Variational Quantum Circuits for Deep Reinforcement Learning

Samuel Yen-Chi Chen

doi:10.1109/ACCESS.2020.3010470

Variational Quantum Circuits for Deep Reinforcement Learning

2020-01-01

183

OA

AI

DOI：10.1109/ACCESS.2020.3010470

Original

Save

View PDF

Abstract

En 中文

The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-giants have attempted newquantum circuits for machine learning tasks. However, the existing quantum computing platforms are hard to simulate classical deep learning models or problems because of the intractability of deep quantum circuits. Thus, it is necessary to design feasible quantum algorithms for quantum machine learning for noisy intermediate scale quantum (NISQ) devices. This work explores variational quantum circuits for deep reinforcement learning. Specifically, we reshape classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits. Moreover, we use a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks. To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and target network. Besides, our variational quantum circuits can be deployed in many near-term NISQ machines.

Keywords:

Communication network

deep reinforcement learning

quantum machine learning

quantum information processing

variational quantum circuits

noisy intermediate scale quantum

quantum computing

Variational Quantum Circuits for Deep Reinforcement Learning

Abstract

AI Summary

Journal

Organization