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

2026-02-25

AI Summary

This work addresses the severe performance degradation of quantum convolutional neural networks (QCNNs) on near-term quantum hardware due to noise accumulation with increasing circuit depth. To mitigate this limitation, the authors propose a noise-adaptive hybrid quantum-classical convolutional neural network that performs depth-wise intermediate measurements on discarded qubits at pooling layers. The multi-basis measurement outcomes are then fed as classical features into subsequent classical layers, enabling hierarchical classification through quantum-classical synergy. This approach effectively repurposes otherwise lost quantum information into noise-resilient signals, overcoming the sharp performance drop typically observed in conventional QCNNs as circuit scale grows. Experimental results demonstrate that the proposed method achieves more stable convergence, lower loss variance, and higher accuracy than standard QCNNs across various circuit sizes and realistic noise conditions, with its multi-basis variant closely approaching noiseless performance under real hardware noise, particularly excelling in large-scale circuits.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Non-convex Optimization

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Security and Privacy: Large-scale security measurements

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.

Problem

• quantum convolutional neural networks
• noise sensitivity
• circuit depth
• near-term quantum hardware
• hierarchical quantum classifiers

Innovation

• noise-adaptive
• hybrid quantum-classical
• depth-stratified feature extraction
• intermediate measurement
• quantum convolutional neural networks