Quantum Receiver Enhanced by Adaptive Learning

TL;DR

This paper introduces QREAL, an adaptive learning-based quantum receiver architecture that dynamically adjusts to environmental conditions, significantly improving discrimination accuracy of coherent states beyond standard quantum limits.

Contribution

The paper presents a novel adaptive learning framework for quantum receivers, enhancing their robustness and performance in diverse operational environments.

Findings

Error rate reduced by up to 40% over the standard quantum limit

Experimental implementation achieved record-high efficiency

Effective adaptation to environmental variations demonstrated

Abstract

Quantum receivers aim to effectively navigate the vast quantum-state space to endow quantum information processing capabilities unmatched by classical receivers. To date, only a handful of quantum receivers have been constructed to tackle the problem of discriminating coherent states. Quantum receivers designed by analytical approaches, however, are incapable of effectively adapting to diverse environment conditions, resulting in their quickly diminishing performance as the operational complexities increase. Here, we present a general architecture, dubbed the quantum receiver enhanced by adaptive learning (QREAL), to adapt quantum receiver structures to diverse operational conditions. QREAL is experimentally implemented in a hardware platform with record-high efficiency. Combining the QREAL architecture and the experimental advances, the error rate is reduced up to 40% over the standard quantum limit in two coherent-state encoding schemes.