GPA: Grover Policy Agent for Generating Optimal Quantum Sensor Circuits

TL;DR

This paper introduces GPA, a quantum policy agent that efficiently generates optimal quantum sensor circuits with high quantum Fisher information using Grover search techniques.

Contribution

The paper presents a novel quantum policy agent combining phase estimation and Grover search to optimize quantum sensor circuits, improving efficiency and scalability.

Findings

GPA generates QSCs with higher QFI than existing methods.

GPA uses fewer gates to achieve optimal sensitivity.

Evaluation on a two-qubit system shows improved performance.

Abstract

This study proposes a GPA for designing optimal Quantum Sensor Circuits (QSCs) to address complex quantum physics problems. The GPA consists of two parts: the Quantum Policy Evaluation (QPE) and the Quantum Policy Improvement (QPI). The QPE performs phase estimation to generate the search space, while the QPI utilizes Grover search and amplitude amplification techniques to efficiently identify an optimal policy that generates optimal QSCs. The GPA generates QSCs by selecting sequences of gates that maximize the Quantum Fisher Information (QFI) while minimizing the number of gates. The QSCs generated by the GPA are capable of producing entangled quantum states, specifically the squeezed states. High QFI indicates increased sensitivity to parameter changes, making the circuit useful for quantum state estimation and control tasks. Evaluation of the GPA on a QSC that consists of two qubits and a sequence of R_x, R_y, and S gates demonstrates its efficiency in generating optimal QSCs with a QFI of 1. Compared to existing quantum agents, the GPA achieves higher QFI with fewer gates, demonstrating a more efficient and scalable approach to the design of QSCs. This work illustrates the potential computational power of quantum agents for solving quantum physics problems