Implementation of Quantum Deep Reinforcement Learning Using Variational Quantum Circuits
S Lokes, C. Sakthi Jay Mahenthar, S Parvatha Kumaran, P. Sathyaprakash, Vaithiyashankar Jayakumar
Abstract
With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. However, even the most powerful classical supercomputers find it difficult to execute these tasks. Advancements in quantum computing are leading researchers and tech-giants who strive for better quantum circuits to do machine learning tasks. Current works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. However, it is strenuous to simulate the classical deep learning approach on existing quantum computing devices due to the inflexibility of Deep quantum circuits. Consequently, designing viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices is essential. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Q network-based Reinforcement Learning by remodeling the target network and experience replay into a representation of VQC. In addition, to the reduction in model parameters, quantum information encoding schemes are used to achieve better results than classical neural networks. VQCs are employed for policyselection and decision-making reinforcement learning by approximating the deep Q-value function with target network and experience replay.