Asynchronous measurement-device-independent quantum key distribution with hybrid source.

The linear constraint of secret key rate capacity is overcome by the twin-field quantum key distribution (QKD). However, the complex phase-locking and phase-tracking technique requirements throttle the real-life applications of the twin-field protocol. The asynchronous measurement-device-independent (AMDI) QKD, also called the mode-pairing QKD, protocol can relax the technical requirements and keep the similar performance of the twin-field protocol. Here, we propose an AMDI-QKD protocol with a nonclassical light source by changing the phase-randomized weak coherent state to a phase-randomized coherent-state superposition in the signal state time window. Simulation results show that our proposed hybrid source protocol significantly enhances the key rate of the AMDI-QKD protocol, while exhibiting robustness to imperfect modulation of nonclassical light sources.

Post-matching quantum conference key agreement.

Twin-field interference-based quantum conference key agreement protocols have been proposed and have achieved good performance in terms of the key rate and transmission distance in the finite-key regime. However, its performance significantly decreases when the strict constraint is broken regarding the optical pulse intensity and probability. Here, we propose a post-matching QCKA protocol to remove this constraint while obtaining a higher key rate. Numerical results in the symmetric case show that our protocol can obtain a transmission distance 25% more than the previous asymmetric QCKA protocol when the decoy state optical pulse intensity is 1% higher than the ideal value of the constraint, and can obtain a transmission distance 100% higher when the decoy state optical pulse intensity is 10% higher than the ideal value of the constraint.

Neural network-based prediction of the secret-key rate of quantum key distribution.

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.