Quantum circuits as a game: A reinforcement learning agent for quantum compilation and its application to reconfigurable neutral atom arrays

TL;DR

This paper presents a reinforcement learning agent called QC-Daemon that optimizes quantum circuit compilation for reconfigurable neutral atom arrays, achieving significant fidelity improvements and demonstrating transferability to unseen circuits.

Contribution

Introduction of QC-Daemon, a reinforcement learning-based quantum circuit compiler that improves atom layout and generalizes across different circuits.

Findings

Reduced logarithmic infidelity on benchmark problems up to 100 qubits

Transformer-based QC-Daemon successfully generalizes to unseen circuits

Demonstrated transferability of the learned strategy

Abstract

We introduce the "quantum circuit daemon" (QC-Daemon), a reinforcement learning agent for compiling quantum device operations aimed at efficient quantum hardware execution. We apply QC-Daemon to the move synthesis problem called the Atom Game, which involves orchestrating parallel circuits on reconfigurable neutral atom arrays. In our numerical simulation, the QC-Daemon is implemented by two different types of transformers with a physically motivated architecture and trained by a reinforcement learning algorithm. We observe a reduction of the logarithmic infidelity for various benchmark problems up to 100 qubits by intelligently changing the layout of atoms. Additionally, we demonstrate the transferability of our approach: a Transformer-based QC-Daemon trained on a diverse set of circuits successfully generalizes its learned strategy to previously unseen circuits.