Quantum Machine Learning
3,957 papers
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 3553-3564 of 3,957
Quantum Optical Experiments Modeled by Long Short-Term Memory
Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler, Sepp Hochreiter
Quantum Wasserstein Generative Adversarial Networks
Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu
Quantum-inspired annealers as Boltzmann generators for machine learning and statistical physics
Alexander E. Ulanov, Egor S. Tiunov, A. I. Lvovsky
QuBiT: a quantitative tool for analyzing epithelial tubes reveals unexpected patterns of organization in the <i>Drosophila</i> trachea.
Yang R, Li E, Kwon YJ, Mani M, Beitel GJ.
QuESTlink -- Mathematica embiggened by a hardware-optimised quantum emulator
Tyson Jones, Simon C Benjamin
Remote preparation for single-photon two-qubit hybrid state with hyperentanglement via linear-optical elements.
Jiao XF, Zhou P, Lv SX, Wang ZY.
RepLAB: a computational/numerical approach to representation theory
Denis Rosset, Felipe Montealegre-Mora, Jean-Daniel Bancal
Representations and descriptors unifying the study of molecular and bulk systems
Kevin Rossi, James Cumby
Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer
João Caldeira, Joshua Job, Steven H. Adachi, Brian Nord, Gabriel N. Perdue
Retrieving Quantum Information with Active Learning
Yongcheng Ding, José D. Martín-Guerrero, Mikel Sanz, Rafael Magdalena-Benedicto, Xi Chen, Enrique Solano
Revising the measurement process in the variational quantum eigensolver: is it possible to reduce the number of separately measured operators?
Izmaylov AF, Yen TC, Ryabinkin IG.
Revisiting old combinatorial beasts in the quantum age: quantum annealing versus maximal matching
Daniel Vert, Renaud Sirdey, Stéphane Louise