IEEE International Conference on Quantum Computing and Engineering (QCE)

Robust Adaptive Quantum Feedback Under Practical Uncertainties- A Lightweight Approach

Anusha Krishna Murthy, John Watkins, Elizabeth Behrman, James E. Steck, Edwin M. Sawan

Published Aug 30, 2025• 0 citations

Abstract

Quantum systems are highly sensitive to timing jitter, measurement noise, and partial observability, which degrade feedback control performance. We present a lightweight adaptive quantum feedback strategy inspired by Deep Deterministic Policy Gradient (DDPG), but designed to operate without full reinforcement learning training. The method adaptively updates control gains using filtered partial measurements and scalar rewards to enhance fidelity under uncertainty. We describe the control formulation, motivate its relevance to near-term devices, and outline future directions for simulation and implementation.

DOI: 10.1109/qce65121.2025.10474

Related papers

Adaptive Quantum Control with Feedback Learning Algorithm

Jincheng Zhang

This paper presents a novel feedback learning algorithm for adaptive quantum control. The algorithm leverages reinforcement learning principles combined with quantum control theory to dynamically adjust control strategies based on the observed state and environment of a quantum system. The core mechanism involves constructing an adaptive quantum control feedback learning algorithm that enables autonomous optimization of the quantum system. The algorithm utilizes a state representation of the quantum system, a reward function reflecting the desired outcome, and a control policy updated through a reinforcement learning process. Mathematical formulations are provided to describe the system dynamics, the control policy, and the learning process. The proposed algorithm aims to overcome the limitations of traditional fixed-parameter quantum control methods by adapting to uncertainties and complex dynamics, ultimately leading to improved control performance and robustness. The theoretical framework presented here offers a foundation for developing advanced quantum control strategies in various applications, including quantum computation, quantum sensing, and quantum metrology.

Aug 29, 20260 citations

Adaptive and Robust Feedback-Based Quantum Optimization using Reinforcement Learning

Sanjay Sharma, Shyam Akashe, Govind Murari Upadhyay +1 more

Abstract The paper introduces a novel hybrid Feedback-Based Quantum Optimization (FBQO) framework that integrates reinforcement learning (RL) for adaptive parameter control and Kalman filters for noise mitigation to enhance quantum optimization on noisy intermediate-scale quantum (NISQ) devices. Unlike traditional quantum Approximate optimization algorithm (QAOA), the proposed method dynamically tunes parameters through learned policies and statistically filtered feedback, enabling faster convergence and greater noise resilience. Key contributions include the formulation of quantum optimization as a Markov Decision Process, integration of deep quantum networks (DQNs) for adaptive control, and the use of Kalman filtering for robust state estimation. Experimental results on the Max-Cut problem demonstrate superior performance in convergence rate, stability, and optimization accuracy over existing techniques. The main finding is that RL-FBQO achieves convergence in 10–20 iterations, compared to 20–30 for FBQO and 40–50 for QAOA, with superior stability.

Journal of Transformative Technologies and Sustainable DevelopmentOct 21, 20250 citations

Robust optimization for quantum reinforcement learning control using partial observations

Chen Jiang, Yu Pan, Zheng‐Guang Wu +2 more

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of the quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and the quantum approximate optimization algorithm (QAOA). It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for a QAOA with a noisy control Hamiltonian. This robust control optimization model can be trained to compensate for the uncertainties in practical quantum computing.

arXiv:2206.14420Physical Review AJun 24, 202210 citations

Enhancement of Machine Learning for Adaptive Quantum Computing Control: The Optimization Performance and Prevention of Real Time Error

V. Saravanan, Ganesan M, R. Balasubramaniyan +3 more

Adaptive quantum computing control is important for the stability and efficiency of quantum systems, especially in the presence of dynamic environmental noise and hardware fluctuations. We present a unified strategy for machine learning (ML) techniques to improve adaptive quantum control in two main directions: optimization speed and out-of-sample error. Here, we propose a hybrid learning method combining reinforcement learning (RL), deep neural networks (DNNs), and quantum-inspired models to actively and adaptively update quantum gate parameters and error mitigation strategies on the fly. Embedding of quantum noise models directly into the learning allows the system to predict and correct for decoherence, gate errors and measurement inaccuracy before they affect computational fidelity. Using extensive simulations as well as experimental validation on noisy intermediate-scale quantum (NISQ) processors, our improved ML model achieves a substantially higher fidelity and robustness compared to standard model-free and static control approaches. We introduce a novel feedback-loop mechanism based on online learning algorithms enabling the system to adjust for changing noise profiles and hardware drift without retraining. The optimization part uses metaheuristic methods to quickly converge to control parameters that perform well, and the error prevention mechanism features anomaly detection and prediction correction layers to minimize the effects of cumulative decoherence. The results demonstrate that our adaptive control framework can be used to not only speed up the convergence in training quantum algorithms, but also sustain the performance of gates against error at a variety of error rates with the reduction of the real-time impact of the error up to 35 %. This work highlights the promise of machine learningaugmented quantum control for improving reliability and scalability in quantum computing and serves as a stepping stone for future incorporation with fault-tolerant designs.

International Conference on Electronics and Computing, Communication Networking Automation Technologies (ICEC2NT)Sep 3, 20252 citations

Experiment-compatible measurement--feedback quantum state preparation with reinforcement learning

Xiaotian Nie, Tao Zhang, Linghui Chen

Ground-state preparation is a critical task in quantum simulation and quantum computing, as it enables the study of correlated phases and the generation of entangled resource states. While measurement--feedback control has emerged as a promising route to state preparation, existing schemes either rely on handcrafted, task-specific policies or are designed using full quantum-state information that is unavailable in real experiments and becomes impractical for large many-body systems. Here we develop an adaptive measurement--feedback protocol based on reinforcement learning under partial observability. The controller uses only the history of experimentally accessible measurement outcomes to choose both the measurement operator and the feedback action in real time. To make training compatible with experiments, we introduce a stochastic terminal reward built from one-shot measurements of randomly sampled Hamiltonian components, avoiding unphysical full-state reconstruction while remaining an unbiased estimator of the target energy. We demonstrate the method by preparing ground states of the Bose--Hubbard model and by generating GHZ states, establishing a scalable and hardware-compatible route to quantum state preparation.

quant-pharXiv:2606.13005Chinese Physics LettersJun 12, 20260 citations

Adaptive Quantum Control via Reinforcement Learning

Jincheng Zhang

This paper investigates the application of reinforcement learning (RL) for adaptive quantum control. Traditional quantum control methods often rely on pre-designed pulse shapes optimized for specific scenarios, lacking adaptability to variations in system parameters or environmental noise. This work proposes a novel framework where an RL agent learns to dynamically adjust quantum control pulses in real-time, maximizing the fidelity of quantum operations. The agent receives feedback from the quantum system, typically measured through state variables, and utilizes this information to optimize the control pulse sequence. This adaptive approach addresses the limitations of static control schemes and promises enhanced robustness and performance in complex quantum systems. The core claim centers on utilizing reinforcement learning to design optimal quantum control pulses. The central mechanism involves a trained controller dynamically adjusting pulses based on system feedback. The presented methodology offers a new approach to quantum control, particularly relevant in noisy environments and systems with parameter variations. The research demonstrates the potential of RL for achieving superior quantum control outcomes compared to conventional techniques. ---

Sep 4, 20260 citations