Layered Uploading for Quantum Convolutional Neural Networks

arXiv:2404.09750v1 Announce Type: cross
Abstract: Continuing our analysis of quantum machine learning applied to our use-case of malware detection, we investigate the potential of quantum convolutional neural networks. More precisely, we propose a new architecture where data is uploaded all along the quantum circuit. This allows us to use more features from the data, hence giving to the algorithm more information, without having to increase the number of qubits that we use for the quantum circuit. This approach is motivated …

analysis architecture arxiv case convolutional neural networks cs.cr data detection features machine machine learning malware malware detection networks neural networks precisely quant-ph quantum