GPA: Grover Policy Agent for Generating Optimal Quantum Sensor Circuits
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Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 38 references that are in the map. First posted on arXiv 19 Feb 2025.
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References · 8
- 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
- 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
- 2020Quantum reinforcement learning in continuous action spacecited by 27
- 2022Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Designcited by 25
- 2022Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learningcited by 21
- 2022Quantum Reinforcement Learning via Policy Iterationcited by 19
- 2022Policy Gradients using Variational Quantum Circuitscited by 15
- 2022Quantum Deep Reinforcement Learning for Robot Navigation Taskscited by 14
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