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A quantum convolutional neural network on NISQ devices

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arXiv211001771v1 quantph 5 Oct 2024

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Training Quantum Neural Networks on NISQ Devices ResearchGate

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A Quantum Convolutional Neural Network on NISQ Devices

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14 avr. 2024 · A quantum convolutional neural network on NISQ devices. This machine learning model can provide implementable quantum circuits that accurately corresponds to a specific classical convolutional kernel and opens up the prospect of exploiting quantum power to process information in the era of big data. 14 avr. 2024 · Here we propose a quantum convolutional neural network (QCNN) inspired by convolutional neural networks (CNN), which greatly reduces the computing complexity compared with its. 9 oct. 2018 · The authors proposed hybrid quantum-classical convolutional neural networks (QCCNNs), with an architecture suitable for noisy intermediate scale quantum (NISQ) devices, and proposed a “quantum feature map”, which is essentially a variational quantum circuit and maps the small windows of the input data into a scalar. Expand. 14 avr. 2024 · Compared with previous work, this machine learning model can provide implementable quantum circuits that accurately corresponds to a specific classical convolutional kernel. It provides an efficient avenue to transform CNN to QCNN directly and opens up the prospect of exploiting quantum power to process information in the era of big data. 14 avr. 2024 · Here we propose a quantum convolutional neural network (QCNN) inspired by convolutional neural networks (CNN), which greatly reduces the computing complexity compared with its classical counterparts, with basic gates and variational parameters, where is the input data size, is the filter mask size and is the number of parameters in a Hamiltonian. on NISQ devices. In general, a VQA can be mapped to a fully-parameterized quantum circuit (PQC) and driven by a classical optimizer[23{29]. Quantum convolutional neural networks (QCNN) as an example of QML have emerged as the overlap of classical convolutional networks (CNN) and QML, and provide a potential solution to speeding up. 1 mars 2024 · Recently, hybrid classical–quantum algorithms have emerged as means to derive quantum advantage on developing NISQ devices while constructing quantum neural network models. These algorithms are optimally constructed and simulated on classical hardware.