Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension

arXiv:2403.19099v1 Announce Type: cross
Abstract: Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis. This approach is particularly attractive due to the absence of the barren plateau problem, a fundamental challenge in training quantum neural networks (QNNs), and its feasibility. However, a limitation arises when applying QCNNs to classical data. The network architecture is most natural when the number of input qubits is a power of …

abstract analysis architectures arxiv challenge convolutional neural network convolutional neural networks cs.lg data data analysis machine machine learning network networks neural network neural networks quant-ph quantum quantum neural networks training type