Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2502.13755 · IEEE Transactions on Artificial Intelligence

GPA: Grover Policy Agent for Generating Optimal Quantum Sensor Circuits

Ahmad Alomari, Sathish Kumar

Published Apr 2, 2025• 1 citation
arXivHTMLPDF

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

DOI
10.1109/tai.2025.3556979

Related papers

QRA: Quantum Reinforcement Agent for Generating Optimal Quantum Sensor Circuits

Ahmad Alomari, Sathish Kumar

This study proposes a QRA approach for designing optimal Quantum Sensor Circuits (QSCs) to address complex quantum physics problems. The QRA 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 QRA are capable of producing entangled quantum states, specifically the squeezed states, by performing generalized Ramsey measurements on qubits. High QFI indicates increased sensitivity to parameter changes, making the circuit useful for quantum state estimation and control tasks. Evaluation of the QRA on a QSC that consists of two qubits and a sequence of$\boldsymbol{R}_{\boldsymbol{x}},\ \boldsymbol{R}_{\boldsymbol{y}}$and$\boldsymbol{S}$gates demonstrates its efficiency in generating optimal QSCs with a QFI of 1. This work illustrates the potential computational power of quantum agents for solving quantum physics problems.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 15, 2024•2 citations

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

Ahmad Alomari, Sathish Kumar

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.

arXiv:2508.21246•Aug 28, 2025•0 citations

Reinforcement Learning Based Actor Critic and Policy Agent for Optimized Quantum Sensor Circuit Design

Temitope Bolaji Adeniyi, Sathish Kumar

This paper explores the application of reinforcement learning (RL) algorithms to the domain of quantum sensor circuit design. We focus on the design of four qubits quantum circuits that can efficiently generate states with optimal sensitivity, which maximizes the Quantum Fisher Information (QFI). We formulate the design task as a Markov Decision Process, implementing and comparing two distinct RL strategies: pure policy gradient and actor-critic agents to optimize a Ramsey sequence for maximum QFI. The results demonstrate that both agents are able to find quantum circuit designs with an optimal QFI value of 1. However, the agents differ in the gate sequence of the final Quantum sensor circuit configurations that indicate the optimal state. The policy gradient outperforms the actor-critic method in convergence time and the number of gate sequences for the encoding circuit.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 15, 2024•2 citations

Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learning

Simon M. Wiedemann, Daniel M. Hein, Steffen Udluft +1 more

We present a full implementation and simulation of a novel quantum reinforcement learning method. Our work is a detailed and formal proof of concept for how quantum algorithms can be used to solve reinforcement learning problems and shows that, given access to error-free, efficient quantum realizations of the agent and environment, quantum methods can yield provable improvements over classical Monte-Carlo based methods in terms of sample complexity. Our approach shows in detail how to combine amplitude estimation and Grover search into a policy evaluation and improvement scheme. We first develop quantum policy evaluation (QPE) which is quadratically more efficient compared to an analogous classical Monte Carlo estimation and is based on a quantum mechanical realization of a finite Markov decision process (MDP). Building on QPE, we derive a quantum policy iteration that repeatedly improves an initial policy using Grover search until the optimum is reached. Finally, we present an implementation of our algorithm for a two-armed bandit MDP which we then simulate.

arXiv:2206.04741•Jun 9, 2022•10 citations

Reinforcement learning for optimizing large qubit array based quantum sensor circuits

Laxmisha Ashok Attisara, Kumar Sathish A.P.

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 (Yi, in Quantum Sci Technol, 045001, 2022, Thomas et al, in 2021). 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.

arXiv:2508.21253•Discover Applied Sciences•Jan 5, 2026•0 citations

Approximate Quantum State Preparation Through Proximal Policy Optimization

Marco Mordacci, Michele Amoretti

In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed. QSP is a challenging task, since the search space grows exponentially with the number of qubits, making the identification of the optimal circuit non-trivial. To address this problem, deep reinforcement learning is employed through an agent based on proximal policy optimization. The objective of the agent is to identify the best possible approximation of the target state while simultaneously minimizing the number of gates used. At each step, the agent appends a new gate to the circuit and recomputes the fidelity between the approximated state and the target states. Various experiments have been performed from 2 to 5 qubits. Both predefined states, such as Bell, GHZ, W, and Dicke states, and completely random states are considered. The proposed framework is able to achieve approximation errors of $10^{-14}$.

quant-pharXiv:2607.21121•Jul 24, 2026•0 citations
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
This study proposes a QRA approach for designing optimal Quantum Sensor Circuits (QSCs) to address complex quantum physics problems. The QRA 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 QRA are capable of producing entangled quantum states, specifically the squeezed states, by performing generalized Ramsey measurements on qubits. High QFI indicates increased sensitivity to parameter changes, making the circuit useful for quantum state estimation and control tasks. Evaluation of the QRA on a QSC that consists of two qubits and a sequence ofRx, Ry\boldsymbol{R}_{\boldsymbol{x}},\ \boldsymbol{R}_{\boldsymbol{y}}Rx​, Ry​andS\boldsymbol{S}Sgates demonstrates its efficiency in generating optimal QSCs with a QFI of 1. This work illustrates the potential computational power of quantum agents for solving quantum physics problems.
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.
This paper explores the application of reinforcement learning (RL) algorithms to the domain of quantum sensor circuit design. We focus on the design of four qubits quantum circuits that can efficiently generate states with optimal sensitivity, which maximizes the Quantum Fisher Information (QFI). We formulate the design task as a Markov Decision Process, implementing and comparing two distinct RL strategies: pure policy gradient and actor-critic agents to optimize a Ramsey sequence for maximum QFI. The results demonstrate that both agents are able to find quantum circuit designs with an optimal QFI value of 1. However, the agents differ in the gate sequence of the final Quantum sensor circuit configurations that indicate the optimal state. The policy gradient outperforms the actor-critic method in convergence time and the number of gate sequences for the encoding circuit.
We present a full implementation and simulation of a novel quantum reinforcement learning method. Our work is a detailed and formal proof of concept for how quantum algorithms can be used to solve reinforcement learning problems and shows that, given access to error-free, efficient quantum realizations of the agent and environment, quantum methods can yield provable improvements over classical Monte-Carlo based methods in terms of sample complexity. Our approach shows in detail how to combine amplitude estimation and Grover search into a policy evaluation and improvement scheme. We first develop quantum policy evaluation (QPE) which is quadratically more efficient compared to an analogous classical Monte Carlo estimation and is based on a quantum mechanical realization of a finite Markov decision process (MDP). Building on QPE, we derive a quantum policy iteration that repeatedly improves an initial policy using Grover search until the optimum is reached. Finally, we present an implementation of our algorithm for a two-armed bandit MDP which we then simulate.
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 (Yi, in Quantum Sci Technol, 045001, 2022, Thomas et al, in 2021). 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.
In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed. QSP is a challenging task, since the search space grows exponentially with the number of qubits, making the identification of the optimal circuit non-trivial. To address this problem, deep reinforcement learning is employed through an agent based on proximal policy optimization. The objective of the agent is to identify the best possible approximation of the target state while simultaneously minimizing the number of gates used. At each step, the agent appends a new gate to the circuit and recomputes the fidelity between the approximated state and the target states. Various experiments have been performed from 2 to 5 qubits. Both predefined states, such as Bell, GHZ, W, and Dicke states, and completely random states are considered. The proposed framework is able to achieve approximation errors of 10−1410^{-14}10−14.