Physical Review Research

Adaptive denoising quantum state preparation in a dynamic environment

Zhao-Ming Wang, Tuo-Zhi Chen

Published Nov 25, 2024• 5 citations

Abstract

Quantum state preparation (QSP) is an essential step in many quantum information processing tasks and has been deeply studied in a closed system. However, environmental noise inevitably destroys the quantumness of the system, resulting in decreased fidelity. In this work, we investigate the enhancement of fidelity by pulse control for arbitrary single or two-qubit QSP in a noisy environment. The control pulses are designed by leveraging supervised learning. The environment is modeled as a collection of bosons, and we use the quantum state diffusion equation technique to deal with the dynamics of the system. We consider two types of noise: quasistatic noise and dynamic noise. We propose active and adaptive denoising schemes for the quasistatic and dynamic noise, respectively. The environmental parameters are required to construct the dataset when training the neural network (NN) in adaptive denoising, while they are not needed in active denoising. The adaptive denoising scheme is universal in that it can be effectively applied to both quasistatic and dynamic noises. High-fidelity QSP has been obtained by the well-trained NN. Our work demonstrates that machine learning has great potential in optimal pulse design in a dynamic environment. Published by the American Physical Society 2024

DOI: 10.1103/physrevresearch.6.043195

Related papers

Machine-Learning-Assisted Pulse Design for State Preparation in a Noisy Environment

Zhaowei Wang, Hongyang Ma, Yun‐An Yan +2 more

High-precision quantum control is essential for quantum computing and quantum information processing. However, its practical implementation is challenged by environmental noise, which affects the stability and accuracy of quantum systems. In this paper, using machine learning techniques we propose a quantum control approach that incorporates environmental factors into the design of control schemes, improving the control fidelity in noisy environments. Specifically, we investigate arbitrary quantum state preparation in a two-level system coupled to a bosonic bath. We use both Deep Reinforcement Learning (DRL) and Supervised Learning (SL) algorithms to design specific control pulses that mitigate the noise. These two neural network (NN) based algorithm both have the advantage that the well trained NN can output the optimal pulse sequence for any environmental parameters. Comparing the performance of these two algorithms, our results show that DRL is more effective in low-noise environments due to its strong optimization capabilities, while SL provides greater stability and performs better in high-noise conditions. These findings highlight the potential of machine learning techniques to enhance the quantum control fidelity in practical applications.

arXiv:2508.20377Aug 28, 20250 citations

Machine learning assisted zero-area pulse design in an open quantum system

Yi-Ye Li, Lian-Ao Wu, Zhao-Ming Wang

Accurate and precise quantum control is crucial for quantum information processing. Zero-area pulse control, originally developed for closed systems, has played a significant role in driving quantum systems toward desired states. However, its effectiveness is often compromised by environmental noise. Traditional methods can find optimal pulses for fixed environmental parameters, and they need to be recalculated again once the environmental parameters change. Here, we propose a supervised learning (SL)-based protocol to design optimal zero-area pulses in noisy environments. By including environmental parameters as input during training, the neural network (NN) learns to generate effective controls for various environmental parameters. We demonstrate the protocol using quantum state transmission through a spin chain governed by a non-Markovian master equation. The SL-optimized pulses significantly enhance transmission fidelity compared to ideal pulses, with greater improvements observed at higher system-bath coupling and temperature, where more optimization space exists. Interestingly, fidelity first increases and then decreases with stronger Markovianity, suggesting that highly Markovian environments limit control performance. We also compare the SL method with a genetic algorithm, and the similar results confirm the accuracy and generalization ability of the NN-based approach.

Physical Review ResearchAug 8, 20253 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

Noise-Adaptive Predictive Dynamical Decoupling

Ali Abu-Nada, Subhashish Banerjee, Lian-Ao Wu

Protecting quantum coherence against realistic environmental noise remains one of the fundamental obstacles to scalable quantum technologies. We develop a noise-adaptive dynamical decoupling framework that combines analytical open-quantum-system modeling with machine-learning-based forecasting for a qubit interacting with random telegraph noise. Unlike conventional dynamical decoupling protocols based on fixed pulse schedules, the proposed approach continuously forecasts short-time coherence evolution and adaptively applies control pulses according to the instantaneous noise dynamics. We investigate stationary and non-stationary environments spanning both Markovian and non-Markovian regimes. Numerical simulations demonstrate that the machine-learning-assisted adaptive control strategy substantially outperforms conventional periodic dynamical decoupling while using a comparable number of control pulses. The improvement becomes particularly pronounced in non-Markovian and non-stationary regimes, where memory effects, coherence revivals, and temporally evolving noise strongly limit the effectiveness of static pulse protocols. These results establish predictive machine-learning-assisted dynamical decoupling as a promising and scalable framework for adaptive quantum control in realistic noisy quantum devices.

quant-pharXiv:2606.15769Jun 16, 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

Adaptive Quantum Noise Correction Algorithm – A Dynamic Optimization Approach

Jincheng Zhang

Quantum information processing offers unprecedented capabilities, but the inherent fragility of quantum systems necessitates sophisticated error correction techniques. Traditional quantum error correction methods often rely on pre-defined error correction codes, which struggle to adapt to the dynamic nature of the quantum environment. This paper proposes a novel adaptive quantum noise correction algorithm designed to dynamically adjust error correction parameters based on environmental fluctuations. The algorithm leverages a feedback loop that continuously assesses the quantum system's state and adjusts the correction process accordingly. This approach significantly enhances the algorithm's efficiency and robustness, potentially enabling the realization of more complex and reliable quantum computations. We present a detailed analysis of the algorithm's performance, demonstrating its effectiveness in mitigating noise and achieving improved fidelity.

Aug 31, 20260 citations