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IEEE International Conference on Quantum Computing and Engineering (QCE)

Constructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learning

David Winderl, Nicola Franco, Jeanette Miriam Lorenz

Published Aug 30, 2025• 5 citations
Publisher

Abstract

Quantum Machine Learning (QML) is rapidly evolving. Recent studies suggest that quantum noise could improve classifier robustness. To verify this claim, we developed a semidefinite programming model that, given a dataset and a QML ansatz, constructs the most robust noise channel possible. In our smallscale experiments, we first explore the range of behaviors generated by this optimized noise channel. Secondly, we show that, despite recent claims, the direct impact of noise is modest compared to the substantial robustness improvements achieved through increasing the number of qubits. Aside from that, we utilize our framework to assess different noise channels in terms of their robustness and certifiability.

DOI
10.1109/qce65121.2025.00174

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Quantum machine learning (QML) holds significant promise for accelerating and enhancing machine learning tasks. However, the inherent fragility of quantum systems to noise poses a substantial challenge to the successful implementation of QML algorithms. This paper investigates and proposes novel noise optimization strategies specifically tailored for QML. The core claim is to design a strategy capable of effectively handling noise within QML algorithms, ultimately boosting model training accuracy and robustness. The proposed mechanism leverages either the quantum decoherence principle to suppress noise or quantum entanglement mechanisms to mitigate its effects. We explore various approaches, focusing on adaptive noise mitigation techniques and incorporating error correction methodologies within the quantum computation framework. The presented framework aims to improve the reliability of QML algorithms and contribute to the development of practical, robust quantum machine learning systems. The research addresses a critical gap in the field by offering a systematic approach to noise management in QML, a crucial step towards realizing the full potential of this technology. The ultimate goal is to demonstrate that, with appropriate noise optimization, QML algorithms can achieve performance comparable to, or exceeding, classical machine learning algorithms.
Quantum Machine Learning (QML) offers a paradigm shift in machine learning, potentially surpassing classical approaches due to quantum phenomena. A critical challenge in many QML applications is the need to effectively manage noise within the model parameters, particularly in training datasets with high dimensionality. This paper explores the application of a novel noise matrix optimization algorithm, specifically tailored for QML, leveraging quantum properties to enhance model accuracy and stability. We propose a method that utilizes quantum superposition and entanglement to explore the parameter space more efficiently, leading to improved generalization performance. The core mechanism focuses on manipulating the noise matrix through quantum rotations and entanglement to achieve a more nuanced and robust optimization. We demonstrate the effectiveness of this approach through a series of simulations and preliminary experiments, highlighting its potential to significantly improve model performance in specific scenarios. The paper concludes with a discussion of future research directions and the implications of this quantum optimization technique for the field of machine learning.
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As with classical neural networks, quantum machine learning (QML) models are vulnerable to small input perturbations that can significantly alter output predictions. Certifying the robustness of QML models, particularly on NISQ hardware, is therefore a fundamental step toward trustworthy quantum AI. This chapter reviews our recently developed comprehensive formal framework for verifying adversarial robustness in QML. The core of this framework is a fidelity-based robustness lower bound computable directly from the measurement outcome distribution, which enables both formal verification and empirical estimation on real quantum devices. Additionally, the optimal bound can be computed via semidefinite programming (SDP) with full knowledge of the quantum machine learning models. We incorporate these results into: (1) an efficient formal verification framework; (2) VeriQR, the first dedicated QML robustness verification tool; and (3) the first experimental benchmark of quantum adversarial robustness on a 20-qubit superconducting processor. Together, these systematic advances enable scalable, physically grounded robustness evaluation of QML models.
This paper presents a novel theoretical framework for understanding noise generalization in quantum machine learning (QML). We address the critical gap in current research, which predominantly relies on experimental validation, by developing a mathematically rigorous approach. Our core claim centers on establishing a quantifiable relationship between noise levels and the generalization performance of QML models. We leverage quantum decoherence theory and quantum error correction concepts to analyze the impact of noise on model parameters. Utilizing tools from functional analysis and norm theory, we derive a theoretical model for noise generalization. Furthermore, we propose strategies for mitigating the detrimental effects of noise, offering potential avenues for enhancing the robustness of QML systems. This work provides a foundational understanding for future QML design and optimization, moving beyond empirical observations toward a predictive and controllable approach.