Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations

TL;DR

This paper introduces a reinforcement learning approach using MDP modeling and deep Q-learning algorithms to automate and scale quantum circuit design in the NISQ era, addressing the challenge of mapping circuits to universal gate sets.

Contribution

It presents a novel application of deep reinforcement learning, specifically Q-learning and DQN, for automated quantum circuit design, which is more scalable than traditional heuristic methods.

Findings

Reinforcement learning effectively automates quantum circuit design.

Deep Q-learning improves scalability and performance.

The approach outperforms traditional heuristic methods.

Abstract

Quantum computing promises advantages over classical computing. The manufacturing of quantum hardware is in the infancy stage, called the Noisy Intermediate-Scale Quantum (NISQ) era. A major challenge is automated quantum circuit design that map a quantum circuit to gates in a universal gate set. In this paper, we present a generic MDP modeling and employ Q-learning and DQN algorithms for quantum circuit design. By leveraging the power of deep reinforcement learning, we aim to provide an automatic and scalable approach over traditional hand-crafted heuristic methods.