Machine Learning for Optimal Parameter Prediction in Quantum Key Distribution

Abstract

For a practical quantum key distribution (QKD) system, parameter optimization - the choice of intensities and probabilities of sending them - is a crucial step in gaining optimal performance, especially when one realistically considers finite communication time. With the increasing interest in the field to implement QKD over free-space on moving platforms, such as drones, handheld systems, and even satellites, one needs to perform parameter optimization with low latency and with very limited computing power. Moreover, with the advent of the Internet of Things (IoT), a highly attractive direction of QKD could be a quantum network with multiple devices and numerous connections, which provides a huge computational challenge for the controller that optimizes parameters for a large-scale network. Traditionally, such an optimization relies on brute-force search, or local search algorithms, which are computationally intensive, and will be slow on low-power platforms (which increases latency in the system) or infeasible for even moderately large networks. In this work we present a new method that uses a neural network to directly predict the optimal parameters for QKD systems. We test our machine learning algorithm on hardware devices including a Raspberry Pi 3 single-board-computer (similar devices are commonly used on drones) and a mobile phone, both of which have a power consumption of less than 5 watts, and we find a speedup of up to 100-1000 times when compared to standard local search algorithms. The predicted parameters are highly accurate and can preserve over 95-99% of the optimal secure key rate. Moreover, our approach is highly general and not limited to any specific QKD protocol.

Added benchmarking data on hardware devices; More emphasis on generality of our method (with extended numerical results for three different protocols)

References in corpus (12)

• Satellite-to-ground quantum key distribution
• Overcoming the rate-distance barrier of quantum key distribution without using quantum repeaters
• Sending or not sending: twin-field quantum key distribution with large misalignment error
• Phase-Matching Quantum Key Distribution
• Protocol choice and parameter optimization in decoy-state measurement-device-independent quantum key distribution
• Simple security analysis of phase-matching measurement-device-independent quantum key distribution
• Asymmetric Protocols for Scalable High-Rate Measurement-Device-Independent Quantum Key Distribution Networks
• Machine Learning for Optimal Parameter Prediction in Quantum Key Distribution
• General theory for decoy-state quantum key distribution with arbitrary number of intensities
• Parameters optimization and real-time calibration of Measurement-Device-Independent Quantum Key Distribution Network based on Back Propagation Artificial Neural Network
• Simple Method for Asymmetric Twin-Field Quantum Key Distribution
• Exact minimum and maximum of yield with a finite number of decoy light intensities

Cited by in corpus (11)

• Atomically-thin Single-photon Sources for Quantum Communication
• Machine Learning for Optimal Parameter Prediction in Quantum Key Distribution
• Simple Quantum Key Distribution using a Stable Transmitter-Receiver Scheme
• Automated machine learning for secure key rate in discrete-modulated continuous-variable quantum key distribution
• Neural network-based prediction of the secret-key rate of quantum key distribution
• Data-Centric Machine Learning in Quantum Information Science
• Research progress of artificial intelligence empowered quantum communication and quantum sensing systems
• Trade-off between Bagging and Boosting for quantum separability-entanglement classification
• Experimental kernel-based quantum machine learning in finite feature space
• Adaptive Techniques in Practical Quantum Key Distribution
• Optimizing Epsilon Security Parameters in QKD