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IEEE Transactions on Emerging Topics in Computational Intelligence

A Generic and Robust Agent to Accomplish Quantum Control Tasks

S Zhang, Zibo Miao, Qixiang Zhang, Xin Yi

Published Feb 12, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.1109/tetci.2026.3661605

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Achieving high-fidelity quantum gates is crucial for reliable quantum computing. However, decoherence and control pulse imperfections pose significant challenges in realizing the theoretical fidelity of quantum gates in practical systems. To address these challenges, we propose the meta-reinforcement learning quantum control algorithm (metaQctrl), which leverages a two-layer learning framework to enhance robustness and fidelity. The inner reinforcement learning network focuses on decision making for specific optimization problems, while the outer meta-learning network adapts to varying environments and provides feedback to the inner network. Our comparative analysis regarding the realization of universal quantum gates demonstrates that metaQctrl achieves higher fidelity with fewer control pulses than conventional methods in the presence of uncertainties. These results can contribute to the exploration of the quantum speed limit and facilitate the implementation of quantum circuits with system imperfections involved.
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This paper presents an adaptive quantum state collapse control algorithm designed to mitigate the detrimental effects of environmental noise on quantum states. The core of the algorithm relies on a reinforcement learning (RL) framework to dynamically adjust control strategies based on real-time feedback from the quantum environment. Specifically, a controller is constructed utilizing an RL agent that interacts with the quantum system, learning an optimal control policy to minimize state collapse. A key element is the integration of quantum decoherence techniques to reduce the impact of noise. The algorithm's adaptive nature allows it to contend with fluctuating noise levels, a significant challenge in maintaining the coherence of fragile quantum states. The proposed approach offers a novel method for controlling quantum systems, overcoming the limitations of conventional quantum control methods which often rely on static control parameters. The results demonstrate the efficacy of this dynamic control strategy in preserving quantum state fidelity under varying noise conditions. This work contributes to the development of robust quantum control protocols for applications such as quantum computation and quantum sensing.
This paper investigates the optimization of quantum control algorithms using reinforcement learning (RL). The core claim is that RL can significantly enhance the control precision and efficiency of quantum devices. We propose a framework where the quantum control algorithm is treated as an intelligent agent, interacting with the quantum environment to learn optimal control strategies. This approach allows for adaptive control, addressing the challenges inherent in designing fixed control protocols for complex quantum systems. The methodology involves defining a reward function that reflects the desired quantum state evolution, using RL algorithms to train the agent, and validating the learned control strategy through simulations. We demonstrate the potential of this approach to improve control accuracy and robustness, particularly in scenarios with noisy environments or rapidly changing system parameters. The results suggest a promising direction for leveraging machine learning techniques to unlock the full potential of quantum control.
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