ReLAQA: Reinforcement Learning-Based Autonomous Quantum Agent for Quantum Applications

Abstract

This study proposes a quantum reinforcement learning (QRL) approach for robotic applications, which incorporates a Grover-based autonomous quantum agent (GAQA) and a quantum environment represented as a quantum TicTacToe (QTTT) game. The QTTT environment is a quantum circuit of qubits in their superposition states, manipulated by the agent through quantum gates to establish a goal state. By utilizing amplitude estimation and Grover search techniques, the proposed reinforcement learning-based autonomous quantum agent (ReLAQA) enhances the probability amplitudes of the actions taken, which results in reducing the number of observed states required to reach a solution. Empirical results substantiate the quantum advantages of the proposed GAQA in reinforcement learning (RL) tasks by observing fewer states of 6300, outperforming classical agents. Therefore, signifying its potential to enhance complex problem-solving in robotics.