LSTM Network for Quantum Key Distribution Phase Stability
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Solution Overview
Problem
Existing quantum cryptography systems face inefficiencies due to phase drift issues, requiring frequent calibration via methods like interference fringe scanning, which disrupts signal transmission and increases hardware complexity.
Innovation Solution
An active feedback control method using a pre-trained double-layer LSTM network predicts zero-phase voltage values based on real-time ambient temperature, humidity, and laser light intensity, updating at fixed intervals to maintain phase stability in quantum key distribution systems without additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If interference fringe scanning-transmission method is used for phase compensation, then phase stability is improved, but transmission efficiency deteriorates due to system interruption during calibration
Solution Approach 1:
The LSTM network is pre-trained offline using historical data containing temperature, humidity, laser intensity, and voltage information. This preliminary training enables the system to predict zero-phase voltage values in real-time without requiring interruptive calibration procedures, thus maintaining both phase stability and transmission efficiency
Solution Approach 2:
The patent replaces the traditional mechanical/optical interference fringe scanning method with a machine learning-based prediction system. The LSTM network processes environmental parameters and historical voltage data to directly predict compensation voltages, eliminating the need for real-time optical scanning and transmission interruption
2Productivity
If FPGA-based real-time phase compensation technology is used, then transmission efficiency is improved, but device complexity and hardware overhead increase
Solution Approach 1:
The patent substitutes complex FPGA-based real-time phase compensation hardware with a software-based machine learning model. The LSTM network, trained offline on environmental and operational data, provides real-time voltage predictions using standard computing resources, dramatically reducing hardware overhead while maintaining transmission efficiency
Solution Approach 2:
The system uses readily available environmental sensors (temperature, humidity, laser intensity) and operational data to train and operate the LSTM network. This self-service approach eliminates the need for specialized FPGA hardware by leveraging existing system components and data sources
Data Source
AI summary
An active feedback control method for a quantum communication system based on machine learning is disclosed. In the transmission process of a quantum key distribution system, the present invention uses a pre-trained double-layer LSTM network to predict, according to a real-time ambient temperature, humidity and laser light intensity fluctuation, as well as voltage changes in the past moment, a zero-phase voltage value of a phase modulator at a receiving end at the next moment, and updates the network at a fixed time interval, so that the LSTM network can accurately predict for a long time, ensuring that the quantum key distribution system operates stably and efficiently for a long time. The present invention greatly improves the transmission efficiency of the quantum key distribution system by method of active prediction and feedback control. The present invention is not limited to being applied to quantum key distribution systems or phase encoding systems, and also applicable to quantum key distribution systems or quantum communication networks based on other encoding methods.