Quantum Machine Learning
248 papers for year 2022
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 109-120 of 248
Improving the Efficiency of Payments Systems Using Quantum Computing
Christopher McMahon, Donald McGillivray, Ajit Desai, Francisco Rivadeneyra, Jean-Paul Lam, Thomas Lo, Danica Marsden, Vladimir Skavysh
Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems like Max-Cut
Stefan Boettcher
Introducing the Quantum Research Kernels: Lessons from Classical Parallel Computing
A. Y. Matsuura, Timothy G. Mattson
Introduction to Quantum Computing with IBM Quantum Experience Using Qiskit and OpenQASM
Jong Wan Lee*
Investigating Quantum Many-Body Systems with Tensor Networks, Machine Learning and Quantum Computers
Korbinian Kottmann
Iteration Complexity of Variational Quantum Algorithms
Vyacheslav Kungurtsev, Georgios Korpas, Jakub Marecek, Elton Yechao Zhu
Kernel-based quantum regressor models learn non-Markovianity
Diego Tancara, Hossein T. Dinani, Ariel Norambuena, Felipe F. Fanchini, Raúl Coto
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection
Joseph Gallego-Mejia, Oscar Bustos-Brinez, Fabio A. González
Learning dynamical systems: an example from open quantum system dynamics
Pietro Novelli
Learning Fourier series with parametrized quantum circuits
Dirk Heimann, Hans Hohenfeld, Gunnar Schönhoff, Elie Mounzer, Frank Kirchner
Learning to predict arbitrary quantum processes
Hsin-Yuan Huang, Sitan Chen, John Preskill
Local Approach to Quantum-inspired Classification
Enrico Blanzieri, Roberto Leporini, Davide Pastorello