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 85-96 of 237
Image Classification via Quantum Machine Learning
Héctor Iván García Hernández, Raymundo Torres Ruiz, Guo-Hua Sun
Implementation of a Hamming distance-like genomic quantum classifier using inner products on ibmqx2 and ibmq_16_melbourne.
Kathuria K, Ratan A, McConnell M, Bekiranov S.
Implementing Bilinear Interpolation on Quantum Images
Fei Yan, Shan Zhao, Salvador E. Venegas-Andraca
Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems
R. Martínez-Peña, J. Nokkala, G. L. Giorgi, R. Zambrini, M. C. Soriano
Interpretable and unsupervised phase classification
Julian Arnold, Frank Schäfer, Martin Žonda, Axel U. J. Lode
Introducing Materials for Quantum Technology
Jason M Smith
Ising formulations of routing optimization problems
Daniel Jaroszewski, Fabian Klos, Benedikt Sturm
Lackadaisical quantum walks on triangular and honeycomb 2D grids
Nikolajs Nahimovs
Laplacian Eigenmaps with variational circuits: a quantum embedding of graph data
Slimane Thabet, Jean-Francois Hullo
Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation
Alberto Fabrizio, Ksenia R. Briling, David D. Girardier, Clemence Corminboeuf
Learning phase transitions in ferrimagnetic GdFeCo alloys
N. A. Koritsky, S. V. Solov'yov, A. K. Fedorov, A. K. Zvezdin
Learning temporal data with variational quantum recurrent neural network
Yuto Takaki, Kosuke Mitarai, Makoto Negoro, Keisuke Fujii, Masahiro Kitagawa