Quantum state generation using model-based deep reinforcement learning

Abstract

In the pursuit of realizing a quantum computer through quantum gating, generating a Schrödinger’s cat state that is robust against noise and decoherence presents a significant challenge. Recent studies have explored deep reinforcement learning methods; however, the results often fail to accurately reflect the state of the quantum system due to quantum back-action during the observation process. In this study, we propose a novel approach for quantum state generation that integrates particle filters with deep reinforcement learning. The particle filter estimates the quantum system's state based on observed results and subsequently provides feedback to the reinforcement learning agent. We compare our current findings on generating Schrödinger's cat state with previous results derived from deep reinforcement learning techniques.