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

TL;DR

This study systematically investigates how combined classical and quantum noise affects the robustness of variational quantum classifiers using realistic noise models and the Titanic dataset.

Contribution

It provides the first comprehensive experimental analysis of classical and quantum noise effects on hybrid quantum-classical machine learning models.

Findings

Classical input noise significantly worsens quantum model stability and accuracy.

Quantum decoherence effects are amplified by noisy classical data.

Designing noise-aware QML pipelines is crucial for NISQ-era 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