Variational Quantum Circuits for Deep Reinforcement Learning

TL;DR

This paper introduces a novel approach using variational quantum circuits to implement deep reinforcement learning algorithms, aiming for feasible deployment on near-term quantum devices and reducing model complexity.

Contribution

It presents the first proof-of-principle demonstration of variational quantum circuits approximating deep Q-values in reinforcement learning, integrating experience replay and target networks.

Findings

Quantum circuits effectively approximate deep Q-values.

Reduced parameter count compared to classical neural networks.

Feasible deployment on NISQ devices.

Abstract

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 new quantum 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.

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