Quantum Machine Learning
237 papers for year 2020
Quantum Machine Learning Research Context
This category covers quantum machine learning research, including quantum kernels, variational classifiers, hybrid learning systems, generative models, and QML benchmarks.
Showing 25-36 of 237
Automatic virtual voltage extraction of a 2x2 array of quantum dots with machine learning
Giovanni A. Oakes, Jingyu Duan, John J. L. Morton, Alpha Lee, Charles G. Smith, M. Fernando Gonzalez Zalba
Barren plateaus preclude learning scramblers
Zoë Holmes, Andrew Arrasmith, Bin Yan, Patrick J. Coles, Andreas Albrecht, Andrew T. Sornborger
Benchmarking 50-Photon Gaussian Boson Sampling on the Sunway TaihuLight
Yuxuan Li, Mingcheng Chen, Yaojian Chen, Haitian Lu, Lin Gan, Chaoyang Lu, Jianwei Pan, Haohuan Fu, Guangwen Yang
Bilinear dynamic mode decomposition for quantum control
Andy Goldschmidt, Eurika Kaiser, Jonathan L. Dubois, Steven L. Brunton, J. Nathan Kutz
Bootstrapping quantum process tomography via a perturbative ansatz.
Govia LCG, Ribeill GJ, Ristè D, Ware M, Krovi H.
Chaos and Complexity from Quantum Neural Network: A study with Diffusion Metric in Machine Learning
Sayantan Choudhury, Ankan Dutta, Debisree Ray
Characterizing the loss landscape of variational quantum circuits
Patrick Huembeli, Alexandre Dauphin
Circuit-based quantum random access memory for classical data with continuous amplitudes
Tiago M. L. de Veras, Ismael C. S. de Araujo, Daniel K. Park, Adenilton J. da Silva
Classical and quantum random-walk centrality measures in multilayer networks
Lucas Böttcher, Mason A. Porter
Classical symmetries and the Quantum Approximate Optimization Algorithm
Ruslan Shaydulin, Stuart Hadfield, Tad Hogg, Ilya Safro
Comment on 'Semi-Quantum Private Comparison Based on Bell States'
You-Lin Chen, Yu-Chin Lu, Zhong-Xuan Lin, Tzonelih Hwang
Comparative study of variational quantum circuit and quantum backpropagation multilayer perceptron for COVID-19 outbreak predictions
Pranav Kairon, Siddhartha Bhattacharyya