Automated design of quantum optical experiments for device-independent quantum key distribution
arXiv:2209.06468 · doi:10.1103/PhysRevA.107.062607
Abstract
Device-independent quantum key distribution (DIQKD) reduces the vulnerability to side-channel attacks of standard QKD protocols by removing the need for characterized quantum devices. The higher security guarantees come however, at the price of a challenging implementation. Here, we tackle the question of the conception of an experiment for implementing DIQKD with photonic devices. We introduce a technique combining reinforcement learning, optimisation algorithm and a custom efficient simulation of quantum optics experiments to automate the design of photonic setups maximizing a given function of the measurement statistics. Applying the algorithm to DIQKD, we get unexpected experimental configurations leading to high key rates and to a high resistance to loss and noise. These configurations might be helpful to facilitate a first implementation of DIQKD with photonic devices and for future developments targeting improved performances.
7+15 pages, 4+3 figures
References in corpus (7)
- Deep Learning in Neural Networks: An Overview
- Secure Quantum Key Distribution
- Distillation of secret key and entanglement from quantum states
- Device-independent quantum key distribution secure against collective attacks
- Advances in device-independent quantum key distribution
- Noisy pre-processing facilitating a photonic realisation of device-independent quantum key distribution
- Challenging preconceptions about Bell tests with photon pairs
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