Reconfigurable quantum communication route synchronization system for urban trunk network

Abstract

The application discloses a reconfigurable quantum communication route synchronization system for urban trunk network, relates to the technical field of quantum communication, and comprises a quantum channel state perception module, a clock offset prediction and registration module, a reconfigurable route planning engine, an entangled state registration and regulation module, a synchronization instruction scheduling module and an optimization module; the system perceives the decoherence time, entanglement fidelity and synchronization offset risk of a quantum link in real time, generates a quantum link feasibility score map in combination with a graph neural network, and outputs an optimal entanglement forwarding path accordingly; meanwhile, the transmission window width and rhythm are dynamically adjusted based on the task priority and network load, high-precision synchronization and resource coordination across nodes are realized, the communication performance evaluation model and feedback learning mechanism are introduced, and the route and synchronization strategy can be continuously optimized; and the application significantly improves the stability of the quantum communication system in a complex urban network environment.

Description

Technical Field

[0001] This invention relates to the field of quantum communication technology, and more specifically to a reconfigurable quantum communication routing synchronization system for urban trunk networks. Background Technology

[0002] As urban communication network architectures become increasingly complex and heterogeneous, traditional routing communication technologies face numerous challenges in the efficient synchronization and dynamic reconstruction of multi-source heterogeneous quantum signals. Most existing quantum communication networks rely on static topologies and single-path quantum key distribution mechanisms, making them ill-suited for the high-frequency, high-concurrency, and multi-path communication scenarios in urban trunk networks. This leads to problems such as low utilization of quantum channel resources, increased network congestion risk, and decreased synchronization accuracy.

[0003] Furthermore, quantum routing strategies generally lack dynamic sensing and control mechanisms for channel decoherence, inter-node clock offset, and changes in quantum state entanglement strength. Especially in urban multi-hop trunk networks, the coupling effect of inter-node link delay fluctuations and equipment temperature drift noise is significant, which seriously restricts the synchronization reliability of quantum communication systems. Summary of the Invention

[0004] The purpose of this invention is to provide a reconfigurable quantum communication routing synchronization system for urban trunk networks, in order to address the shortcomings of the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a reconfigurable quantum communication routing synchronization system for urban trunk networks, comprising:

[0006] The quantum channel state sensing module is used to acquire the decoherence time, entanglement fidelity and channel interference index of each quantum link in the urban trunk network in real time, and to construct a channel state feature matrix.

[0007] The clock offset prediction and registration module is used to construct a node synchronization offset estimation model based on the historical delay sequence between nodes and the bit error rate of qubits, and output the clock offset prediction curve.

[0008] A reconfigurable routing planning engine is used to generate a quantum link feasibility scoring map based on the channel state feature matrix and clock offset prediction curve using a graph neural network algorithm, and output the optimal entangled forwarding path.

[0009] The entanglement registration and control module is used to perform entanglement strength control operations between key nodes in the optimal entanglement forwarding path and generate synchronization control signals.

[0010] The synchronization instruction scheduling module is used to trigger the node controller to perform corresponding synchronization operations based on the control results, and to dynamically calibrate and adjust the rhythm of the quantum bit transmission window;

[0011] The optimization module is used to analyze the path stability, synchronization delay, and quantum resource utilization of each communication task, and optimize the route reconstruction and synchronization strategies in subsequent tasks based on the analysis results.

[0012] Preferably, the quantum channel state sensing module includes:

[0013] Non-invasive sampling and detection of entangled photons transmitted in a quantum link were performed to extract the single-photon count change rate and interference fringe phase drift characteristics related to the link decoherence process.

[0014] By integrating the synchronous clock drift logs, physical layer noise detection data, and historical bit error rate curves from edge nodes, a multi-channel neural network model is constructed to output the entanglement fidelity prediction value of each quantum link under the current topology.

[0015] The predicted values ​​of decoherence time, entanglement fidelity, and link interference entropy are normalized to generate a channel state feature matrix for quantum routing decisions.

[0016] Preferably, the clock offset prediction and registration module includes:

[0017] Historical time delay sequences between adjacent nodes in an urban trunk quantum communication network are collected. Cross-node time offset logs are constructed based on quantum state reception timestamps and reference clock signals, and drift trends and abrupt change characteristics are extracted from them.

[0018] By combining the bit error rate variation curves of qubit pairs under different load cycles, a matrix of correlation factors between delay drift and bit error rate is constructed, and a synchronization offset estimation function is established using a weighted multivariate regression model.

[0019] Based on the output offset prediction value of the synchronization offset estimation function, clock offset prediction curves for node pairs in future communication cycles are generated.

[0020] Preferably, the reconfigurable route planning engine includes:

[0021] By mapping the channel state feature matrix to the clock offset prediction curve, a hetero-weighted graph model is constructed with nodes representing quantum communication devices and edges representing quantum links. The edge weights simultaneously encode entanglement fidelity, decoherence time, and synchronization offset risk indicators.

[0022] The heterogeneous weighted graph model is trained and its features are aggregated based on the graph neural network algorithm. The global feasibility score of the link is learned by utilizing the information propagation mechanism between nodes, and the quantum link feasibility score map is output.

[0023] Based on the quantum link feasibility scoring graph, a reconfigurable path search algorithm is executed. Combining the task requirements of the target node pair, real-time load, and entangled link cache status, the optimal entangled forwarding path is dynamically output.

[0024] Preferably, the entangled state registration and control module includes:

[0025] Entanglement strength baseline measurements are performed on key relay node pairs in the optimal entanglement forwarding path. Based on the deviation between the measured entanglement fidelity and the expected transmission standard, the quantum state registration offset is calculated.

[0026] Based on the quantum state registration offset and clock synchronization state, the photon entanglement pump power and quantum state preparation rate between relay nodes are dynamically adjusted;

[0027] A synchronization control signal containing entanglement strength adjustment parameters, synchronization window offset compensation amount, and registration completion status is generated and distributed to each node in the path, triggering the local controller to execute the corresponding synchronization adjustment command.

[0028] Preferably, the synchronization instruction scheduling module includes:

[0029] The synchronization control signal is analyzed, the window offset compensation amount, rhythm adjustment factor and registration status flag of each target node are extracted, and mapped into a standardized synchronization control instruction set.

[0030] The corresponding synchronization control command is issued to the controller of each relay node in the path to trigger the local oscillator frequency adjustment, transmission timing correction and reception window delay configuration of the node;

[0031] The width of the quantum bit transmission window and the rhythm interval are dynamically adjusted based on the priority of the communication task and the current network load.

[0032] Preferably, dynamically adjusting the width of the quantum bit transmission window and the rhythm interval includes:

[0033] Collect the node queuing depth, entangled link occupancy rate and task scheduling delay parameters in the current quantum communication network, and construct a time resource scheduling factor vector by combining the priority labels of each communication task;

[0034] Based on the time resource scheduling factor vector, the quantum bit transmission window width and rhythm interval of each path are dynamically calculated using a hierarchical weighted scheduling algorithm;

[0035] The calculation results are synchronized to the local controllers of each node in the path, and the window configuration parameters are adjusted in real time based on the node's responsiveness and synchronization status.

[0036] Preferably, the optimization module includes:

[0037] Record path stability indicators, synchronization delay statistics, and quantum resource occupancy data during each communication task execution, and archive and store them in a structured and tagged manner according to task identifiers;

[0038] Feature clustering is performed on archived data to construct a communication performance evaluation model and extract key influencing factors, including link fidelity volatility, synchronization rhythm error range and node processing lag threshold.

[0039] Based on the aforementioned communication performance evaluation model, and considering the current network status and task requirements, the quantum routing reconstruction rules and synchronization rhythm strategy are dynamically adjusted.

[0040] Preferably, the method of performing feature clustering on archived and stored data to construct a communication performance evaluation model includes: selecting multi-dimensional indicators for historical archived communication task data, including path interruption frequency, average synchronization offset, unit bit entanglement resource consumption, and task completion delay, and constructing a communication performance feature matrix; based on the communication performance feature matrix, using a density clustering algorithm to classify task samples, identifying different communication behavior patterns, and using these patterns as the basis to train the communication performance evaluation model.

[0041] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0042] 1. This invention constructs a reconfigurable quantum communication routing and synchronization system for urban trunk quantum communication networks, achieving full-process perception and intelligent control of quantum link states, clock offset trends, entanglement consistency, and multi-task scheduling rhythm. The system utilizes graph neural networks to generate quantum link feasibility scoring maps and combines a feedback learning mechanism to continuously optimize path reconstruction and synchronization strategies, effectively improving the stability of quantum communication links and overcoming the problem that traditional static quantum routing is difficult to adapt to dynamic and complex environments.

[0043] 2. This invention, by introducing a dynamic scheduling mechanism based on task priority and network load status, and a closed-loop control structure of entanglement registration and rhythm control, enables the system to adaptively adjust the transmission window width and synchronization rhythm in multi-task, high-concurrency scenarios. This ensures the quality of service for high-priority tasks and improves overall network throughput. This technical solution possesses strong self-learning capabilities, policy transfer capabilities, and deployment flexibility, providing crucial technical support for intelligent scheduling and resource optimization in future large-scale quantum networks. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0045] Figure 1 This is a flowchart of the system modules of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] For examples, please refer to Figure 1 As shown in this embodiment, the reconfigurable quantum communication routing synchronization system for urban trunk networks includes:

[0048] The quantum channel state sensing module is used to acquire the decoherence time, entanglement fidelity and channel interference index of each quantum link in the urban trunk network in real time, and to construct a channel state feature matrix.

[0049] The clock offset prediction and registration module is used to construct a node synchronization offset estimation model based on the historical delay sequence between nodes and the bit error rate of qubits, and output the clock offset prediction curve.

[0050] A reconfigurable routing planning engine is used to generate a quantum link feasibility scoring map based on the channel state feature matrix and clock offset prediction curve using a graph neural network algorithm, and output the optimal entangled forwarding path.

[0051] The entanglement registration and control module is used to perform entanglement strength control operations between key nodes in the optimal entanglement forwarding path and generate synchronization control signals.

[0052] The synchronization instruction scheduling module is used to trigger the node controller to perform corresponding synchronization operations based on the control results, and to dynamically calibrate and adjust the rhythm of the quantum bit transmission window;

[0053] The optimization module is used to analyze the path stability, synchronization delay, and quantum resource utilization of each communication task, and optimize the route reconstruction and synchronization strategies in subsequent tasks based on the analysis results.

[0054] The quantum channel state sensing module in this embodiment of the invention aims to provide high-precision, high-real-time quantum link state data support for routing synchronization control in urban trunk quantum communication networks. By dynamically extracting and intelligently predicting key parameters such as link decoherence time, entanglement fidelity, and channel interference entropy, the system can generate a multi-dimensional quantum channel state feature matrix, providing quantitative basis for subsequent modules such as routing path optimization, entanglement state registration, and synchronization control.

[0055] In this step, the system first deploys photon detector arrays at both ends of the quantum link to perform non-invasive behavior detection of entangled photon pairs transmitted in the link. This detection process does not destroy the quantum state itself, employing non-destructive single-photon counting technology combined with high-precision time-resolved photomultiplier tubes to timestamp single-photon events. By analyzing the rate of change of single-photon counts within a unit time window, the dynamic characteristics of the decoherence process in the link can be extracted, manifested as enhanced count fluctuations and increased noise background.

[0056] Simultaneously, based on interferometry, interference fringe images are acquired and their phase drift trend curves over time are extracted. Significant shifts, blurring, or abnormally accelerated drift rates in the interference fringes typically indicate an increased risk of decoherence due to environmental disturbances (such as temperature drift or electromagnetic interference) in the link. These characteristics are encoded as physical-scale link variability factors and serve as an important input dimension for subsequent neural network modeling.

[0057] To ensure detection accuracy, background noise is removed and statistical filtering is performed on the detection signal in this step. A weighted moving average algorithm is used to calculate the single photon count change rate and the interference fringe drift rate, and the decoherence correlation physical feature vector is output.

[0058] After completing the physical layer feature extraction, the system further combines the synchronization clock drift logs recorded in the edge nodes, physical layer noise detection data, and historical bit error rate (QBER) variation curves to construct a multi-channel neural network model based on time-series modeling. This model aims to predict the entanglement fidelity of each quantum link under the current topology in real time.

[0059] The clock drift log provides the trend of time base differences between nodes in the network, which can be used to infer entanglement degradation caused by link synchronization instability; the physical layer noise data includes multi-source interference factors such as temperature disturbances, voltage fluctuations, and fiber attenuation, which can quantify the intensity of external disturbances to the link; the historical bit error rate curve reflects the stability of entanglement quality of the link under different loads in the past period. The system uses the above multimodal data as independent input channels and inputs them to the fusion layer for feature integration.

[0060] The neural network employs a structure combining bidirectional gated recurrent units (Bi-GRU) and convolutional layers to capture temporal correlations in the data and identify frequency domain pattern features. The model is trained by minimizing the mean square error between the predicted fidelity and the measured value, and finally outputs a continuous variable between zero and one, representing the predicted entanglement fidelity of the current link under given perturbation conditions.

[0061] To enhance the model's generalization ability under different link types (such as short-distance direct connection vs. long-distance multi-hop connection), a transfer learning mechanism and attention weight adjustment strategy are introduced during the training phase, enabling it to automatically identify link characteristics and adjust prediction bias.

[0062] Based on the predicted values ​​of link decoherence time and entanglement fidelity, the system further introduces the channel interference entropy index to form a complete link state description triplet. The channel interference entropy is calculated from the distribution and rate of change of photon detection noise per unit time of the link, and is used to quantify the comprehensive interference intensity of the external environment on the stability of the link signal.

[0063] The three parameters mentioned above—decoherence time (in seconds), entanglement fidelity prediction (a dimensionless continuous variable), and interference entropy (in bits per second)—exhibit differences in both magnitude and physical meaning. To standardize the input and ensure the comparability of the quantum routing engine for different link states, the system performs Z-score normalization on each of the three metrics to eliminate the influence of dimensionality. The normalized data is organized into a matrix structure indexed by link number, called the "channel state feature matrix."

[0064] In this matrix, each row corresponds to a physical link, and each column represents its standard values ​​for decoherence time, entanglement fidelity, and interference entropy, respectively. This matrix can be updated in real time and shared with the subsequent reconfigurable routing planning engine through a caching mechanism.

[0065] The clock offset prediction and registration module proposed in this embodiment of the invention is used to achieve time reference alignment between different nodes in an urban trunk quantum communication network. Since quantum communication systems rely on high-precision time synchronization to ensure the correct identification of entangled states and the matching of transmission windows, significant clock offsets between nodes can easily lead to entangled state registration failure, increased qubit error rate, and consequently affect the reliability and security of the entire quantum routing link.

[0066] First, the system continuously collects time delay sequence data from historical communication processes between adjacent nodes. This data includes the receiving timestamp (recorded by the local detector) and the standard time corresponding to the global reference clock signal for each quantum state transmission. The system generates a cross-node time offset log by comparing the difference between the receiving time and the reference time.

[0067] To ensure data stability, a sliding time window mechanism (e.g., a window every 5 minutes) is used to filter and denoise the raw time delay data, and a coefficient of variation analysis method is introduced to determine its volatility. When the time offset value within a certain window shows a sudden change or a linear drift trend, the system marks that segment as a potential drift anomaly period.

[0068] In the process of extracting clock offset trends, the system uses local linear regression (LOWESS) to fit the time offset sequence, calculates the drift rate (i.e., the increment of offset per unit time) at each time point, and further identifies the drift direction (forward or reverse) and the frequency of change, providing dynamic time feature support for subsequent model construction.

[0069] After the historical time offset trend is extracted, the system further introduces the quantum bit error rate (QBER) variation curve as an important auxiliary data for synchronization modeling.

[0070] Specifically, the system performs long-term monitoring of the bit error rate between node pairs under different communication load cycles and performs periodic segmentation processing on the bit error rate curve. For example, the bit error rate is sampled every 10 minutes, and its statistical characteristics such as mean, rate of change, and peak time point are recorded.

[0071] Subsequently, the system performs time-domain alignment processing on the bit error rate feature sequence and the aforementioned delay drift rate sequence to identify whether there is a direct correlation between bit error rate changes and clock offset. By calculating the correlation coefficient between the bit error rate change gradient and the drift rate within the same time window, the system constructs a delay drift and bit error rate correlation factor matrix.

[0072] Each row of the matrix represents measurement data within a time window, including the mean change in bit error rate, bit error gradient, drift direction, drift rate, etc.; each column represents a feature dimension, and the whole matrix constitutes a multivariate input sample.

[0073] The system uses this factor matrix as training samples to construct a weighted multivariate regression model. The target variable of the regression model is the offset value at future time points, and the input variables are the error rate characteristics and drift characteristics within the current time period. To improve the model's prediction accuracy, the system assigns dynamic weights to each input feature, with the weight values ​​determined by the following two factors:

[0074] This feature has a statistical correlation with historical offset trends;

[0075] The perturbation sensitivity of this feature under the current network load conditions.

[0076] The weight adjustment follows a gradient weighting mechanism, prioritizing the strengthening of error factors that are highly sensitive to synchronous changes.

[0077] Ultimately, the model output is a set of adjustable offset prediction functions, whose inputs are the bit error rate characteristics and drift index of the current time period, and whose outputs are the offset prediction values ​​at several future time points.

[0078] Based on the synchronization offset estimation function trained above, the system can calculate the offset prediction value sequence for future communication cycles in real time for the input data within a given time window, that is, generate a complete clock offset prediction curve.

[0079] The prediction curve reflects the evolution trend of the synchronization state between current node pairs over a future period (e.g., the next 15 minutes or 1 hour). The extreme points and inflection points in the curve can provide early warning of potential synchronization mismatch risks, providing an early warning mechanism for modules such as window scheduling and clock registration of entangled states.

[0080] In practical applications, the system uses the output value of the predicted curve as the input signal and transmits it to the local controller of the quantum node, which drives the local frequency-stabilized oscillator to perform fine-tuning operations, thereby achieving frequency-level clock synchronization correction.

[0081] In addition, the system has a prediction error feedback mechanism. Whenever the difference between the actual measured offset value and the prediction curve exceeds a set threshold, the incremental update process of the model will be triggered, thereby continuously optimizing the adaptability and robustness of the synchronous estimation model in the dynamic urban environment.

[0082] This invention provides a reconfigurable routing planning engine, aiming to construct a quantum routing decision-making system with environmental awareness, autonomous learning, and path reconstruction capabilities to address multi-dimensional factors such as dynamic changes in quantum links, synchronization offset uncertainties, and resource bottlenecks in urban trunk quantum communication networks. Based on the aforementioned channel state feature matrix and clock offset prediction curve, the engine models the global network state using a graph neural network algorithm, outputs a quantum link feasibility score map, and performs optimal entangled path search and replanning under multiple constraints.

[0083] First, the system abstracts the quantum communication network as a graph structure with edge weights. Each node in the graph represents a physical device in the city's quantum communication network (such as a quantum router, quantum repeater, entanglement source, etc.), and each edge represents a usable quantum link. Each edge has multiple physical attribute indicators, including but not limited to:

[0084] Entanglement fidelity represents the quantum purity of entangled pairs during link transmission;

[0085] Decoherence time, the duration during which the quantization link remains stable in the quantum state;

[0086] The synchronization offset risk index, derived from the clock offset prediction curve analysis in the aforementioned module, reflects the degree of impact of potential synchronization errors in the current link on communication.

[0087] To accurately represent the heterogeneity and joint influence among the aforementioned attributes, the system adopts a heterogeneous weighted graph model, which sets multiple weight dimensions for each edge. Specifically, the three indicators of each edge are mapped to the [0,1] interval through normalization, and then a joint edge weight function is defined according to different task strategies. For example, a weighted average function or an exponential weighting function is used to combine the edge weights to form the final graph data structure that can be processed by GNN.

[0088] In addition, nodes themselves can carry several static or dynamic attributes, such as device processing power, cache capacity, and current load, providing more comprehensive input information for subsequent model training.

[0089] The constructed heterogeneous weighted graph is then input into a Graph Neural Network (GNN) model. As a deep learning method for processing structured graph data, GNN can automatically learn the complex relationships between nodes and edges in the graph and uncover potential network structure patterns through the information aggregation and propagation mechanism between nodes.

[0090] This invention employs a GNN architecture based on a message-passing mechanism, where in each round of propagation, each node in the graph receives information from its neighboring nodes and updates its state using an aggregation function. After multiple propagation rounds, this process enables each node's state to encode the contextual information of other nodes in the entire network.

[0091] Specifically, the GNN input is the aforementioned graph structure, and the model considers the multidimensional weights of each edge during each propagation process. By propagating entanglement fidelity, decoherence states, and synchronization offset risks along the edges, the model automatically adjusts its path preferences, tending to retain links with high stability and low synchronization risk.

[0092] During training, the optimal path in historical network states is used as the supervision signal. A loss function is constructed by combining the actual stability indicators of the path (such as transmission success rate and average bit error rate), and the model is optimized through backpropagation. The output is a feasibility score for each edge, forming a quantum link feasibility score graph. Each edge is assigned a continuous feasibility score between 0 and 1, reflecting the credibility of the link for entanglement forwarding in the current state.

[0093] After generating the link feasibility scoring map, the system enters the path planning phase. To achieve efficient and dynamic entangled forwarding, the system considers the following three factors:

[0094] Task requirements constraints include: source and target node pairs, required number of entangled link layers (i.e., number of hops), latency limit, security level, etc.

[0095] Real-time link status: reflects the current availability of the link, such as whether the entanglement buffer is sufficient and whether the link is congested;

[0096] Path score feedback: Feasibility score from the aforementioned GNN output, used for priority ranking in path selection.

[0097] This invention proposes a constraint-aware path search algorithm (such as one based on an improved Dijkstra's or A* search), which performs path traversal on a rating graph and prioritizes the path with the highest cumulative rating that satisfies the task constraints as the candidate path.

[0098] If, during the path establishment process, the status of certain links deteriorates rapidly (e.g., a sudden drop in fidelity or excessive synchronization offset), the system will trigger a path reconstruction mechanism to recalculate feasible paths in the scoring graph, automatically bypassing faulty links and achieving uninterrupted entangled communication service.

[0099] Specifically, the system first initializes the path search task in the quantum link feasibility scoring graph, sets the source node and the target node, and embeds task constraints (such as maximum hop count, minimum entanglement fidelity threshold, maximum delay limit, etc.) as screening conditions into the search logic. Based on the improved A* algorithm, when evaluating each candidate path, the system not only considers the cumulative score from the starting point to the current node, but also introduces a heuristic function based on the distance to the target node, and determines whether all constraints are satisfied in each step of path expansion. If the current path has a link with an edge weight lower than the fidelity threshold or a synchronization offset risk exceeding the limit, the path is immediately pruned and skipped.

[0100] Once a path is established, the system initiates a status monitoring process for that path, listening for fluctuations in the link's status in real time. If the fidelity of any link decreases at a rate exceeding a set threshold, or if the predicted synchronization offset curve shows a sharp upward trend, the system immediately identifies this as a "link degradation event" and triggers a path reconstruction mechanism. Under this mechanism, unusable links in the original path are marked as high-risk edges in the scoring graph and temporarily excluded. The system then re-executes the path search on the updated scoring graph, ensuring that the entangled communication process is not interrupted, ultimately forming a highly robust quantum communication link with dynamic fault tolerance.

[0101] In quantum communication networks, the stability and consistency of entangled states are the core foundation for ensuring high-fidelity transmission of multi-hop quantum links. Especially in urban trunk network environments, due to the influence of physical factors such as link delay fluctuations, light source temperature drift, and device aging, the actual transmission fidelity of quantum entangled pairs often deviates from the expected standard, thus affecting the connection quality and synchronization efficiency of entangled links.

[0102] To address this, the entangled state registration and control module proposed in this invention is specifically designed to perform fine-grained calibration and synchronization control operations on entangled states between key relay nodes in the optimal entangled forwarding path. This module combines entanglement fidelity measurement results with clock synchronization status to dynamically adjust photon source parameters and entanglement generation strategies.

[0103] First, the system identifies key relay node pairs in the current optimal entanglement forwarding path, i.e., the connection nodes between multiple relay stations that must be traversed to transmit the quantum state throughout the entire quantum link. The system performs entanglement strength baseline measurement operations between these node pairs to evaluate the actual quantum entanglement fidelity of the current transmission.

[0104] Fidelity measurement employs the Bell state benchmark detection method, which involves statistically sampling the Bell state interference results of paired entangled photons to calculate the purity index of the entangled state. Fidelity measurements typically range between 0 and 1, with values ​​closer to 1 indicating higher entanglement quality.

[0105] The system pre-defines a set of expected transmission standard parameters, including the minimum entanglement fidelity limit under different link lengths, equipment types, and transmission loss conditions. For example, it specifies that the expected fidelity for short-range urban links should not be lower than 0.88, while long-range links can tolerate up to 0.80.

[0106] After receiving the measured fidelity, the system calculates the difference between it and the expected value for the corresponding link to obtain the entanglement registration offset. In words, this offset equals the measured fidelity minus the expected fidelity. If the result is negative, it indicates that the current link status is below the standard and needs to enter the adjustment phase.

[0107] After identifying the offset, the system enters the entanglement control phase. To ensure the consistency of the entangled states, this invention proposes a multi-factor driven control strategy that simultaneously considers the quantum state registration offset and the clock synchronization state between the current node pairs.

[0108] Control operations mainly include the following two types of parameter adjustments:

[0109] Photon entanglement pump power adjustment: The system changes the photon generation rate and quality by controlling the laser pump power of the entanglement source. If the offset is large (i.e., the fidelity is significantly lower than the standard), the system will increase the pump power to increase the generation rate and energy of entangled photon pairs, thereby improving the interference quality and entanglement strength; if the offset is small, the power will be fine-tuned to reduce energy consumption.

[0110] Quantum state preparation rate adjustment: The system adjusts the photon pair transmission rhythm by controlling the operating frequency and time window of the entanglement source to coordinate the quantum state reception timing between nodes and reduce state mismatch caused by clock skew. For example, when the synchronization offset prediction value indicates that a node has a microsecond-level advance or lag, the system will adjust its transmission window offset accordingly to achieve consistency in quantum state arrival time.

[0111] The adjustment strategy follows the control principle of "primarily focusing on entanglement offset and secondarily on synchronization status". When the offset is the main source of error, the entanglement generation parameters are adjusted first; when the synchronization mismatch is significant, the transmission time window is adjusted first.

[0112] In addition, the system establishes a set of empirical models to fit the registration offset with the pump power change curve. For example, through a polynomial regression function, each 0.01 unit fidelity deviation is mapped to a specific power amplification factor to ensure that the control operation has high precision and controllability.

[0113] After completing the above parameter calculations, the system generates a set of synchronization control signals containing complete control information. These signals are in a structured data format and include:

[0114] Entanglement strength adjustment parameters: such as target pump power setting value, light source emission frequency;

[0115] Synchronization window offset compensation: the amount of time (in nanoseconds) by which a node should delay or advance its transmission.

[0116] Registration completion status flag: indicates whether the current control operation has achieved the expected entanglement fidelity.

[0117] The system broadcasts the synchronization signal to all key nodes along the optimal path. Upon receiving the signal, each node's local controller parses it and executes corresponding operations. These operations include hardware-level control such as calling the laser power adjustment module, adjusting the voltage drive, and calibrating the quantum modulator frequency.

[0118] The entire regulation process operates in a closed loop. That is, after each round of regulation, the system re-measures fidelity and performs synchronous monitoring, and determines whether to continue fine-tuning or enter a stable state based on feedback. This closed-loop mechanism can effectively address quantum state fluctuations caused by environmental disturbances in urban networks.

[0119] In quantum communication networks, especially in urban trunk environments involving multi-hop relays, dynamic path reconstruction, and concurrent transmission of multiple tasks, ensuring that qubits are accurately transmitted and received within the correct time window is a key aspect of guaranteeing the registration accuracy of quantum entangled states and the reliability of communication.

[0120] To address this, the present invention proposes a synchronization instruction scheduling module, which implements multi-level synchronization control operations on each quantum communication node in the path according to the synchronization control signal output by the entangled state registration and control module, and further dynamically adjusts the width and rhythm of the quantum bit transmission window according to the actual network load and task priority.

[0121] This module first receives a structured synchronization control signal generated by the entangled state registration and control module. This signal contains specific control parameters for each key node on the path, including but not limited to:

[0122] Window offset compensation: This indicates the nanosecond-level adjustment that the node should make relative to the reference time base in terms of transmission or reception time;

[0123] Rhythm adjustment factor: Represents the compression or expansion ratio of the quantum bit emission rhythm (i.e., period) between nodes;

[0124] Registration status flag: indicates whether the target entanglement fidelity has been achieved and whether further synchronization correction is required.

[0125] The system performs semantic parsing and parameter extraction on the signal, and converts it into a standard synchronization instruction set recognizable by the local controller according to a predetermined communication protocol. For example, for the window offset compensation amount, the system maps it to an adjustment instruction for controlling the delay line of the laser pulse module; for the rhythm adjustment factor, it corresponds to an instruction for changing the trigger frequency of the laser source.

[0126] All instructions are uniformly timestamped and packaged into a relay-distributable format to ensure time consistency when multiple nodes on the path execute control instructions synchronously.

[0127] The system will send the generated synchronization control commands to the local controllers of each relay node in the optimal path. The specific operations include the following three categories:

[0128] Oscillator frequency adjustment: used to fine-tune the local reference clock frequency and reduce the drift error between the node time base and the global reference;

[0129] Emission timing correction: Adjust the start time of the photon emission trigger pulse to ensure that the entangled state is delivered to the next node within the correct time window;

[0130] Receive window delay configuration: By adjusting the trigger threshold or time window switch point of the receive module, it can be precisely matched with the transmission rhythm to achieve high-precision quantum state capture.

[0131] Upon receiving a command, the local controller executes the corresponding operations sequentially according to the preset execution priority and response latency tolerance threshold. If, during execution, it is detected that the hardware cannot fully respond to the target parameters (such as limited frequency modulation range), the system will send a response capability report back to the upper-level module, which will then perform downgrading adjustment or rhythm delay.

[0132] To further improve the system's flexibility and efficiency, the synchronous instruction scheduling module integrates a dynamic window adjustment mechanism based on communication task priority and network load, which is used to achieve real-time control of the qubit transmission window width and rhythm interval.

[0133] The mechanism includes the following three core steps:

[0134] Construct a time resource scheduling factor vector:

[0135] The system continuously collects multiple indicators reflecting the operational load status of the network, mainly including:

[0136] Node queuing depth: Represents the number of quantum tasks currently pending;

[0137] Entangled link occupancy rate: reflects whether link resources are saturated;

[0138] Task scheduling delay: refers to the actual delay time from scheduling to completion of a previous task;

[0139] Communication task priority labels: for example, set emergency key distribution tasks to high priority and non-real-time detection tasks to low priority.

[0140] These parameters are normalized and then merged into a multi-dimensional vector called the time resource scheduling factor vector, which is used as the input feature for subsequent scheduling algorithms.

[0141] Configuration parameters for the hierarchical weighted scheduling algorithm calculation window:

[0142] Based on the aforementioned scheduling factor vector, the system introduces a hierarchical weighted scheduling algorithm to intelligently configure the transmission rhythm on different paths.

[0143] The algorithm logic is as follows:

[0144] Each task is assigned a scheduling weight, which is calculated based on its priority, load urgency, and historical latency sensitivity.

[0145] High-weighted tasks correspond to larger transmission time windows (i.e., longer available time for each batch of sub-bits) and shorter rhythm intervals (i.e., faster transmission frequency).

[0146] Low-weight tasks are handled with a slowed-down pace and compressed window strategy to reduce their interference with the resources of high-weight tasks.

[0147] For example, if a high-priority task has a weight of 0.9, the system can shorten its standard launch interval (e.g., 200 nanoseconds) to 150 nanoseconds and expand the window width from the standard 100 nanoseconds to 140 nanoseconds.

[0148] Synchronize to the node controller and dynamically provide feedback for correction:

[0149] Once the scheduling result is calculated, it is encapsulated as a window configuration parameter instruction and synchronously sent to the relevant nodes in the path. Upon receiving the instruction, each node immediately updates its local time scheduling table, adjusting the window length and frequency of qubit transmission and reception.

[0150] In actual operation, the node will also adaptively adjust the window parameters based on the following factors:

[0151] Local hardware responsiveness: such as the minimum response period limit of quantum modulators;

[0152] Synchronous status feedback: whether the target deviation compensation has been achieved in the previous round of regulation;

[0153] Inter-node link error rate feedback: If the adjustment of the rhythm is too fast and causes the error rate to increase, the system can revert to the previous stable configuration state.

[0154] This mechanism enables dynamic rhythm adaptive scheduling across the entire path at the time level, ensuring both the communication quality of high-priority tasks and the smooth rhythm of low-priority tasks, thus avoiding sudden system overload.

[0155] In urban trunk quantum communication networks, the characteristics of quantum link states fluctuating over time, dynamic network topology reconfiguration, and dense task concurrency make it difficult for traditional statically configured routing and synchronization strategies to consistently guarantee communication quality. To address this, the optimization module proposed in this invention archives, models, and learns from the large amount of operational data generated in historical communication tasks, constructs a communication performance evaluation model, and adaptively optimizes routing reconfiguration rules and synchronization rhythm strategies based on the current network state. This results in the construction of an intelligent quantum communication control mechanism with feedback learning and policy transfer capabilities.

[0156] After each communication task is completed, the system automatically extracts key operational data from the task process, including:

[0157] Path stability metrics include path interruption frequency, number of node switching times, and average fidelity fluctuation range of the link.

[0158] Synchronization delay statistics: such as minimum, maximum and average synchronization offset values, and the number of time base adjustments between nodes;

[0159] Quantum resource utilization: such as the amount of entangled resources consumed per unit quantum bit, and the link buffer utilization rate;

[0160] Task completion status: such as whether it was completed on time, whether the error rate is compliant, and the task type and priority labels.

[0161] The data above is indexed by task identifiers to construct structured records, which are then stored in an archived database in chronological order. Each task record is labeled with information such as "low latency and high stability," "frequent path interruptions," and "synchronization reconfiguration failures," serving as an important supervisory reference for subsequent clustering and modeling.

[0162] To extract communication behavior patterns and establish a communication performance evaluation model, the system performs feature engineering and cluster analysis on archived data, specifically including the following key steps:

[0163] Several representative performance metrics are extracted from historical archived data as feature dimensions for modeling, including but not limited to:

[0164] Path interruption frequency: refers to the average number of times a path becomes unavailable during task communication, reflecting network robustness;

[0165] Average synchronization offset: The average clock deviation between nodes, reflecting the effectiveness of the synchronization strategy;

[0166] Entanglement resource consumption per unit bit: reflects communication efficiency, and is calculated by dividing the total entanglement resources consumed by the task by the number of successfully transmitted qubits;

[0167] Task completion latency: The total time from task scheduling to task completion, measuring transmission delay.

[0168] The system normalizes the above indicators to form a communication performance feature matrix, with each row representing a task sample and each column representing a feature dimension.

[0169] The system employs a density-based spatial clustering algorithm (DBSCAN) to analyze the feature matrix. This algorithm can effectively identify task clusters with different "densities" and distinguish typical communication behavior patterns, such as:

[0170] A "high-quality task mode" characterized by high stability, low latency, and high resource efficiency;

[0171] "Vulnerable path mode" characterized by frequent interruptions and synchronization reconfigurations;

[0172] An "inefficient task mode" that consumes high resources but has a low success rate.

[0173] After clustering, the system performs aggregated statistical and visual analysis on various task behavior samples to form multiple "communication behavior templates," providing interpretable references for subsequent model training and policy transfer.

[0174] Based on clustering labels, the system trains a communication performance evaluation model. Its input is the feature vector of the task currently running, and its output is the behavior category or performance score of the corresponding task (such as "stability score" or "resource efficiency score").

[0175] The model can be trained using algorithms such as random forest, support vector machine (SVM) or multilayer perceptron (MLP), and the selection criteria include model accuracy, interpretability and online inference speed.

[0176] During training, the system evaluates the important feature dimensions of the model and extracts the key influencing factors most closely related to path stability and latency, including:

[0177] Link fidelity volatility: also known as fidelity standard deviation, reflects the stability of the link's state during communication.

[0178] Synchronization rhythm error range: This represents the maximum time offset between the receive and transmit windows, reflecting the accuracy of the synchronization mechanism;

[0179] Node processing lag threshold: measures the response delay of a node in performing adjustment operations after receiving control commands, affecting synchronization timeliness.

[0180] These key factors will be used in the next step for dynamic tuning of routing and synchronization strategies.

[0181] When a new communication task is about to be scheduled or is in the running phase, the system can extract the current network status features (such as link status snapshots, node queue lengths, task parameters, etc.) in real time, construct a task feature vector, and input it into the communication performance evaluation model.

[0182] The system determines which type of communication behavior pattern the task is more inclined to based on the model output. If it belongs to a high-risk pattern, it automatically retrieves the "high-quality path template" or "efficient synchronization strategy" with the highest matching degree in the history, and parameterizes the following two core strategies:

[0183] Quantum routing reconstruction rules: Adjust the edge weight allocation in the path scoring function, such as increasing the weight of the link stability factor and weakening the priority of hop count;

[0184] Synchronization rhythm strategy: Adjust parameters such as the maximum compression ratio of the transmission window and the lower limit of the rhythm interval in the rhythm scheduling algorithm to enhance the node coordination capability.

[0185] The strategy migration process has online learning capabilities, and can continue to adjust the model weights based on the results of new task execution, thereby enhancing the model's long-term evolution and optimization capabilities.

[0186] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A reconfigurable quantum communication routing synchronization system for urban trunk networks, characterized in that: include: The quantum channel state sensing module is used to acquire the decoherence time, entanglement fidelity and channel interference index of each quantum link in the urban trunk network in real time, and to construct a channel state feature matrix. The clock offset prediction and registration module is used to construct a node synchronization offset estimation model based on the historical delay sequence between nodes and the bit error rate of qubits, and output the clock offset prediction curve. A reconfigurable routing planning engine is used to generate a quantum link feasibility scoring map based on the channel state feature matrix and clock offset prediction curve using a graph neural network algorithm, and output the optimal entangled forwarding path. The entanglement registration and control module is used to perform entanglement strength control operations between key nodes in the optimal entanglement forwarding path and generate synchronization control signals. The synchronization instruction scheduling module is used to trigger the node controller to perform corresponding synchronization operations based on the control results, and to dynamically calibrate and adjust the rhythm of the quantum bit transmission window; The optimization module is used to analyze the path stability, synchronization delay, and quantum resource utilization of each communication task, and optimize the route reconstruction and synchronization strategies in subsequent tasks based on the analysis results.

2. The reconfigurable quantum communication routing synchronization system for urban trunk networks according to claim 1, characterized in that: The quantum channel state sensing module includes: Non-invasive sampling and detection of entangled photons transmitted in a quantum link were performed to extract the single-photon count change rate and interference fringe phase drift characteristics related to the link decoherence process. By integrating the synchronous clock drift logs, physical layer noise detection data, and historical bit error rate curves from edge nodes, a multi-channel neural network model is constructed to output the entanglement fidelity prediction value of each quantum link under the current topology. The predicted values ​​of decoherence time, entanglement fidelity, and link interference entropy are normalized to generate a channel state feature matrix for quantum routing decisions.

3. The reconfigurable quantum communication routing synchronization system for urban trunk networks according to claim 1, characterized in that: The clock offset prediction and registration module includes: Historical time delay sequences between adjacent nodes in an urban trunk quantum communication network are collected. Cross-node time offset logs are constructed based on quantum state reception timestamps and reference clock signals, and drift trends and abrupt change characteristics are extracted from them. By combining the bit error rate variation curves of qubit pairs under different load cycles, a matrix of correlation factors between delay drift and bit error rate is constructed, and a synchronization offset estimation function is established using a weighted multivariate regression model. Based on the output offset prediction value of the synchronization offset estimation function, clock offset prediction curves for node pairs in future communication cycles are generated.

4. The reconfigurable quantum communication routing synchronization system for urban trunk networks according to claim 3, characterized in that: The reconfigurable route planning engine includes: By mapping the channel state feature matrix to the clock offset prediction curve, a hetero-weighted graph model is constructed with nodes representing quantum communication devices and edges representing quantum links. The edge weights simultaneously encode entanglement fidelity, decoherence time, and synchronization offset risk indicators. The heterogeneous weighted graph model is trained and its features are aggregated based on the graph neural network algorithm. The global feasibility score of the link is learned by utilizing the information propagation mechanism between nodes, and the quantum link feasibility score map is output. Based on the quantum link feasibility scoring graph, a reconfigurable path search algorithm is executed. Combining the task requirements of the target node pair, real-time load, and entangled link cache status, the optimal entangled forwarding path is dynamically output.

5. The reconfigurable quantum communication routing synchronization system for urban trunk networks according to claim 4, characterized in that: The entangled state registration and control module includes: Entanglement strength baseline measurements are performed on key relay node pairs in the optimal entanglement forwarding path. Based on the deviation between the measured entanglement fidelity and the expected transmission standard, the quantum state registration offset is calculated. Based on the quantum state registration offset and clock synchronization state, the photon entanglement pump power and quantum state preparation rate between relay nodes are dynamically adjusted; A synchronization control signal containing entanglement strength adjustment parameters, synchronization window offset compensation amount, and registration completion status is generated and distributed to each node in the path, triggering the local controller to execute the corresponding synchronization adjustment command.

6. The reconfigurable quantum communication routing synchronization system for urban trunk networks according to claim 5, characterized in that: The synchronization instruction scheduling module includes: The synchronization control signal is analyzed, the window offset compensation amount, rhythm adjustment factor and registration status flag of each target node are extracted, and mapped into a standardized synchronization control instruction set. The corresponding synchronization control command is issued to the controller of each relay node in the path to trigger the local oscillator frequency adjustment, transmission timing correction and reception window delay configuration of the node; The width of the quantum bit transmission window and the rhythm interval are dynamically adjusted based on the priority of the communication task and the current network load.

7. The reconfigurable quantum communication routing synchronization system for urban trunk networks according to claim 6, characterized in that: Among them dynamic Adjusting the width of the quantum bit transmission window and the rhythm interval, including: Collect the node queuing depth, entangled link occupancy rate and task scheduling delay parameters in the current quantum communication network, and construct a time resource scheduling factor vector by combining the priority labels of each communication task; Based on the time resource scheduling factor vector, the quantum bit transmission window width and rhythm interval of each path are dynamically calculated using a hierarchical weighted scheduling algorithm; The calculation results are synchronized to the local controllers of each node in the path, and the window configuration parameters are adjusted in real time based on the node's responsiveness and synchronization status.

8. The reconfigurable quantum communication routing synchronization system for urban trunk networks according to claim 7, characterized in that: The optimization module includes: Record path stability indicators, synchronization delay statistics, and quantum resource occupancy data during each communication task execution, and archive and store them in a structured and tagged manner according to task identifiers; Feature clustering is performed on archived data to construct a communication performance evaluation model and extract key influencing factors, including link fidelity volatility, synchronization rhythm error range and node processing lag threshold. Based on the aforementioned communication performance evaluation model, and considering the current network status and task requirements, the quantum routing reconstruction rules and synchronization rhythm strategy are dynamically adjusted.

9. The reconfigurable quantum communication routing synchronization system for urban trunk networks according to claim 8, characterized in that: The process involves clustering archived data to construct a communication performance evaluation model. This includes selecting multi-dimensional indicators for historical archived communication task data, such as path interruption frequency, average synchronization offset, unit bit entanglement resource consumption, and task completion delay, to construct a communication performance feature matrix. Based on the communication performance feature matrix, a density clustering algorithm is used to classify task samples, identify different communication behavior patterns, and train the communication performance evaluation model based on these patterns.

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