Quantum convolutional neural networks
作者: Iris CongSoonwon ChoiMikhail D. Lukin
作者单位: 1000000041936754X, grid.38142.3c, Department of Physics, Harvard University, Cambridge, MA, USA
20000 0001 2181 7878, grid.47840.3f, Department of Physics, University of California, Berkeley, Berkeley, CA, USA
刊名: Nature Physics, 2019, Vol.15 (12), pp.1273-1278
来源数据库: Springer Nature Journal
DOI: 10.1038/s41567-019-0648-8
英文摘要: Abstract(#br)Neural network-based machine learning has recently proven successful for many complex applications ranging from image recognition to precision medicine. However, its direct application to problems in quantum physics is challenging due to the exponential complexity of many-body systems. Motivated by recent advances in realizing quantum information processors, we introduce and analyse a quantum circuit-based algorithm inspired by convolutional neural networks, a highly effective model in machine learning. Our quantum convolutional neural network (QCNN) uses only O (log( N )) variational parameters for input sizes of N qubits, allowing for its efficient training and implementation on realistic, near-term quantum devices. To explicitly illustrate its capabilities, we show that...
全文获取路径: Springer Nature  (合作)
影响因子:19.352 (2012)