Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning

TL;DR

This paper presents a hybrid discrete-continuous optimization approach using deep reinforcement learning to efficiently compile and reduce quantum circuits in trapped-ion systems, improving their practicality.

Contribution

It introduces a novel hybrid optimization framework combining gradient-based and reinforcement learning methods for quantum circuit compilation.

Findings

Significantly reduces quantum circuit size for trapped-ion systems

Efficiently simulates collective gates on classical hardware

Applicable to reproducing unknown unitary processes

Abstract

Shortening quantum circuits is crucial to reducing the destructive effect of environmental decoherence and enabling useful algorithms. Here, we demonstrate an improvement in such compilation tasks via a combination of using hybrid discrete-continuous optimization across a continuous gate set, and architecture-tailored implementation. The continuous parameters are discovered with a gradient-based optimization algorithm, while in tandem the optimal gate orderings are learned via a deep reinforcement learning algorithm, based on projective simulation. To test this approach, we introduce a framework to simulate collective gates in trapped-ion systems efficiently on a classical device. The algorithm proves able to significantly reduce the size of relevant quantum circuits for trapped-ion computing. Furthermore, we show that our framework can also be applied to an experimental setup whose goal is to reproduce an unknown unitary process.