Variational Quantum Circuit-Based Reinforcement Learning for POMDP and Experimental Implementation

Abstract

Variational quantum circuit is proposed for applications in supervised learning and reinforcement learning to harness potential quantum advantage. However, many practical applications in robotics and time-series analysis are in partially observable environment. In this work, we propose an algorithm based on variational quantum circuits for reinforcement learning under partially observable environment. Simulations suggest learning advantage over several classical counterparts. The learned parameters are then tested on IBMQ systems to demonstrate the applicability of our approach for real-machine-based predictions.