization point of classical communication. A genetic algorithm is used to optimize the distribution of detection moments, with the objective function being to maximize the average signal-to-noise ratio. , where S is the signal strength. The generated detection time set contains a series of optimized time points, each associated with the expected noise level and measurement fidelity estimate.

[0047] Step S120: Separate the transmission delay component and the processing delay component using the handshake delay record, map the transmission delay component and the processing delay component into spin vectors, perform phase modulation on the spin vectors to form a spin staggered timing sequence, and establish a parallel verification architecture based on the spin staggered timing sequence.

[0048] Specifically, delay components are decomposed using the handshake delay sequence to identify different sources of delay during protocol execution. The handshake delay record includes initialization request delay T_init, authentication delay T_auth, parameter negotiation delay T_nego, and acknowledgment response delay T_conf. The total delay for each stage can be decomposed into... Where T_trans is the transmission delay component (photon propagation time in the channel), T_proc is the processing delay component (quantum operation and classical computation time), and T_queue is the queuing time. The transmission delay component is extracted using the minimum round-trip time (RTT) estimation method. The minimum RTT value measured during low-load periods is approximately equal to 2 × T_trans. Considering that the propagation speed of light in optical fiber is approximately 2 × 10^8 m / s, the reasonableness of the measurement results can be verified based on the communication distance. The processing delay component is separated using statistical methods: in a large number of handshake samples, T_proc represents a relatively stable baseline value plus random fluctuations. A median filter is used to extract the baseline processing time, and the standard deviation reflects the uncertainty of the processing delay. The separation algorithm uses the expectation-maximization (EM) method to iteratively optimize the estimated values ​​of each component. The convergence condition is set to the parameter change between adjacent iterations being less than a set threshold. Finally, the transmission delay sequence {T_trans^(i)} and the processing delay sequence {T_proc^(i)} are obtained, where the superscript i indicates the i-th handshake process.

[0049] The separated propagation delay and processing delay components are mapped to quantum spin space to construct a quantized representation of the time delay. The mapping rule is based on the linear correspondence between the time delay value and the spin angle: Where T_max is the normalization constant, taken as the maximum observed time delay value, ensuring the mapping angle is within the interval [0, 2π]. The spin vector is represented on the Bloch sphere as... ,in and Let θ represent the spin-up and spin-down ground states, respectively, where θ is the polar angle (corresponding to propagation delay), φ is the azimuth angle (corresponding to processing delay), and i is the imaginary unit. This mapping preserves the integrity of the time delay information while endowing it with the mathematical structure of quantum mechanics. For each handshake process, a corresponding spin vector |ψ_i> is generated, forming a spin vector sequence. The magnitude of the vectors is kept normalized to ensure the physical rationality of the quantum state. The density matrix of the spin vectors is obtained through outer product operations, containing a complete quantum description of the time delay information. This is achieved through Pauli matrix decomposition. ,in Here, n is the Pauli operator and n is the Bloch vector, which can intuitively represent the distribution characteristics of time delay in three-dimensional space.

[0050] In some embodiments, the step of phase-modulating the spin vector to form a spin-shunted timing sequence includes: performing eigenstate decomposition on the spin vector to obtain an upper spin eigenstate, a lower spin eigenstate, and a superimposed spin eigenstate; establishing a forward phase modulation rule based on the upper spin eigenstate and an inverse phase modulation rule based on the lower spin eigenstate; and coordinating the forward phase modulation rule and the inverse phase modulation rule using the superimposed spin eigenstate to form a spin-shunted timing sequence.

[0051] For spin vector sequences Eigenstate decomposition is performed to extract different spin component features. Eigenstate decomposition is achieved by diagonalizing the spin operator; the eigenvalues ​​are positive and negative half, corresponding to the spin-up and spin-down states, respectively. For a general spin vector... The decomposition coefficients α and β satisfy the normalization condition and are determined by the position of the vector on the Bloch sphere. Upper spin eigenstates For fast response modes with low latency, the lower spin eigenstate The corresponding slow response mode with large time delay is superimposed with spin eigenstates. This represents the intermediate response characteristics. The decomposition process calculates the projection probability of each spin vector onto each eigenstate; these probability values ​​reflect the weight distribution of different response modes during the handshake process. Statistical analysis reveals that under normal communication conditions, the upper spin component dominates, while the lower spin component increases during network congestion, and the fluctuations in the superposition component reflect the system's instability.

[0052] A forward phase modulation rule is established based on the upper spin eigenstate to achieve timing optimization of the fast response mode. Forward phase modulation is achieved through a phase factor. Acting on the upper spin component, modulating the phase Where ω_+ is the modulation angular frequency, t is the time variable, and φ_0 is the initial phase. The modulation angular frequency is determined based on the dominant frequency of the upper spin component, through... The spectral analysis of the time series is used to obtain the data. The physical meaning of forward modulation is to apply a periodic phase advance to fast-response events, distributing them uniformly along the time axis and avoiding sudden resource contention. Similarly, a reverse phase modulation rule is established based on the lower spin eigenstate to modulate the phase. The negative sign indicates a phase delay rather than a forward movement. Inverse modulation targets slow-response events, creating a time buffer through phase delay to reserve processing resources for fast-responding events. The modulation intensity is dynamically adjusted via feedback control; when an excessive concentration of a certain type of event is detected, the corresponding modulation depth is increased.

[0053] By utilizing superimposed spin eigenstates to coordinate forward and reverse phase modulation rules, a smooth fusion of two modulation modes is achieved. The phase modulation of the superposition state employs a nonlinear function. Where A is the modulation amplitude, this function generates the modulation envelope at the beat frequencies of ω_+ and ω_−. The core of the coordination mechanism is to prevent destructive interference from forward and reverse modulation, by selecting an appropriate frequency ratio. (where m and n are coprime integers) to ensure the quasi-periodicity of the modulation mode. Complete phase modulation is applied to the original spin vector: The modulation operator Effective Hamiltonian P is the projection operator corresponding to the eigenstate. The modulated spin vector sequence The timing of the spin-staggered vectors is staggered in the time domain, and the phase difference between adjacent vectors is kept within a set range to avoid resource conflicts caused by synchronization. The spin-staggered timing sequence is obtained by rearranging the timestamps of the modulated vectors to form an optimized event scheduling sequence.

[0054] A parallel verification architecture is established based on spin-staggered timing to achieve efficient multi-channel quantum handshake verification. The parallel architecture design is based on the natural grouping characteristics of staggered timing: events with similar phases are assigned to the same verification channel to ensure processing continuity within that channel; events with large phase differences are assigned to different channels to achieve true parallel processing. The number of channels, N_ch, is determined based on system resources and the periodicity of the staggered timing, typically ranging from 4 to 8 channels. Each verification channel is configured with independent quantum state preparation, transmission, and measurement modules, and channels exchange control information via a classical communication network. The algorithm for allocating verification tasks uses hash mapping. Where φ is the modulation phase of the event, This indicates rounding down. This mapping ensures load balancing, with each channel having a similar average number of tasks. A pipelined processing model is used within each channel, where the measurement phase of the current task overlaps with the preparation phase of the next task, improving resource utilization. Synchronization between channels is achieved through a global clock and a staggered timing table; each channel is aware of the busy / idle status of other channels, supporting dynamic task migration. The parallel verification architecture also includes a fault-tolerance mechanism: when a channel fails, its tasks are automatically reassigned to adjacent channels, ensuring continuous system operation. The architecture's output is an ordered set of multi-channel verification results, maintaining the timing relationship of the original handshake requests.

[0055] Step S130: Set multiple parallel execution windows according to the parallel verification architecture and the probe timing set, send pre-verification probe packets through the parallel execution windows and collect responses to obtain a verification response sequence, generate verification feature codes based on the verification response sequence, and construct a pre-matching codebook using the verification feature codes.

[0056] Specifically, a spatiotemporally coordinated parallel execution window is designed by combining the line verification architecture and the probe timing set. The parallel verification architecture provides N_ch independent verification channels, each with a specific phase range, numbered sequentially from 1 to N_ch. The probe timing set contains a series of optimized time points corresponding to local minima of quantum channel noise, denoted as m. The execution window is set using a Cartesian product approach, combining each channel with each probe timing to generate a two-dimensional channel-time window matrix, with the total number of windows being N_ch multiplied by m. The window size is determined based on the coherence time T_2 in S110, and the time width of each window is set to one-tenth of the coherence time, i.e., Δt = 0.1 × T_2. This choice ensures that the measurement is completed before significant decoherence of the quantum state, and the spatial dimension covers the complete phase range of a single channel. The overlap between windows is controlled within 10%, ensuring both coverage integrity and avoiding resource waste. Window scheduling uses a priority sorting formula: Where P_window is the window priority value, N(τ_j) is the predicted noise level corresponding to the detection timing τ_j (the value comes from the noise prediction model of S110), Load(k) is the current load rate of channel k (value from 0 to 1), and w_1 and w_2 are weighting coefficients. High-priority windows are executed first, achieving the dual optimization goals of noise minimization and load balancing.

[0057] Pre-verification probe packets are sent via a parallel execution window, and the response signal of the quantum channel is acquired. The pre-verification probe packets are prepared using weakly coherent states, with the average photon number set within the range of 0.1 to 0.5. This range ensures the validity of the single-photon approximation and avoids interference from multiphoton events with the measurement results. The quantum state of the probe packet is customized according to the channel characteristics of the execution window and is represented using standard qubit states. ,in and Let θ_k represent the horizontal and vertical polarization states (or other orthogonal quantum states) of a photon, respectively. θ_k is the polar angle parameter (ranging from 0 to π), determining the amplitude ratio of the two ground states. φ_k is the azimuth parameter (ranging from 0 to 2π), determining the relative phase between the two ground states. e is the base of the natural logarithm (approximately 2.718), and i is the imaginary unit (satisfying i² = -1). These parameters are uniquely determined by the staggered phase of channel k. The transmission timing strictly follows the arrangement of the detection time set. At each specified detection time, all channels in the ready state simultaneously transmit their configured detection packets. The quantum channel response is acquired through a single-photon detector array, with the array configuration corresponding one-to-one with the verification channel. The detector output contains three key pieces of information: the precise time of the detection event, the detected photon count, and the phase value estimated through interferometry. The raw response data requires three preprocessing steps: subtracting the detector's dark count background, performing count correction based on detector efficiency, and precisely aligning the time stamps of different detectors. The resulting verification response sequence contains four-tuple data: the timestamp of the detection time, the corrected photon count, the estimated phase information, and the corresponding channel identifier. These data fully record the multidimensional characteristics of each detection event.

[0058] In some embodiments, generating a verification signature based on the verification response sequence includes: performing quantum measurement inverse reconstruction on the verification response sequence to obtain original quantum state information; extracting a quantum coherence fingerprint based on the original quantum state information; performing quantum state overlap analysis using the quantum coherence fingerprint to obtain overlap features; and quantum encoding the overlap features to form a verification signature.

[0059] The verification response sequence is reconstructed using quantum measurement to recover the original quantum state information before detection. The complete quantum state description is inferred from the finite measurement results using the maximum likelihood estimation method. For each set of quaternary data in the response sequence, a likelihood function describing the measurement process is constructed. An iterative optimization algorithm is used to find the density matrix that maximizes the likelihood function; this density matrix is ​​the reconstructed quantum state ρ_recon. The reconstruction process must satisfy the fundamental constraints of quantum mechanics: the density matrix must be a Hermitian matrix (satisfied with...). Conditions, among which The symbol represents the conjugate transpose operation of the matrix. The trace of the density matrix must be equal to 1 (i.e., Tr(ρ) = 1, where Tr represents the summation over the diagonal elements of the matrix), and all eigenvalues ​​of the density matrix must be non-negative (to ensure a physically plausible probabilistic interpretation). Reconstruction accuracy is evaluated using the quantum state fidelity index; a fidelity value close to 1 indicates high reconstruction quality, while a value close to 0 indicates reconstruction failure. The response data collected in each execution window is independently reconstructed, ultimately obtaining a set of original quantum states equal to the number of windows. The purity of each quantum state is calculated simultaneously during the reconstruction process. , where γ is the purity value (ranging from 0 to 1), ρ is the reconstructed density matrix, Tr represents the trace operation of the matrix, and ρ² represents the matrix product of the density matrix itself. A purity close to 1 indicates that the quantum state maintains good coherence, and a purity close to 1 / d (d is the dimension of Hilbert space) indicates a completely mixed state.

[0060] Based on the reconstructed original quantum state information, quantum coherence fingerprints are extracted to construct unique identifiers for each quantum state. First, the reconstructed density matrix is ​​expanded under a computational basis consisting of two orthogonal states |0> and |1>, extracting four complex matrix elements, including diagonal elements (representing classical probabilities) and off-diagonal elements (representing quantum coherence). Then, the expansion is performed under the Hadamard basis, which consists of equal-weighted superposition states. Superposition state with opposite phase The structure is defined, where √2 is the normalization factor. It is then expanded under the circular polarization basis, including right-handed circularly polarized states. and left-handed circularly polarized state Where i is the imaginary unit, introducing a phase difference of π / 2. The expansion coefficients under the three measurement bases together form a 12-dimensional complex eigenvector, containing complete information about the quantum state. The eigenvector is dimensionality-reduced using principal component analysis (PCA), retaining principal components with a cumulative variance contribution rate of over 95%, typically reducing it to 6-8 dimensions. A coherence fingerprint is defined as structured data containing three elements: the dimensionality-reduced principal component vector, the quantum state purity value, and the von Neumann entropy value (quantum information entropy). The stability of a fingerprint is assessed by the variance of the results of multiple measurements on the same quantum state; low variance indicates good repeatability and reliability.

[0061] Quantum state overlap analysis is performed using quantum coherence fingerprinting to quantitatively assess the similarity of quantum states across different execution windows. Overlap reflects the distinguishability of two quantum states. For pure states, overlap is directly calculated using the square of the modulus of the inner product; for general mixed states, the trace distance formula is used. Where D represents the trace distance (ranging from 0 to 1, where 0 indicates identical traces and 1 indicates perfect orthogonality), ρ_i and ρ_j are the two density matrices to be compared, and |A| represents the modulo operation of matrix A (defined as matrix A). The square root of Tr is the trace operation of the matrix. The fast overlap estimation method based on coherent fingerprints uses cosine similarity calculation in vector space, simplifying high-dimensional density matrix operations to low-dimensional vector operations, thus reducing computational complexity from... Reduce the value to O(d). Construct an overlap matrix O between all window pairs. This is a symmetric matrix with diagonal elements of 1 (self-overlap) and off-diagonal elements O_ij representing the quantum state overlap between window i and window j. Perform spectral decomposition on the overlap matrix. Where U is a unitary matrix containing eigenvectors, The diagonal matrix contains eigenvalues ​​arranged in descending order. This represents the conjugate transpose. The principal eigenvalue (maximum eigenvalue) reflects the overall overlap level, while the information entropy of the eigenvalue distribution characterizes the complexity of the overlap pattern. The extracted overlap feature vector contains the first few principal eigenvalues, the eigenvalue distribution entropy, and the key components of the eigenvector. These features not only contain numerical information but also implicitly encode the topological relationship structure between different windows.

[0062] The extracted overlapping features are quantum-encoded to generate compact and secure verification signatures. The classical overlapping feature information is embedded into the phase structure of the quantum state using the phase degrees of freedom of the quantum state. The encoding process first creates a uniform superposition state as a carrier, and then modulates the phase of different computational ground states according to the components of the overlapping features. Specifically, the encoding is implemented by constructing a specific quantum state whose probability amplitudes are equal when expanded under the computational basis, but whose phase carries feature information. To enhance the discriminative power and security of the signature, a nonlinear transformation function is introduced, and a hyperbolic tangent function is used to adjust the dynamic range of the phase, allowing small changes in the features to be amplified during encoding. The encoding implementation requires quantum circuits, including three types of quantum gates: Hadamard gates for creating the initial superposition state, phase rotation gates for applying the feature-dependent phase, and controlled operation gates for establishing multi-qubit entanglement to enhance security. After encoding, the probability distribution is obtained by measurement under the computational basis, and the probability is measured... Where p_k represents the probability of obtaining the k-th ground state, This represents the k-th calculated ground state. For the encoded quantum state, Represents the ground state The conjugate (left vector) of the probability distribution is discretized and concatenated to form a binary verification signature. The signature length is flexibly set according to security requirements, typically 128 bits or 256 bits, providing sufficient security margin. The generated verification signature has cryptographically anti-collision properties, meaning that different overlapping features are extremely difficult to generate the same or similar signatures.

[0063] A pre-matching codebook is constructed using the generated verification feature codes, establishing a reference database system to support rapid authentication. The codebook construction process performs k-means clustering analysis on all verification feature codes, grouping similar feature codes in the feature space into the same category. The number of clusters, k, is determined using the elbow rule, taking a value at the inflection point of the intra-cluster variance curve. The center point of each cluster serves as a codeword in the codebook, forming a codebook set containing k codewords. The coverage radius of the codebook is defined as the maximum distance from any point in the feature space to the nearest codeword. Codebook optimization follows rate-distortion theory, minimizing the codebook size under a given distortion constraint. Each codeword is assigned a weight value, reflecting its frequency of use in historical matching; higher-weight codewords receive priority during search. The codebook index is organized using a KD-tree structure, supporting efficient nearest neighbor search. The pre-matching decision threshold is set to... Where θ_match is the distance threshold, μ is the mean distance between codewords, σ is the standard deviation, and the coefficient 2 corresponds to the 95% confidence interval. Pre-matching is considered successful when the distance between the input feature code and the nearest codeword is less than the threshold. The constructed pre-matching codebook contains four core components: a codeword set, an index structure, a weight vector, and a matching threshold.

[0064] Step S140: Construct an authentication sub-process and a negotiation sub-process using a parallel verification architecture. Generate a quantum signature token through the authentication sub-process, and obtain key negotiation parameters through the negotiation sub-process. Then, fuse the quantum signature token and key negotiation parameters in real time to form a handshake state matrix.

[0065] Specifically, leveraging a parallel verification architecture, two core sub-processes are built upon a multi-channel framework. The N_ch channels of the parallel verification architecture are dynamically allocated according to functional requirements: the authentication sub-process occupies the first N_auth channels (typically N_auth = N_ch / 2), while the negotiation sub-process uses the remaining N_nego channels. The authentication sub-process is responsible for identity verification and authorization confirmation, employing a quantum digital signature protocol and utilizing staggered channel timing to avoid authentication request congestion. The negotiation sub-process handles the key negotiation process, implementing parameter exchange and synchronization, fully utilizing the pipeline characteristics of the parallel architecture to improve negotiation efficiency. The two sub-processes exchange intermediate results through a shared memory region, with read-write locks used for memory access to prevent data contention. The startup timing of the sub-processes is determined based on the spin staggered timing of the S120: the authentication sub-process... Executed within the time window, the negotiation sub-process is in phase The process executes within a specific window, achieving staggered operation during off-peak hours. Each sub-process employs a state machine model, comprising four states: initialization, execution, waiting, and completion, with state transitions driven by events. Synchronization between sub-processes is achieved through a semaphore mechanism; an authentication completion signal triggers negotiation initiation, and a negotiation ready signal allows parameter passing. The performance monitoring module records the execution time, resource usage, and success rate of both sub-processes in real time, enabling dynamic adjustment of channel allocation ratios.

[0066] A quantum signature token is generated through an authentication sub-process, enabling authentication and non-repudiation between the communicating parties. Quantum signatures are based on the difficulty of single-vector quantum functions; the signer, Alice, prepares a set of quantum states. Where α_i is the complex amplitude coefficient, satisfying the normalization condition The signing key is pre-shared via a quantum key distribution (QKD) protocol and includes a private key SK and a public key PK pair. The signature generation process is as follows: First, the hash value h = H(m) is calculated for the message m to be signed, where H is a collision-resistant hash function; then, the hash value is quantum encrypted using the private key E_SK(h) to generate the quantum signature σ; finally, the message, signature, and timestamp are packaged into a complete signature data packet. The quantum signature token structure includes four fields: token identifier (64-bit random number), signature body (containing signature σ and message m), validity period (start time and expiration time), and verification parameters (public key fingerprint and algorithm identifier). Token generation uses a batch processing mode, generating 100 tokens per batch, accelerating signature operations through parallel computing. The quantum state encoding of the token uses four-state encoding based on the BB84 protocol, prepared under two sets of conjugate bases (computational base and Hadamard base) to enhance security. The generated tokens are transmitted through a quantum channel, and the qubit error rate is monitored during transmission. When the QBER exceeds a threshold of 11%, the token is regenerated. The tokens are cached in a quantum memory, with storage time limited by decoherence, typically holding for 100 milliseconds. The authentication subprocess outputs a sequence of quantum tokens containing complete signature information.

[0067] Key negotiation parameters are obtained via a negotiation sub-process to establish a secure shared key foundation. The negotiation sub-process employs a quantum key negotiation protocol (QKA), combining classical Diffie-Hellman principles with quantum mechanics. The negotiation parameters contain three types of information: basis selection parameters (defining the selection strategy for the measurement basis), error correction parameters (the generation matrix of the forward error correction code), and privacy amplification parameters (the selection of a family of hash functions). The parameter generation process is accelerated using the pre-matched codebook of the S130: first, the best-matching codeword is selected from the codebook as the initial parameters, and then the parameters are optimized through a small number of communication rounds. Basis selection employs a randomization strategy; each qubit selects a computational basis with probability p and a Hadamard basis with probability 1-p, with the p value adaptively adjusted based on channel characteristics. Error correction parameters are customized based on the channel's error mode, using LDPC (low-density parity-check) codes for efficient error correction. The code rate is dynamically adjusted according to the QBER: a high code rate of 0.9 is used when the QBER is below 5%, and it decreases to 0.7 when it is above 5%. Privacy amplification employs a two-stage hashing approach: the first stage uses a Toeplitz matrix to compress the original key, and the second stage uses SHA-3 for final processing. Parameter exchange is conducted through a certified classic channel, with each parameter accompanied by a MAC tag to prevent tampering. The negotiation process is limited to no more than five rounds, with each round exchanging no more than 1KB of data to minimize information exposed to eavesdroppers. After negotiation, a negotiation parameter packet containing all necessary parameters is generated.

[0068] In some embodiments, the step of fusing the quantum signature token and the key negotiation parameters in real time to form a handshake state matrix includes: performing quantum state analysis on the quantum signature token to obtain a token state vector; performing parameter vectorization processing on the key negotiation parameters to obtain a negotiation state vector; performing tensor product operation on the token state vector and the negotiation state vector to obtain a fused state matrix; and performing state normalization processing on the fused state matrix to form a handshake state matrix.

[0069] Quantum state analysis is performed on quantum signature tokens to extract quantum information and convert it into a vector representation. The analysis process begins with projection measurements on the quantum state portion of the token. The choice of measurement basis is determined by the token's encoding method: BB84 encoding uses computational and Hadamard bases, while the six-state protocol adds circular basis measurements. The measurement result sequence is post-processed into a classical bit string, with the bit string length equal to the dimension of the quantum state. The token's signature portion is reconstructed using quantum state tomography to obtain the density matrix of the signature state. The timestamp and verification parameters are converted to binary representations and concatenated with the quantum measurement results. The token state vector construction includes the quantum state measurement results, signature state matrix elements, normalized timestamp, and verification parameter hash values. The vector dimension is fixed at 256 dimensions; insufficient dimensions are padded with zeros, and excess dimensions are compressed using principal component analysis. Complex components are processed by storing the real and imaginary parts separately to maintain information integrity. The vector is normalized using the L2 norm to ensure numerical stability in subsequent operations. After analysis, a standardized token state vector is obtained.

[0070] The key negotiation parameters are vectorized, transforming the discrete set of parameters into a continuous vector space representation. The vectorization mapping rule is designed based on the parameter type: the basis selection parameter p is mapped as follows: Here, H is the binary entropy function, reflecting the degree of randomness; the error correction parameters consist of the eigenvalue spectrum {λ_1,λ_2,...,λ_k} of the generator matrix G, arranged in descending order and normalized; the privacy amplification parameters are quantized using the security parameters of the hash function (output length, collision resistance exponent). The temporal characteristics of the parameters are captured through differential coding, and the parameter changes between adjacent rounds are... Added as an extra dimension to the vector. Meta-information of the negotiation process (number of rounds, success rate, average interaction time) is encoded as normalized scalar values. Construct the negotiation state vector. Where p_base is the basis selection vector, λ_ecc is the error correction feature vector, h_pa is the privacy amplification parameter vector, Δp_seq is the temporal difference vector, and meta is the meta-information vector. The total dimension of the vector is consistent with the token state vector (256 dimensions), and the same normalization method is used. The vectorization process preserves the relative relationships and statistical properties between parameters. After processing, a structured negotiation state vector is obtained.

[0071] For example, the step of performing a tensor product operation on the token state vector and the negotiation state vector to obtain a fusion state matrix includes: performing a coordination analysis on the token state vector and the negotiation state vector to obtain strong coordination components and weak coordination components; performing a balance characteristic analysis based on the strong coordination components and the weak coordination components to obtain a balance adjustment factor; constructing a coordination balance network using the balance adjustment factor; and performing a vector fusion operation based on the coordination balance network to form a fusion state matrix.

[0072] A coordination analysis is performed on the token state vector and the negotiation state vector to identify the association pattern between the two vectors. Coordination is determined by calculating the cross-correlation function. The evaluation is performed, where C(τ) represents the cross-correlation function value, v_token(t) and v_param(t) are the component values ​​of the two vectors at time t, τ is the time offset, and Σ represents the summation over all times t. Strongly coordinated components are defined as the frequency components corresponding to the main peak of the cross-correlation function, and the spectral energy of the main peak position and its neighborhood is extracted using Fourier transform. Weakly coordinated components contain the remaining spectral components, especially the low-frequency trend term and the high-frequency noise term. Component separation uses a bandpass filter bank, and the filtered components are returned to the time domain via inverse Fourier transform. Coordination strength index. Where S_coord represents the coordination strength (ranging from 0 to 1), E_strong represents the energy of the strong coordination component, and E_weak represents the energy of the weak coordination component. Spatial coordination is further evaluated by calculating the angle between the two vectors. A complete description of the coordination characteristics is obtained by combining the analysis results from the frequency and spatial domains.

[0073] Balance characteristics are analyzed based on the identified strong and weak coordination components to determine the optimal fusion strategy parameters. Balance characteristics are determined by constructing an energy distribution function. The analysis involves E(α) representing the total energy, α being the balance parameter (ranging from 0 to 1), and E_strong and E_weak representing the energies of the strong and weak components, respectively. The optimal balance point is determined by maximizing information entropy, thus achieving the best balance between the contributions of the strong and weak components. Balance adjustment factor. Here, β is the adjustment factor (ranging from -1 to 1), α_opt is the optimal balance point, and 0.5 is the equal-weight reference point. A positive β value indicates that the strong coordination component should be strengthened, while a negative value indicates that the weak coordination component needs to be compensated. The dynamic adjustment strategy adaptively adjusts according to the coordination strength: when the coordination strength is high, β is appropriately reduced to prevent over-dependence; when the coordination strength is low, β is adjusted to enhance the contribution of the weak component. The stability factor is introduced by calculating the variance of the cross-correlation function and is used to evaluate the time stability of the coordination. The final balance adjustment factor comprehensively considers the balance requirements and stability requirements to guide subsequent fusion operations.

[0074] A coordinated balance network is constructed using a balance adjustment factor to achieve intelligent routing and weighting of vector components. The network adopts a three-layer feedforward structure: the input layer receives strong and weak coordinated components, the hidden layer contains 128 neurons using the hyperbolic tangent activation function, and the output layer generates a 256-dimensional fusion weight vector. The network weights are initialized considering the balance adjustment factor, assigning initial weights based on the relative importance of strong and weak components. The connection weights of the hidden layers are optimized based on the results of coordination analysis, with neurons corresponding to strong coordination frequencies receiving larger weights. The network calculates the fusion weight distribution through a single forward propagation, avoiding the time overhead of iterative optimization. The output vector is normalized using softmax to generate a fusion weight distribution that sums to 1. The entropy of the weight distribution is used as a quality indicator; high entropy indicates balanced fusion, and low entropy indicates selective fusion. The completed network provides an adaptive fusion weight allocation scheme.

[0075] Vector fusion operations are performed based on a coordinated balancing network to generate a fused state matrix containing information from both sides. The fusion operation uses a weighted tensor product form: Where M_fusion is the fusion state matrix, w_i is the i-th fusion weight, v_token^(i) and v_param^(i) are the i-th components of the two vectors respectively, ⊗ represents the tensor product operation, and Σ represents the summation over all components. The tensor product expands into a 256×256 matrix, and each matrix element... , where M_ij is the element in the i-th row and j-th column of the matrix, containing the cross-information of the two vector components. The diagonal elements of the matrix reflect the correlation between components of the same dimension, while the off-diagonal elements encode cross-dimensional interactions. The fusion process introduces phase modulation to enhance the contribution of specific frequency components. Matrix symmetry ensures the Hermitianness of the fused state. The sparsity step sets the matrix elements smaller than 1% of the largest element to zero, reducing noise effects. The final fused state matrix contains complete information about the token and negotiation parameters while preserving the mathematical structure of the quantum state.

[0076] The fused state matrix undergoes state normalization to ensure it meets the physical requirements of a quantum state matrix. The normalization process comprises four steps: Hermitization to ensure the matrix equals its conjugate transpose, positive definiteness to ensure all eigenvalues ​​are non-negative, trace normalization to make the trace of the matrix equal to 1, and purity adjustment to control the mixing degree of the quantum states. Hermitization is achieved by averaging the matrix and its conjugate transpose. Positive definiteness uses spectral decomposition to replace negative eigenvalues ​​with minimal positive values. Trace normalization is achieved by dividing by the trace of the matrix. Purity adjustment is performed based on the target purity: the current purity (defined as the trace of the square of the density matrix) is calculated; if it is lower than the target, the principal eigenvalues ​​are enhanced through power iteration; if it is higher than the target, white noise is added to reduce it. The target purity is set according to application requirements, typically between 0.8 and 0.9, balancing information capacity and noise resistance. The normalized matrix serves as the handshake state matrix M_handshake, with dimensions 256×256, fully preserving the fusion information of the quantum signature token and key negotiation parameters. To facilitate state determination in subsequent steps, two types of process information are embedded without affecting the quantum state characteristics: the authentication progress vector v_auth records the completion status of 128 authentication sub-steps, including identity verification, permission confirmation, and token generation. It is encoded in the first row of the matrix because the first row is the easiest to extract in quantum measurement and has the least impact on the overall quantum state; the negotiation completion degree C_nego reflects the overall progress of key negotiation. Modulating it onto the diagonal weights utilizes the characteristic that diagonal elements represent classical probabilities, thus not destroying quantum coherence. This design makes the handshake state matrix both a carrier of quantum information and a complete record of the handshake process.

[0077] Step S150: Use the handshake state matrix and pre-matched codebook to perform handshake prediction analysis to obtain the success probability value. Based on the success probability value and the handshake state matrix, trigger the key pre-generation mechanism to prepare a preliminary key and construct a candidate key pool using the preliminary key.

[0078] In some embodiments, the step of using the handshake state matrix and the pre-matched codebook to perform handshake prediction analysis to obtain a success probability value includes: extracting the quantum entanglement correlation strength from the handshake state matrix; performing long-range quantum state correlation analysis based on the quantum entanglement correlation strength to obtain a correlation probability distribution; and using the pre-matched codebook to perform quantum measurement prediction on the correlation probability distribution to obtain a success probability value.

[0079] The quantum entanglement correlation strength is extracted from the handshake state matrix to quantify the degree of quantum correlation between the two parties. The entanglement correlation strength is obtained by calculating the mutual information after partial trace operations. The 256×256 handshake matrix is ​​divided into four 64×64 sub-blocks according to their source, corresponding to Alice self-correlation, Alice-Bob mutual correlation, Bob-Alice mutual correlation, and Bob self-correlation, respectively. The reduced density matrix is ​​obtained through partial trace operations: the reduced state of Alice is obtained by taking the trace over the Bob system, and the reduced state of Bob is obtained by taking the trace over the Alice system. The formula for calculating quantum mutual information is as follows: Where I(A:B) represents the mutual information between systems A and B, S(ρ_A) and S(ρ_B) are the von Neumann entropies of the reduced density matrix, and S(M_handshake) is the entropy of the overall system. Entanglement strength is defined as... Where E is the normalized entanglement strength (from 0 to 1), and the denominator is normalized using the smaller of the two reduced entropies. A strength value greater than 0.8 indicates strong entanglement, 0.5 to 0.8 indicates moderate entanglement, and less than or equal to 0.5 indicates weak entanglement or separable states. The robustness of entanglement is assessed by the fidelity reduction rate after adding noise. Spatial distribution characteristics are obtained by calculating the entanglement strength between different sub-blocks.

[0080] Long-distance quantum state correlation analysis is performed based on the strength of quantum entanglement to assess the synchronization degree of quantum states between communicating parties. The correlation analysis combines quantum state tomography and correlation function calculation. The analysis strategy is selected according to the entanglement strength: full tomography is used for strong entanglement, partial tomography for moderate entanglement, and classical correlation analysis is performed only for weak entanglement. The quantum correlation function is defined using the reduced density matrices ρ_A and ρ_B obtained from the preceding steps. Here, G(τ) represents the correlation strength at time delay τ, ρ_A(t) and ρ_B(t+τ) are the density matrices of Alice at time t and Bob at time t+τ, respectively, and Tr is the trace operation. Correlation function curves are obtained by scanning different time delays τ. The peak of the curve corresponds to the maximum correlation, and the peak position indicates the transmission delay. The correlation probability distribution is obtained by normalizing the correlation function so that its integral equals 1. The characteristic parameters of the distribution include average delay, delay jitter (variance), and distribution sharpness (kurtosis). Multimode correlations are identified using orthogonalization methods, with each orthogonal mode corresponding to an independent correlation channel. Spectral analysis of the correlation strength reveals periodic components. The quantum advantage of long-range correlations is quantified by the degree to which Bell's inequality is violated.

[0081] Quantum measurement prediction of the association probability distribution is performed using a pre-matched codebook to estimate the probability of a successful handshake. The prediction process maps the association probability distribution to the codebook space and evaluates the degree of matching with successful patterns. First, the continuous probability distribution is discretized into several intervals, and the probability quality of each interval is calculated. The similarity between the discrete distribution vector and each codeword in the codebook is calculated. Quantum fidelity is used as the similarity metric. Where F is the fidelity value (from 0 to 1), p_i is the i-th component of the discrete distribution, b_i is the i-th component of the codeword, and Σ represents the summation over all components. The square root and square operations ensure the correct measurement of quantum state similarity. The maximum fidelity value F_max among all codewords is selected as the benchmark. The probability of successful prediction is determined through a nonlinear mapping. The prediction process obtains a curve with 'n' as the steepness parameter (typically 2-4), controlling the steepness of the mapping curve and transforming the fidelity value into a probability space that better reflects the actual success rate distribution. The prediction process also considers the statistical characteristics of the association distribution: the kurtosis of the distribution reflects the concentration of associations, with a higher peak corresponding to a more certain handshake state; the multimodality of the distribution indicates potential multipath effects, requiring corresponding adjustments to the prediction strategy. A weighted average method is used for multi-codeword matching, with weights determined by the codeword's historical hit rate and its matching degree with the current distribution. The prediction results are calibrated using a machine learning model based on historical data to correct for systematic biases.

[0082] The key pre-generation mechanism is triggered based on the success probability value and the handshake state matrix. First, key parameters are extracted from the handshake state matrix: the negotiation completion degree C_nego is recovered by analyzing the diagonal weights; the authentication progress vector v_auth is extracted through phase demodulation in the first row; and the authentication completion degree is calculated. Then, the quantum properties of the matrix are evaluated: the matrix purity is calculated. Evaluate the degree of coherence preservation, analyze the eigenvalue distribution to judge the state stability, and check the entanglement degree between sub-blocks to confirm the coupling strength of each process. The trigger condition is designed as a multi-layer decision tree: the first layer classifies according to P_success (high > 0.7, medium 0.5 - 0.7, low < 0.5); the second layer checks the handshake progress (both C_nego and A_comp need to reach the corresponding thresholds); the third layer verifies the quantum state quality (purity, stability, etc. need to meet the requirements). Specific trigger rules: when P_success > 0.7, C_nego > 0.6, A_comp > 0.7 and γ > 0.8, start the full pre-generation mode; when P_success ∈ [0.5, 0.7], C_nego > 0.8, A_comp > 0.6 and γ > 0.6, start the partial pre-generation mode; in other cases, delay the pre-generation. In particular, if the trace of the authentication-related sub-block (the first 64×64) or the negotiation-related sub-block (the last 64×64) is extremely low, indicating serious problems in the corresponding process, the pre-generation will be vetoed. This comprehensive trigger mechanism makes full use of the success probability prediction and real-time state information, providing a robust and reliable decision. Generate a trigger signal T according to the above trigger rules: T = 1 when the full pre-generation condition is met, T = 0.5 when the partial pre-generation condition is met, and T = 0 otherwise.

[0083] Prepare and form a preliminary key by means of the key pre-generation mechanism to achieve the advance preparation of the key and resource optimization. The pre-generation mechanism executes different generation strategies according to the value of the trigger signal T. Full pre-generation mode (T = 1): All available entangled photon pairs are allocated, aiming to generate a complete 2048-bit key; the quantum state preparation uses a parametric down-conversion source, and the pump power is set to the maximum value to ensure a high generation rate; all quantum channels are used in parallel to improve efficiency through spatial multiplexing. Partial pre-generation mode (T = 0.5): Only 50% of the quantum resources are used to generate a 1024-bit initial key; the time-division multiplexing method is adopted to share the quantum channel with other tasks; the pump power is reduced to 70% of the nominal value to balance the generation rate and resource consumption. The key generation protocol is selected according to the entanglement correlation strength E: when E > 0.8, the E91 protocol is used to make full use of the entanglement characteristics; when 0.5 < E ≤ 0.8, the BBM92 protocol is used to adapt to medium entanglement; when E ≤ 0.5, fallback to the BB84 protocol to ensure basic security. The raw key is obtained through quantum state measurement, and the selection of the measurement basis refers to the basis selection strategy in the S140 negotiation parameters. Post-processing includes basis pairing, error rate estimation and error correction, and the error correction code uses the LDPC parameters negotiated in S140. Privacy amplification uses a Toeplitz matrix compression, and the compression ratio is calculated according to the eavesdropping upper limit. The preliminary key is stored in 64-bit groups, and each group is accompanied by a generation timestamp and a validity period mark.

[0084] A candidate key pool is constructed using pre-existing keys, providing diverse key selection and dynamic update capabilities. The candidate key pool is designed as a priority queue structure, supporting fast insertion and priority retrieval. The pool capacity is dynamically adjusted based on system memory, typically ranging from 100 to 500 keys. Each pre-existing key is calculated for its quality score upon entering the pool, considering four factors: generation rate (reflecting key freshness), a reverse indicator of qubit error rate (lower error rate, higher score), the proportion of key length to the maximum length, and time freshness (exponential decay over time). The quality score determines the key's priority in the pool, with higher-scoring keys being selected first. Pool maintenance strategies include capacity control (eliminating low-scoring keys when the limit is reached), aging management (automatically removing expired keys), and diversity maintenance (limiting the number of keys with identical parameters). The key index is organized using a hash table, supporting constant-time lookups. The index key consists of key characteristics for easy on-demand retrieval. Pool status monitoring metrics include average quality score, key age distribution, protocol type distribution, and capacity utilization. The final candidate key pool provides flexible key supply capabilities.

[0085] Step S160: Verify the legitimacy of the quantum signature token to generate a verification result, activate the candidate key pool based on the verification result to obtain a usable key, and fuse the usable key with the key negotiation parameters in a segmented quantum state according to the handshake state matrix to generate an enhanced key.

[0086] Specifically, multi-dimensional legitimacy verification is performed based on the quantum signature token. The verification process includes timeliness checks, signature integrity verification, quantum state fidelity assessment, and replay attack detection. The timeliness check extracts the timestamp from the token and compares it with the current system time; tokens exceeding their validity period (typically 300 seconds) are deemed invalid. Signature integrity verification uses the public key fingerprint in the token to locate the corresponding public key, performs quantum decryption on the signature to recover the original hash value, and compares it with the recalculated message hash to confirm consistency. Quantum state fidelity is assessed by measuring the overlap between the received signature state and the original state. , where F is the fidelity value (0 to 1). For the original signature quantum state, This represents the received signature quantum state. A fidelity below 0.9 indicates severe interference during transmission. Replay attack prevention is achieved by maintaining a list of verified token identifiers; duplicate identifiers are rejected outright. The verification process simultaneously extracts structured features of the handshake state matrix from extended fields of the token. The verification result output is a triple containing a validity Boolean value, a confidence score, and an error code.

[0087] The candidate key pool is activated based on the verification results, and a suitable key is selected for subsequent processing. The activation process is determined by the validity field of the verification results: when verification passes, the key pool is activated normally; when verification fails, different strategies are adopted according to the error code. Normal activation process: Access the priority queue of the candidate key pool and extract the key with the highest quality score as the first choice; check the key's validity period and usage status to ensure the key is available; if the first choice key is unavailable, try the next best key in turn, up to a maximum of 5 attempts. Anomaly handling strategy: If the timestamp is incorrect, retry after synchronizing the system clock; if the signature does not match, request a resend of the token; if the fidelity is too low, switch to a backup channel with a lower error rate. Key selection also considers the verification confidence value: for high confidence (greater than 0.95), a long key (2048 bits) is selected; for medium confidence (0.8 to 0.95), a standard key (1024 bits) is selected; for low confidence (less than or equal to 0.8), a short key (512 bits) is selected and additional verification is initiated. The selected key is removed from the pool and marked as in use to prevent concurrent conflicts. The key's metadata, including generation time, number of uses, and remaining validity period, is updated synchronously. Upon activation, a usable key and its complete attribute information are obtained.

[0088] In some embodiments, the step of fusing the available key and the key negotiation parameters into segmented quantum states according to the handshake state matrix to generate an enhanced key includes: establishing a quantum entanglement distribution adjustment based on the handshake state matrix, wherein the quantum entanglement distribution adjustment includes local entanglement distribution adjustment, non-local entanglement distribution adjustment, and hybrid entanglement distribution adjustment; using the local entanglement distribution adjustment and the non-local entanglement adjustment to perform segmented entanglement weaving on the available key to obtain an entangled key segment; using the hybrid entanglement distribution adjustment to perform segmented entanglement weaving on the key negotiation parameters to obtain an entangled parameter segment; and generating an enhanced key by fusing the quantum states of the entangled key segment and the entangled parameter segment.

[0089] Three quantum entanglement distribution adjustment modes are established based on the handshake state matrix. The fused information matrix is ​​divided into multiple sub-blocks according to function for analysis, making full use of the multidimensional quantum information in the matrix to optimize the allocation of entanglement resources. Based on the local properties of the matrix: when the entanglement degree of adjacent sub-blocks is high, the corresponding segment adopts strong local entanglement; more entanglement resources are allocated to regions with high purity within a sub-block. The global correlation mode of the non-local entanglement distribution adjustment reference matrix: long-range entanglement is established between key nodes indicated by the principal eigenvector; the entanglement strength is designed according to the decay mode of matrix elements. Where E_ij is the inter-segment entanglement strength, and M_ij is the matrix element. The hybrid entanglement distribution modulates the integration of local and non-local information, and introduces appropriate randomness based on the matrix entropy. The weights of the three modulations are dynamically determined by the matrix characteristics: increasing local weights to maintain stability at high purity, increasing hybrid weights to improve robustness at high entropy, and enhancing non-local connectivity when eigenvalues ​​are concentrated.

[0090] The available key is segmented into entangled weaving using local and non-local entanglement distribution adjustment, embedding classical key information into a quantum entanglement network. The 2048-bit available key is divided into 32 64-bit segments, with a corresponding quantum register created for each segment. Local weaving is achieved through CNOT gate chains between adjacent qubits, with the weaving density controlled by w_local. Entanglement strength decreases with a gradient within each segment, with the strongest entanglement at segment boundaries to enhance inter-segment coupling. Non-local weaving uses phase gates to connect distant qubits, selecting connection pairs at power-of-two intervals (2, 4, 8, 16, 32 bits) to form a multi-scale entangled structure. An adaptive strategy is employed during the weaving process: real-time monitoring of the entanglement degree of each segment, increasing weaving operations for segments below the target value, and introducing partial measurements to reduce entanglement for segments exceeding the target value. The weaving pattern is designed as a fractal structure, achieving global correlation while preserving local details. After each segment is woven, the mutual information of the subsystems is calculated to verify whether the entanglement distribution meets the design requirements. The 32 entangled key segments form an interconnected quantum network that retains the original key information while also possessing quantum correlation characteristics.

[0091] A hybrid entanglement distribution is used to regulate the parallel, segmented entanglement weaving of key negotiation parameters. The negotiation parameters of S140 include three categories: basis selection parameters, error correction code parameters, and privacy amplification parameters. Each category is independently woven and then cross-correlated. Parameter encoding uses continuous-variable quantum states, mapping normalized parameter values ​​to coherent state amplitudes, achieving a quantized representation of classical information. The deterministic part of the hybrid weaving strategy establishes associations based on parameter function: error correction parameters are strongly correlated with the data segments they protect, basis selection parameters correspond to measurement positions, and privacy amplification parameters are distributed globally. The random part establishes entanglement between non-functionally related parameters with probability p=0.2w_mixed, enhancing anti-attack capabilities. The weaving network topology adaptively changes with C_nego: high completion uses fully connected layers to provide redundant paths, medium completion uses sparse connections to balance efficiency, and low completion degenerates into chain connections to ensure basic functionality. The spatial distribution of entanglement strength is designed as Gaussian, with the strongest entanglement in the central parameter segment, decreasing towards the edges. The weaving operation is implemented through parameterized quantum circuits, with the circuit depth controlled within 20 layers to prevent decoherence accumulation. The completed sequence of entangled parameter segments and the entangled key segments are structurally matched, preparing for final fusion.

[0092] Enhanced keys are generated by fusing the quantum states of entangled key segments and entangled parameter segments, achieving synergistic enhancement of key security and functionality. The fusion employs precise segment-to-segment pairing, with the i-th key segment and the i-th parameter segment integrated through a two-qubit gate operation. The choice of fusion gate is adaptively adjusted based on the entanglement degree of the segment pair: high entanglement (>0.8) uses iSWAP gates to achieve maximum entanglement transfer and information mixing; medium entanglement (0.5-0.8) uses parameterized partial iSWAP gates, with the rotation angle proportional to the average entanglement degree; low entanglement (<0.5) uses control phase gates to maintain information independence while establishing phase correlation. The coherence of the fusion process is protected by a dynamic decoupling sequence, with a refocusing pulse inserted every 10 gate operations to suppress environmental noise. Inter-segment synchronization is achieved through a classical auxiliary channel to ensure correct pairing timing. The fusion quality is monitored in real time using three metrics: output state purity (target >0.9) reflecting coherence preservation, entanglement entropy (target 0.5-0.7) balancing information capacity and correlation strength, and fidelity to the ideal fused state (target >0.95). The post-processing workflow includes quantum error correction to eliminate fusion errors, privacy amplification to compress information entropy, and classical extraction to generate the final bit string. The output enhanced key integrates authentication information, negotiation parameters, and quantum correlation; its length is between 1400-1800 bits depending on the fusion efficiency, providing security guarantees exceeding those of classical keys.

[0093] Step S170: Generate the final distribution key using the enhanced key and spin staggered timing to complete the optimization of the quantum key distribution handshake protocol.

[0094] Specifically, enhanced key distribution and spin-spacing timing are used to optimize the final key distribution. The enhanced key integrates authentication information, negotiation parameters, and quantum correlation, but distribution optimization is needed based on actual channel conditions and timing requirements. Spin-spacing timing provides a multi-channel time scheduling scheme to ensure maximum channel utilization and minimum collisions during key distribution. First, the enhanced key undergoes distribution adaptation: the final key length is determined based on the target application requirements, typically 1024 or 2048 bits; when the enhanced key length is insufficient, length matching is performed using a deterministic expansion function, maintaining the original security attributes during the expansion process; when the length exceeds the limit, the highest quality continuous segment is selected as the core key. Then, segmented scheduling is performed based on spin-spacing timing: the adapted key is divided into 128-bit segments, each segment corresponding to a distribution slot; according to the spin phase allocation principle, authentication-related segments are distributed in the phase 0 to π interval, and negotiation-related segments are distributed in the phase π to 2π interval; the duration of each slot is... Where T_cycle is the complete spin cycle and N_seg is the number of key segments. The distribution process employs a quantum-classical hybrid transmission: critical key segments are transmitted through a quantum channel, utilizing the no-cloning property of quantum states to ensure security; auxiliary information, including segment indexes, checksums, and timing synchronization information, is transmitted through a classical channel. Channel coding is adaptively adjusted based on real-time channel quality: lightweight error correction codes are used for high-quality channels (QBER < 5%), and concatenated error correction codes are used for low-quality channels (QBER > 10%). After distribution, end-to-end verification is performed: the receiver reassembles the key segments in the correct order, calculates the overall hash value, and compares it with the sender's hash to confirm key integrity and consistency. The final output distribution key undergoes a complete handshake optimization process, possessing high security, high efficiency, and strong robustness.

[0095] To implement the optimized method for the quantum key distribution handshake protocol corresponding to the above method embodiments, in order to achieve the corresponding functions and technical effects. See also Figure 2 , Figure 2 This diagram illustrates a structural block diagram of a quantum key distribution handshake protocol optimization device 200 provided in an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The quantum key distribution handshake protocol optimization device 200 provided in this embodiment includes:

[0096] The data acquisition module 201 is used to acquire entangled state distribution data of the quantum channel and handshake delay records of the classical channel, extract the qubit error rate based on the entangled state distribution data, perform fluctuation analysis on the qubit error rate to obtain the quantum noise mode, and deduce the detection time set through the quantum noise mode.

[0097] Architecture construction module 202 is used to separate transmission delay components and processing delay components using the handshake delay record, map the transmission delay components and processing delay components to spin vectors, perform phase modulation on the spin vectors to form a spin-shaving timing sequence, and establish a parallel verification architecture based on the spin-shaving timing sequence; the step of performing phase modulation on the spin vectors to form a spin-shaving timing sequence includes: performing eigenstate decomposition on the spin vectors to obtain upper spin eigenstates, lower spin eigenstates and superimposed spin eigenstates; establishing a forward phase modulation rule based on the upper spin eigenstates, and establishing a reverse phase modulation rule based on the lower spin eigenstates; and coordinating the forward phase modulation rule and the reverse phase modulation rule using the superimposed spin eigenstates to form a spin-shaving timing sequence;

[0098] The verification code book module 203 is used to set multiple parallel execution windows according to the parallel verification architecture and the probe timing set, send pre-verification probe packets through the parallel execution windows and collect responses to obtain a verification response sequence, generate verification feature codes based on the verification response sequence, and construct a pre-matching code book using the verification feature codes;

[0099] The state matrix module 204 is used to construct an authentication sub-process and a negotiation sub-process using the parallel verification architecture, generate a quantum signature token through the authentication sub-process, obtain key negotiation parameters through the negotiation sub-process, and fuse the quantum signature token and the key negotiation parameters in real time to form a handshake state matrix.

[0100] The key pool construction module 205 is used to perform handshake prediction analysis using the handshake state matrix and the pre-matched codebook to obtain a success probability value, trigger a key pre-generation mechanism together with the success probability value and the handshake state matrix, prepare a preliminary key with the help of the key pre-generation mechanism, and construct a candidate key pool using the preliminary key.

[0101] Key enhancement module 206 is used to perform legality verification based on the quantum signature token to generate a verification result, activate the candidate key pool based on the verification result to obtain a usable key, and fuse the usable key with the key negotiation parameters according to the handshake state matrix in a segmented quantum state to generate an enhanced key.

[0102] The key distribution module 207 is used to generate the final distribution key using the enhanced key and the spin staggered timing, thereby optimizing the quantum key distribution handshake protocol.

[0103] The quantum key distribution handshake protocol optimization apparatus 200 described above can implement the quantum key distribution handshake protocol optimization method of the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application embodiment can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0104] like Figure 3 As shown, the third embodiment of the present invention also provides a computer device, including a memory 301, a processor 302, and a computer program stored in the memory 301 and executable on the processor 302, characterized in that the processor 302 executes the program to implement the steps of the optimization method of a quantum key distribution handshake protocol described in the first embodiment of the present invention.

[0105] The purpose of the above embodiments is to reproduce and derive the technical solution of the present invention by way of example, and to fully describe the technical solution, purpose and effect of the present invention. The purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosure of the present invention, and not to limit the scope of protection of the present invention.

[0106] The above embodiments are not an exhaustive list based on the present invention, and there may be many other embodiments not listed. Any substitutions and improvements made without departing from the concept of the present invention are within the protection scope of the present invention.

Claims

1. An optimization method for a quantum key distribution handshake protocol, characterized in that, include: The entangled state distribution data of the quantum channel and the handshake delay record of the classical channel are collected. The qubit error rate is extracted based on the entangled state distribution data. Fluctuation analysis is performed on the qubit error rate to obtain the quantum noise mode. The detection time set is derived in reverse from the quantum noise mode. The handshake delay record is used to separate the transmission delay component and the processing delay component. The transmission delay component and the processing delay component are mapped to spin vectors. The spin vectors are phase-modulated to form a spin-shaving timing sequence. A parallel verification architecture is established based on the spin-shaving timing sequence. The step of phase-modulating the spin vector to form the spin-shaving timing sequence includes: performing eigenstate decomposition on the spin vector to obtain an upper spin eigenstate, a lower spin eigenstate, and a superimposed spin eigenstate; establishing a forward phase modulation rule based on the upper spin eigenstate and an inverse phase modulation rule based on the lower spin eigenstate; and using the superimposed spin eigenstate to coordinate the forward phase modulation rule and the inverse phase modulation rule to form the spin-shaving timing sequence. Multiple parallel execution windows are set according to the parallel verification architecture and the probe timing set. Pre-verification probe packets are sent through the parallel execution windows and responses are collected to obtain a verification response sequence. Verification feature codes are generated based on the verification response sequence. A pre-matching codebook is constructed using the verification feature codes. The parallel verification architecture is used to construct an authentication sub-process and a negotiation sub-process. The authentication sub-process generates a quantum signature token, and the negotiation sub-process transmits key negotiation parameters. The quantum signature token and the key negotiation parameters are fused in real time to form a handshake state matrix. The success probability value is obtained by performing handshake prediction analysis using the handshake state matrix and the pre-matched codebook. The key pre-generation mechanism is triggered together with the success probability value and the handshake state matrix. A preliminary key is prepared using the key pre-generation mechanism. A candidate key pool is constructed using the preliminary key. The validity of the quantum signature token is verified to generate a verification result. Based on the verification result, the candidate key pool is activated to obtain a usable key. The usable key and the key negotiation parameters are fused into segmented quantum states according to the handshake state matrix to generate an enhanced key. The final distribution key is generated using the enhanced key and the spin staggered timing, thus optimizing the quantum key distribution handshake protocol.

2. The method according to claim 1, characterized in that, The step of extracting the qubit error rate based on the entangled state distribution data includes: The quantum coherence decay curve is generated by tracking the quantum coherence decrease of the entangled state distribution data. Identify coherence loss nodes based on the coherence decay curve; Information entropy increase analysis is performed using the coherence loss nodes to form the qubit error rate.

3. The method according to claim 1, characterized in that, The step of generating a verification signature based on the verification response sequence includes: The original quantum state information is obtained by reverse reconstruction of the verification response sequence through quantum measurement. Quantum coherence fingerprints are extracted based on the original quantum state information; The quantum state overlap characteristics are obtained by performing quantum state overlap analysis using the aforementioned quantum coherence fingerprint; The overlapping features are quantum encoded to form a verification feature code.

4. The method according to claim 1, characterized in that, The step of fusing the quantum signature token and the key negotiation parameters in real time to form a handshake state matrix includes: The quantum signature token is analyzed using quantum state parsing to obtain the token state vector; The key negotiation parameters are vectorized to obtain the negotiation state vector; The token state vector and the negotiation state vector are subjected to tensor product operation to obtain the fusion state matrix; The fusion state matrix is ​​normalized to form a handshake state matrix.

5. The method according to claim 1, characterized in that, The step of using the handshake state matrix and the pre-matched codebook to perform handshake prediction analysis to obtain a success probability value includes: Extract the quantum entanglement correlation strength from the handshake state matrix; Based on the quantum entanglement correlation strength, a long-range quantum state correlation analysis is performed to obtain the correlation probability distribution; The correlation probability distribution is predicted by quantum measurement using the pre-matched codebook to obtain a success probability value.

6. The method according to claim 1, characterized in that, The step of fusing the available key and the key negotiation parameters into segmented quantum states according to the handshake state matrix to generate an enhanced key includes: Quantum entanglement distribution regulation is established based on the handshake state matrix, and the quantum entanglement distribution regulation includes local entanglement distribution regulation, non-local entanglement distribution regulation, and mixed entanglement distribution regulation; The available key is segmented and entangled to obtain entangled key segments by using the local entanglement distribution adjustment and the non-local entanglement distribution adjustment. The key negotiation parameters are segmented and entangled using the hybrid entanglement distribution adjustment to obtain entangled parameter segments. An enhanced key is generated by fusing the quantum states of the entangled key segment and the entangled parameter segment.

7. The method according to claim 4, characterized in that, The step of performing a tensor product operation on the token state vector and the negotiation state vector to obtain the fused state matrix includes: Perform coordination analysis on the token state vector and the negotiation state vector to obtain strong coordination components and weak coordination components; Based on the strong coordination component and the weak coordination component, balance characteristic analysis is performed to obtain the balance adjustment factor. A coordinated balance network is constructed using the aforementioned balance adjustment factor; The vector fusion operation is performed based on the coordinated balance network to form a fusion state matrix.

8. An optimization device for a quantum key distribution handshake protocol, characterized in that, include: The data acquisition module is used to acquire entangled state distribution data of the quantum channel and handshake delay records of the classical channel. It extracts the qubit error rate based on the entangled state distribution data, performs fluctuation analysis on the qubit error rate to obtain the quantum noise mode, and derives the detection time set through the quantum noise mode. An architecture construction module is used to separate transmission delay components and processing delay components using the handshake delay record, map the transmission delay components and the processing delay components to spin vectors, perform phase modulation on the spin vectors to form a spin-shaving timing sequence, and establish a parallel verification architecture based on the spin-shaving timing sequence. The step of performing phase modulation on the spin vectors to form the spin-shaving timing sequence includes: performing eigenstate decomposition on the spin vectors to obtain upper spin eigenstates, lower spin eigenstates, and superimposed spin eigenstates; establishing a forward phase modulation rule based on the upper spin eigenstates and an inverse phase modulation rule based on the lower spin eigenstates; and coordinating the forward and inverse phase modulation rules using the superimposed spin eigenstates to form the spin-shaving timing sequence. The CAPTCHA module is used to set multiple parallel execution windows according to the parallel verification architecture and the probe timing set, send pre-verification probe packets through the parallel execution windows and collect responses to obtain a verification response sequence, generate verification feature codes based on the verification response sequence, and construct a pre-matching codebook using the verification feature codes. The state matrix module is used to construct an authentication sub-process and a negotiation sub-process using the parallel verification architecture. The authentication sub-process generates a quantum signature token, and the negotiation sub-process transmits key negotiation parameters to be obtained. The quantum signature token and the key negotiation parameters are fused in real time to form a handshake state matrix. The key pool construction module is used to perform handshake prediction analysis using the handshake state matrix and the pre-matched codebook to obtain a success probability value, trigger a key pre-generation mechanism together with the success probability value and the handshake state matrix, prepare a preliminary key with the help of the key pre-generation mechanism, and construct a candidate key pool using the preliminary key; The key enhancement module is used to perform legality verification based on the quantum signature token to generate a verification result, activate the candidate key pool based on the verification result to obtain a usable key, and fuse the usable key with the key negotiation parameters according to the handshake state matrix in a segmented quantum state to generate an enhanced key. The key distribution module is used to generate the final distribution key using the enhanced key and the spin staggered timing, thereby optimizing the quantum key distribution handshake protocol.

9. A computer device, characterized in that, It includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and, when executing the computer program, implement the method as described in any one of claims 1 to 7.

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