A Systematic Study of Noise Effects in Hybrid Quantum-Classical Machine Learning

2026-04-13

AI Summary

This study addresses the lack of systematic analysis on the combined impact of classical input noise and quantum hardware noise on the performance of variational quantum classifiers. For the first time, it jointly models both noise sources: employing a noisy ZZ feature map for classical data encoding and incorporating quantum noise channels—including depolarizing, amplitude/phase damping, Pauli errors, and readout errors—via Qiskit Aer. The work systematically evaluates model robustness under multi-level composite noise scenarios. Experimental results reveal that classical input noise significantly amplifies quantum decoherence effects, exacerbating training instability and leading to a marked drop in classification accuracy. These findings elucidate the synergistic mechanism through which classical and quantum noise jointly degrade model performance.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Near-term quantum machine learning (QML) models operate in environments wherein noise is unavoidable, arising from both imperfect classical data acquisition and the limitations of noisy intermediate-scale quantum (NISQ) hardware. Although most existing studies have focused primarily on quantum circuit noise in isolation, the combined influence of corrupted classical inputs and quantum hardware noise has received comparatively little attention. In this work, we present a systematic experimental study of the robustness of a variational quantum classifier under realistic multi-level noise conditions. Using the Titanic dataset as a benchmark, a range of dataset-level noise models-including speckle noise, impulse noise, quantization noise, and feature dropout are applied to classical features prior to quantum encoding using a ZZ feature map. In parallel, hardware-inspired quantum noise channels such as depolarizing noise, amplitude damping, phase damping, Pauli errors, and readout errors are incorporated at the circuit level using the Qiskit Aer simulator. The experimental results indicate that noise in classical input data can significantly intensify the effects of quantum decoherence, resulting in less stable training and noticeably lower classification accuracy. Together, these observations emphasize the importance of designing and evaluating quantum machine learning pipelines with noise in mind, and highlight the need to consider classical and quantum noise simultaneously when assessing QML performance in the NISQ era

Problem

• quantum machine learning
• classical noise
• quantum noise
• NISQ
• noise robustness

Innovation

• hybrid quantum-classical machine learning
• classical input noise
• quantum hardware noise
• variational quantum classifier
• NISQ robustness