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:2412.02334 · Advanced Quantum Technologies

Reinforcement Learning to Learn Quantum States for Heisenberg Scaling Accuracy

Jeongwoo Jae, Jhh-Jing Hong, Jinho Choo, Yeong‐Dae Kwon

Published Aug 4, 2025• 2 citations
arXivHTMLPDF

Abstract

Abstract Learning quantum states is a crucial task for realizing quantum information technology. Recently, neural approaches have emerged as promising methods for learning quantum states. This work proposes a meta‐learning model that utilizes reinforcement learning (RL) to optimize the process of learning quantum states. To enhance the data efficiency of the RL, an action repetition strategy inspired by curriculum learning is introduced. The RL agent significantly improves the sample efficiency of learning random quantum states, and achieves infidelity scaling close to the Heisenberg limit. Also, the RL agent trained on three‐qubit states demonstrates generalization capabilities to learning up to five‐qubit states. These results highlight the utility of RL‐driven meta‐learning to enhance the efficiency and generalizability of learning quantum states. This approach can be applied to improve quantum control, quantum optimization, and quantum machine learning.

DOI
10.1002/qute.202500206

Related papers

Quantum-Inspired Algorithms for Reinforcement Learning Policy Optimization

Jincheng Zhang

This paper investigates the potential of quantum-inspired algorithms to improve reinforcement learning (RL) policy optimization. Traditional RL methods often struggle with slow convergence and exploration challenges, particularly in complex environments. We propose a novel approach that draws inspiration from quantum mechanics, specifically superposition and entanglement, to address these limitations. Our algorithms aim to exploit superposition to simultaneously evaluate multiple action possibilities and utilize entanglement to model correlations between states, thereby accelerating the learning process. We present a framework for incorporating these concepts into RL, focusing on algorithmic design rather than a direct quantum simulation. The theoretical analysis suggests that our approach could significantly enhance exploration efficiency and, consequently, policy convergence rates. This work represents a direct application of quantum-inspired ideas to RL policy optimization, offering a potentially transformative pathway for developing more efficient and robust RL agents.

•Sep 7, 2026•0 citations

Reinforcement Learning Quantum Local Search

Chen-Yu Liu, Hsi‐Sheng Goan

Quantum Local Search (QLS) is a promising approach that employs small-scale quantum computers to tackle large combinatorial optimization problems through local search on quantum hardware. However, the random selection of the sub-problem to solve in QLS may not be efficient. In this study, we propose a reinforcement learning (RL) based approach for training an agent to improve sub-problem selection in QLS beyond random selection. Our results demonstrate that the RL agent effectively enhances the average approximation ratio of QLS on fully-connected random Ising problems, indicating the potential of combining RL techniques with Noisy Intermediate-scale Quantum (NISQ) algorithms. This research opens a promising direction for integrating RL into quantum computing to enhance the performance of optimization tasks.

arXiv:2304.06473•IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 17, 2023•11 citations

Quantum-Inspired Reinforcement Learning for Quantum Control

Haixu Yu, Xudong Zhao, Chunlin Chen

Reinforcement learning (RL) is considered a powerful technology with the potential to revolutionize quantum control. However, the application effectiveness of traditional RL is often limited by some insurmountable experimental conditions. Thus, developing new RL algorithms that can efficiently manipulate the quantum system dynamics is a crucial task. Prior research has shown that incorporating quantum mechanical properties into RL can improve learning performance. In this article, we consider the quantum control problem where only the target state can be accurately identified and introduce a quantum-inspired RL (QiRL) method. In particular, we propose a quantum-inspired exploration strategy to replace a commonly used$\epsilon $-greedy strategy, as well as a quantum-inspired reward scheme to incentivize the learning agent. Numerical results on three quantum system control problems, i.e., one-qubit closed quantum system, two-level open quantum system, and many-qubit closed quantum system, verify the effectiveness of QiRL. Comparison results show that the proposed QiRL outperforms existing RL algorithms (deep Q-network and proximal policy optimization) in terms of stability and efficiency for solving quantum control problems.

IEEE Transactions on Control Systems Technology•Aug 14, 2024•11 citations

Reinforcement-learning-assisted quantum optimization

Matteo M. Wauters, Emanuele Panizon, Glen Bigan Mbeng +1 more

We propose a reinforcement learning (RL) scheme for feedback quantum control within the quantum approximate optimization algorithm (QAOA).We reformulate the QAOA variational minimization as a learning task, where an RL agent chooses the control parameters for the unitaries, given partial information on the system.Such an RL scheme finds a policy converging to the optimal adiabatic solution of the quantum Ising chain that can also be successfully transferred between systems with different sizes, even in the presence of disorder.This allows for immediate experimental verification of our proposal on more complicated models: the RL agent is trained on a small control system, simulated on classical hardware, and then tested on a larger physical sample.

arXiv:2004.12323•Physical Review Research•Sep 18, 2020•80 citations

Self-correcting quantum many-body control using reinforcement learning with tensor networks

Friederike Metz, Marin Bukov

Abstract Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems and, consequently, to devise reliable and robust optimal control protocols. Here we present a framework for efficiently controlling quantum many-body systems based on reinforcement learning (RL). We tackle the quantum-control problem by leveraging matrix product states (1) for representing the many-body state and (2) as part of the trainable machine learning architecture for our RL agent. The framework is applied to prepare ground states of the quantum Ising chain, including states in the critical region. It allows us to control systems far larger than neural-network-only architectures permit, while retaining the advantages of deep learning algorithms, such as generalizability and trainable robustness to noise. In particular, we demonstrate that RL agents are capable of finding universal controls, of learning how to optimally steer previously unseen many-body states and of adapting control protocols on the fly when the quantum dynamics is subject to stochastic perturbations. Furthermore, we map our RL framework to a hybrid quantum–classical algorithm that can be performed on noisy intermediate-scale quantum devices and test it under the presence of experimentally relevant sources of noise.

arXiv:2201.11790•Nature Machine Intelligence•Jul 21, 2023•45 citations

A Generic and Robust Agent to Accomplish Quantum Control Tasks

S Zhang, Zibo Miao, Qixiang Zhang +1 more

In view of inevitable uncertainties or disturbances in quantum systems, efficient and systematic numerical methods for robust control design are of significant importance. Although reinforcement learning has recently been applied in the field of quantum control, completing quantum control tasks in practice is always challenging due to the lack of data from real-world environments. In this paper, a generic and robust agent can be constructed catering to system imperfections by applying a curriculum meta-reinforcement learning quantum control (CMQC) approach. The proposed approach incorporates an objective module that can break down a complex quantum control objective into multiple sub-objectives using an exponential decay strategy, enabling the agent to gradually learn to solve the final goal through experience transfer and knowledge consolidation. Furthermore, a meta module is employed to achieve adaptation to environmental imperfections. This allows the agent to adjust its strategy in real time, obtaining strong robustness even in the presence of uncertainties. The superiority of CMQC is demonstrated in robust control problems regarding state transfer in a two-level quantum system and realization of the$R_{x}(\pi)$gate, with reduced computational cost and improved accuracy achieved. Our results pave the way towards more reliable and secure quantum information processing with uncertainties involved.

IEEE Transactions on Emerging Topics in Computational Intelligence•Feb 12, 2026•0 citations
Abstract Learning quantum states is a crucial task for realizing quantum information technology. Recently, neural approaches have emerged as promising methods for learning quantum states. This work proposes a meta‐learning model that utilizes reinforcement learning (RL) to optimize the process of learning quantum states. To enhance the data efficiency of the RL, an action repetition strategy inspired by curriculum learning is introduced. The RL agent significantly improves the sample efficiency of learning random quantum states, and achieves infidelity scaling close to the Heisenberg limit. Also, the RL agent trained on three‐qubit states demonstrates generalization capabilities to learning up to five‐qubit states. These results highlight the utility of RL‐driven meta‐learning to enhance the efficiency and generalizability of learning quantum states. This approach can be applied to improve quantum control, quantum optimization, and quantum machine learning.
This paper investigates the potential of quantum-inspired algorithms to improve reinforcement learning (RL) policy optimization. Traditional RL methods often struggle with slow convergence and exploration challenges, particularly in complex environments. We propose a novel approach that draws inspiration from quantum mechanics, specifically superposition and entanglement, to address these limitations. Our algorithms aim to exploit superposition to simultaneously evaluate multiple action possibilities and utilize entanglement to model correlations between states, thereby accelerating the learning process. We present a framework for incorporating these concepts into RL, focusing on algorithmic design rather than a direct quantum simulation. The theoretical analysis suggests that our approach could significantly enhance exploration efficiency and, consequently, policy convergence rates. This work represents a direct application of quantum-inspired ideas to RL policy optimization, offering a potentially transformative pathway for developing more efficient and robust RL agents.
Quantum Local Search (QLS) is a promising approach that employs small-scale quantum computers to tackle large combinatorial optimization problems through local search on quantum hardware. However, the random selection of the sub-problem to solve in QLS may not be efficient. In this study, we propose a reinforcement learning (RL) based approach for training an agent to improve sub-problem selection in QLS beyond random selection. Our results demonstrate that the RL agent effectively enhances the average approximation ratio of QLS on fully-connected random Ising problems, indicating the potential of combining RL techniques with Noisy Intermediate-scale Quantum (NISQ) algorithms. This research opens a promising direction for integrating RL into quantum computing to enhance the performance of optimization tasks.
Reinforcement learning (RL) is considered a powerful technology with the potential to revolutionize quantum control. However, the application effectiveness of traditional RL is often limited by some insurmountable experimental conditions. Thus, developing new RL algorithms that can efficiently manipulate the quantum system dynamics is a crucial task. Prior research has shown that incorporating quantum mechanical properties into RL can improve learning performance. In this article, we consider the quantum control problem where only the target state can be accurately identified and introduce a quantum-inspired RL (QiRL) method. In particular, we propose a quantum-inspired exploration strategy to replace a commonly used$\epsilon $-greedy strategy, as well as a quantum-inspired reward scheme to incentivize the learning agent. Numerical results on three quantum system control problems, i.e., one-qubit closed quantum system, two-level open quantum system, and many-qubit closed quantum system, verify the effectiveness of QiRL. Comparison results show that the proposed QiRL outperforms existing RL algorithms (deep Q-network and proximal policy optimization) in terms of stability and efficiency for solving quantum control problems.
We propose a reinforcement learning (RL) scheme for feedback quantum control within the quantum approximate optimization algorithm (QAOA).We reformulate the QAOA variational minimization as a learning task, where an RL agent chooses the control parameters for the unitaries, given partial information on the system.Such an RL scheme finds a policy converging to the optimal adiabatic solution of the quantum Ising chain that can also be successfully transferred between systems with different sizes, even in the presence of disorder.This allows for immediate experimental verification of our proposal on more complicated models: the RL agent is trained on a small control system, simulated on classical hardware, and then tested on a larger physical sample.
Abstract Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems and, consequently, to devise reliable and robust optimal control protocols. Here we present a framework for efficiently controlling quantum many-body systems based on reinforcement learning (RL). We tackle the quantum-control problem by leveraging matrix product states (1) for representing the many-body state and (2) as part of the trainable machine learning architecture for our RL agent. The framework is applied to prepare ground states of the quantum Ising chain, including states in the critical region. It allows us to control systems far larger than neural-network-only architectures permit, while retaining the advantages of deep learning algorithms, such as generalizability and trainable robustness to noise. In particular, we demonstrate that RL agents are capable of finding universal controls, of learning how to optimally steer previously unseen many-body states and of adapting control protocols on the fly when the quantum dynamics is subject to stochastic perturbations. Furthermore, we map our RL framework to a hybrid quantum–classical algorithm that can be performed on noisy intermediate-scale quantum devices and test it under the presence of experimentally relevant sources of noise.
In view of inevitable uncertainties or disturbances in quantum systems, efficient and systematic numerical methods for robust control design are of significant importance. Although reinforcement learning has recently been applied in the field of quantum control, completing quantum control tasks in practice is always challenging due to the lack of data from real-world environments. In this paper, a generic and robust agent can be constructed catering to system imperfections by applying a curriculum meta-reinforcement learning quantum control (CMQC) approach. The proposed approach incorporates an objective module that can break down a complex quantum control objective into multiple sub-objectives using an exponential decay strategy, enabling the agent to gradually learn to solve the final goal through experience transfer and knowledge consolidation. Furthermore, a meta module is employed to achieve adaptation to environmental imperfections. This allows the agent to adjust its strategy in real time, obtaining strong robustness even in the presence of uncertainties. The superiority of CMQC is demonstrated in robust control problems regarding state transfer in a two-level quantum system and realization of theRx(π)R_{x}(\pi)Rx​(π)gate, with reduced computational cost and improved accuracy achieved. Our results pave the way towards more reliable and secure quantum information processing with uncertainties involved.