Neural network-based prediction of the secret-key rate of quantum key distribution
arXiv:2108.02578 · doi:10.1038/s41598-022-12647-x
Abstract
Numerical methods are widely used to calculate the secure key rate of many quantum key distribution protocols in practice, but they consume many computing resources and are too time-consuming. In this work, we take the homodyne detection discrete-modulated continuous-variable quantum key distribution (CV-QKD) as an example, and construct a neural network that can quickly predict the secure key rate based on the experimental parameters and experimental results. Compared to traditional numerical methods, the speed of the neural network is improved by several orders of magnitude. Importantly, the predicted key rates are not only highly accurate but also highly likely to be secure. This allows the secure key rate of discrete-modulated CV-QKD to be extracted in real time on a low-power platform. Furthermore, our method is versatile and can be extended to quickly calculate the complex secure key rates of various other unstructured quantum key distribution protocols.
arXiv comment12 pages, 5 figures, 2 tables
References in corpus (45)
- Advances in Quantum Cryptography
- Quantum Circuit Learning
- Secure quantum key distribution with realistic devices
- Continuous variable quantum cryptography using coherent states
- Quantum principal component analysis
- The power of quantum neural networks
- Distillation of secret key and entanglement from quantum states
- Many-body quantum state tomography with neural networks
- Efficient Learning for Deep Quantum Neural Networks
- Quantum Cryptography without Switching
- Quantum machine learning: a classical perspective
- Long-Distance Continuous-Variable Quantum Key Distribution over 202.81 km of Fiber
- Unconditional security proof of long-distance continuous-variable quantum key distribution with discrete modulation
- Breaking the Rate-Loss Bound of Quantum Key Distribution with Asynchronous Two-Photon Interference
- Quantum generalisation of feedforward neural networks
- Virtual Entanglement and Reconciliation Protocols for Quantum Cryptography with Continuous Variables
- Experimental Machine Learning of Quantum States
- Numerical approach for unstructured quantum key distribution
- Reliable numerical key rates for quantum key distribution
- Explicit asymptotic secret key rate of continuous-variable quantum key distribution with an arbitrary modulation
- Quantum autoencoders to denoise quantum data
- Security of Binary Modulated Continuous Variable Quantum Key Distribution under Collective Attacks
- An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes
- Machine learning aided carrier recovery in continuous-variable quantum key distribution
- Machine Learning for Optimal Parameter Prediction in Quantum Key Distribution
- Finite-size security of continuous-variable quantum key distribution with digital signal processing
- Numerical Calculations of Finite Key Rate for General Quantum Key Distribution Protocols
- Asymptotic security of discrete-modulation protocols for continuous-variable quantum key distribution
- Practical phase-modulation stabilization in quantum key distribution via machine learning
- Quantum enhancements for deep reinforcement learning in large spaces
- Versatile security analysis of measurement-device-independent quantum key distribution
- Dimension Reduction in Quantum Key Distribution for Continuous- and Discrete-Variable Protocols
- Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation
- Using deep learning to understand and mitigate the qubit noise environment
- Experimental Simultaneous Learning of Multiple Non-Classical Correlations
- Computing secure key rates for quantum key distribution with untrusted devices
- Trusted detector noise analysis for discrete modulation schemes of continuous-variable quantum key distribution
- Robust calibration of multiparameter sensors via machine learning at the single-photon level
- Numerical finite-key analysis of quantum key distribution
- Parameters optimization and real-time calibration of Measurement-Device-Independent Quantum Key Distribution Network based on Back Propagation Artificial Neural Network
- Experimental composable security decoy-state quantum key distribution using time-phase encoding
- Efficient Quantum State Sample Tomography with Basis-dependent Neural-networks
- Robust Interior Point Method for Quantum Key Distribution Rate Computation
- Phase self-aligned continuous-variable measurement-device-independent quantum key distribution
- Quantum Neural Network and Soft Quantum Computing