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Memetic Computing

QEA-QCNN: optimization of quantum convolutional neural network architecture based on quantum evolution

Yangyang Li, Xiaobin Hao, Guanlong Liu, Ronghua Shang, Licheng Jiao

Published Jul 9, 2024• 9 citations
Publisher
DOI
10.1007/s12293-024-00417-3

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Quantum machine learning has the potential to become a new tool for understanding complex nuclear structures. In this work, we apply a hybrid quantum convolutional neural network (QCNN) to nuclear charge-radius prediction for the first time, aiming to explore the feasibility of quantum machine learning in nuclear-physics data analysis. Based on a classical convolutional neural network (CNN) framework, a small variational quantum convolutional filter is introduced as a quantum feature map to extract local correlations on the nuclear chart. The QCNN shows promising predictive accuracy and training stability, and provides a reliable description of the charge-radius evolution along several representative isotopic chains. These results support further investigation of quantum convolutional architectures for nuclear-structure data analysis.
This chapter explores two paradigms in advanced neural network models: Convolutional Neural Networks (CNNs) and Quantum Convolutional Neural Networks (QCNNs). We start by delving into CNNs, describing how their layered architecture—from convolutional layers that extract local features and pooling layers that downscale spatial dimensions while preserving translational invariance to fully connected layers that combine learned features for decision-making—provides a strong framework for pattern recognition and image analysis. Expanding this traditional premise, the chapter presents QCNNs, where quantum computing methodologies are incorporated within the convolutional framework. With the use of quantum circuits and operations, QCNNs appear to be more efficient, especially with high-dimensional data, which poses difficulties in the classical paradigm. The chapter also compares CNN and QCNN architectures and their operational methods, highlighting how quantum methodology can potentially solve issues such as computational complexity and high-dimensional data challenges.