Quantum Machine Learning for Optimizing Entanglement Distribution in Quantum Sensor Circuits

TL;DR

This paper introduces a quantum machine learning approach using reinforcement learning to optimize entanglement distribution in quantum sensor circuits, significantly improving sensitivity and efficiency in realistic noisy environments.

Contribution

It presents a novel reinforcement learning method for optimizing entanglement in quantum sensors, integrating noise models and error mitigation for practical quantum circuit enhancement.

Findings

Enhanced quantum sensor sensitivity with high QFI and entropy.

Circuit depth and gate count reduced by up to 86%.

Demonstrated effectiveness in realistic noisy quantum environments.

Abstract

In the rapidly evolving field of quantum computing, optimizing quantum circuits for specific tasks is crucial for enhancing performance and efficiency. More recently, quantum sensing has become a distinct and rapidly growing branch of research within the area of quantum science and technology. The field is expected to provide new opportunities, especially regarding high sensitivity and precision. Entanglement is one of the key factors in achieving high sensitivity and measurement precision [3]. This paper presents a novel approach utilizing quantum machine learning techniques to optimize entanglement distribution in quantum sensor circuits. By leveraging reinforcement learning within a quantum environment, we aim to optimize the entanglement layout to maximize Quantum Fisher Information (QFI) and entanglement entropy, which are key indicators of a quantum system's sensitivity and coherence, while minimizing circuit depth and gate counts. Our implementation, based on Qiskit, integrates noise models and error mitigation strategies to simulate realistic quantum environments. The results demonstrate significant improvements in circuit performance and sensitivity, highlighting the potential of machine learning in quantum circuit optimization by measuring high QFI and entropy in the range of 0.84-1.0 with depth and gate count reduction by 20-86%.