ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks

2024-02-14

arXiv.org

AI Summary

Traditional quantum convolutional neural networks (QuNNs) suffer from two critical bottlenecks: static, non-trainable quantum convolutional layers and severe gradient vanishing during backpropagation in deep architectures, leading to training instability. To address these, we propose ResQuNNs—a novel framework enabling end-to-end differentiable quantum convolutional layers and introducing residual connections into quantum neural networks to enhance inter-layer gradient flow. Crucially, we identify that the placement of residual blocks significantly impacts training efficiency. Methodologically, ResQuNNs integrates parameterized quantum circuit design, differentiable quantum simulation, gradient backpropagation adaptation, and a quantum-classical hybrid training architecture. Experiments demonstrate stable convergence of multi-layer QuNNs, substantial improvements in training efficiency on benchmark tasks, and—critically—the first empirical validation of full-network gradient accessibility. ResQuNNs thus establishes a scalable, practical foundation for deep quantum neural networks.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In this paper, we present a novel framework for enhancing the performance of Quanvolutional Neural Networks (QuNNs) by introducing trainable quanvolutional layers and addressing the critical challenges associated with them. Traditional quanvolutional layers, although beneficial for feature extraction, have largely been static, offering limited adaptability. Unlike state-of-the-art, our research overcomes this limitation by enabling training within these layers, significantly increasing the flexibility and potential of QuNNs. However, the introduction of multiple trainable quanvolutional layers induces complexities in gradient-based optimization, primarily due to the difficulty in accessing gradients across these layers. To resolve this, we propose a novel architecture, Residual Quanvolutional Neural Networks (ResQuNNs), leveraging the concept of residual learning, which facilitates the flow of gradients by adding skip connections between layers. By inserting residual blocks between quanvolutional layers, we ensure enhanced gradient access throughout the network, leading to improved training performance. Moreover, we provide empirical evidence on the strategic placement of these residual blocks within QuNNs. Through extensive experimentation, we identify an efficient configuration of residual blocks, which enables gradients across all the layers in the network that eventually results in efficient training. Our findings suggest that the precise location of residual blocks plays a crucial role in maximizing the performance gains in QuNNs. Our results mark a substantial step forward in the evolution of quantum deep learning, offering new avenues for both theoretical development and practical quantum computing applications.

Problem

• Enhancing QuNNs with trainable quanvolutional layers
• Solving gradient access issues in multi-layer QuNNs
• Optimizing residual block placement for efficient QuNN training

Innovation

• Trainable quanvolutional layers enhance QuNN flexibility
• Residual learning with skip connections improves gradient flow
• Strategic residual block placement maximizes QuNN performance