Reinforcement Learning for Optimizing Large Qubit Array based Quantum Sensor Circuits

TL;DR

This paper introduces a reinforcement learning approach combined with tensor-network simulation to optimize large-scale quantum sensor circuits with up to 60 qubits, significantly improving entanglement and efficiency.

Contribution

It presents a scalable method integrating reinforcement learning and tensor networks for optimizing large quantum circuits, a novel approach for quantum sensor design.

Findings

QFI values approaching 1

Entanglement entropy in the 0.8-1.0 range

Up to 90% reduction in circuit depth and gate count

Abstract

As the number of qubits in a sensor increases, the complexity of designing and controlling the quantum circuits grows exponentially. Manually optimizing these circuits becomes infeasible. Optimizing entanglement distribution in large-scale quantum circuits is critical for enhancing the sensitivity and efficiency of quantum sensors [5], [6]. This paper presents an engineering integration of reinforcement learning with tensor-network-based simulation (MPS) for scalable circuit optimization for optimizing quantum sensor circuits with up to 60 qubits. To enable efficient simulation and scalability, we adopt tensor network methods, specifically the Matrix Product State (MPS) representation, instead of traditional state vector or density matrix approaches. Our reinforcement learning agent learns to restructure circuits to maximize Quantum Fisher Information (QFI) and entanglement entropy while reducing gate counts and circuit depth. Experimental results show consistent improvements, with QFI values approaching 1, entanglement entropy in the 0.8-1.0 range, and up to 90% reduction in depth and gate count. These results highlight the potential of combining quantum machine learning and tensor networks to optimize complex quantum circuits under realistic constraints.