Challenge-Response Quantum Reinforcement Learning with Application to Quantum-Assisted Authentication

TL;DR

This paper introduces a quantum reinforcement learning environment based on a challenge-response task for quantum-assisted authentication, demonstrating that hybrid agents can efficiently infer hidden quantum information with minimal resources and robustness to noise.

Contribution

It proposes a novel quantum environment for reinforcement learning focused on quantum information inference, and evaluates hybrid agents' efficiency and robustness in this setting.

Findings

Lightweight hybrid agent infers with as few as two quantum state copies.

Hybrid agents outperform classical and deep hybrid agents in resource efficiency.

Environment is relevant for quantum security and authentication applications.

Abstract

Quantum reinforcement learning (QRL) has emerged as a promising research direction that integrates quantum information processing into reinforcement learning frameworks. While many existing QRL studies apply quantum agents to classical environments, it has been realized that the potential advantages of QRL are most naturally explored in environments that exhibit intrinsically quantum characteristics, where the agent's observations and interactions arise from quantum processes. In this work, we propose a quantum reinforcement learning environment formulated as a challenge-response task with hidden information. In the proposed environment, Alice encodes a classical bit into the parameters of a quantum circuit, while Bob, with a trained reinforcement learning agent, interacts with a limited number of quantum state copies to infer the hidden bit. The agent must select measurement strategies and decide when to terminate the interaction under explicit resource constraints. To study the solvability of the proposed environment, we consider three agents: a purely classical agent, a lightweight hybrid agent and a deep hybrid agent. Through experiments, we analyze the trade-off between inference accuracy and quantum resource consumption under varying interaction penalties. Our results show that the lightweight hybrid agent achieves reliable inference using as few as two quantum state copies, outperforming both the classical baseline and the deep hybrid agent across both high and low-penalty regimes. We further evaluate robustness under realistic quantum noise models and discuss the relevance of the proposed environment for security-oriented applications, including quantum-assisted authentication.