Noise-adaptive hybrid quantum convolutional neural networks based on depth-stratified feature extraction

TL;DR

This paper introduces a noise-adaptive hybrid quantum convolutional neural network that leverages depth-stratified measurements and classical post-processing to enhance quantum classification accuracy under noise, especially as circuit size increases.

Contribution

It proposes a novel hybrid QCNN architecture using depth-stratified measurements and classical neural networks to improve noise resilience in quantum classification.

Findings

Enhanced stability and accuracy over standard QCNNs under noise.

Performance improves with larger circuit sizes, mitigating scaling issues.

Multi-basis measurements approach noiseless performance even with realistic noise.

Abstract

Hierarchical quantum classifiers, such as quantum convolutional neural networks (QCNNs), represent recent progress toward designing effective and feasible architectures for quantum classification. However, their performance on near-term quantum hardware remains highly sensitive to noise accumulation across circuit depth, calling for strategies beyond circuit-architecture design alone. We propose a noise-adaptive hybrid QCNN that improves classification under noise by exploiting depth-stratified intermediate measurements. Instead of discarding qubits removed during pooling operations, we measure them and use the resulting outcomes as classical features that are jointly processed by a classical neural network. This hybrid hierarchical design enables noise-adaptive inference by integrating quantum intermediate measurements with classical post-processing. Systematic experiments across multiple circuit sizes and noise settings, including hardware-calibrated noise models derived from IBM Quantum backend data, demonstrate more stable convergence, reduced loss variability, and consistently higher classification accuracy compared with standard QCNNs. Moreover, we observe that this performance advantage significantly amplifies as the circuit size increases, confirming that the hybrid architecture mitigates the scaling limitations of standard architectures. Notably, the multi-basis measurement variant attains performance close to the noiseless limit even under realistic noise. While demonstrated for QCNNs, the proposed depth-stratified feature extraction applies more broadly to hierarchical quantum classifiers that progressively discard qubits.