HCQA: Hybrid Classical-Quantum Agent for Generating Optimal Quantum Sensor Circuits

TL;DR

This paper introduces HCQA, a hybrid classical-quantum agent that autonomously designs optimal quantum sensor circuits using deep reinforcement learning and quantum-based decision mechanisms to improve quantum state estimation.

Contribution

The study presents a novel hybrid agent combining deep Q-learning with quantum action selection to optimize quantum sensor circuits for enhanced sensing performance.

Findings

HCQA efficiently generates quantum circuits with high QFI.

The approach automates the design of entangled states like squeezed states.

Demonstrated effectiveness on a two-qubit quantum sensor circuit.

Abstract

This study proposes an HCQA for designing optimal Quantum Sensor Circuits (QSCs) to address complex quantum physics problems. The HCQA integrates computational intelligence techniques by leveraging a Deep Q-Network (DQN) for learning and policy optimization, enhanced by a quantum-based action selection mechanism based on the Q-values. A quantum circuit encodes the agent current state using Ry gates, and then creates a superposition of possible actions. Measurement of the circuit results in probabilistic action outcomes, allowing the agent to generate optimal QSCs by selecting sequences of gates that maximize the Quantum Fisher Information (QFI) while minimizing the number of gates. This computational intelligence-driven HCQA enables the automated generation of entangled quantum states, specifically the squeezed states, with high QFI sensitivity for quantum state estimation and control. Evaluation of the HCQA on a QSC that consists of two qubits and a sequence of Rx, Ry, and S gates demonstrates its efficiency in generating optimal QSCs with a QFI of 1. This work highlights the synergy between AI-driven learning and quantum computation, illustrating how intelligent agents can autonomously discover optimal quantum circuit designs for enhanced sensing and estimation tasks.