Quantum Machine Learning

521 papers for year 2024

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 157-168 of 521

Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance

Ishita Agarwal, Taylor L. Patti, Rodrigo Araiza Bravo, Susanne F. Yelin, Anima Anandkumar

2024 arXiv arXiv preprint

Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles

Franziska Greinert, Simon Goorney, Dagmar Hilfert-Rüppell, Malte S. Ubben, Rainer Müller

2024 arXiv arXiv preprint

Fast characterization of multiplexed single-electron pumps with machine learning

N. Schoinas, Y. Rath, S. Norimoto, W. Xie, P. See, J. P. Griffiths, C. Chen, D. A. Ritchie, M. Kataoka, A. Rossi, I. Rungger

2024 arXiv arXiv preprint

Fast classical simulation of qubit-qudit hybrid systems

Haemanth Velmurugan, Arnav Das, Turbasu Chatterjee, Amit Saha, Anupam Chattopadhyay, Amlan Chakrabarti

2024 arXiv arXiv preprint

Fast generation of entanglement between coupled spins using optimization and deep learning methods

Dimitris Koutromanos, Dionisis Stefanatos, Emmanuel Paspalakis

2024 arXiv arXiv preprint

Fast Laplace transforms on quantum computers

Julien Zylberman

2024 arXiv arXiv preprint

Faster and Better Quantum Software Testing through Specification Reduction and Projective Measurements

Noah H. Oldfield, Christoph Laaber, Tao Yue, Shaukat Ali

2024 arXiv arXiv preprint

First Photon Machine Learning

Lili Li, Santosh Kumar, Malvika Garikapati, Yu-Ping Huang

2024 arXiv arXiv preprint

Flood Prediction Using Classical and Quantum Machine Learning Models

Marek Grzesiak, Param Thakkar

2024 arXiv arXiv preprint

Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects

Andrea Bulgarelli, Elia Cellini, Karl Jansen, Stefan Kühn, Alessandro Nada, Shinichi Nakajima, Kim A. Nicoli, Marco Panero

2024 arXiv arXiv preprint

FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields

Shihao Shao, Haoran Geng, Zun Wang, Qinghua Cui

2024 arXiv arXiv preprint

From {\tt Ferminet} to PINN. Connections between neural network-based algorithms for high-dimensional Schrödinger Hamiltonian

Mashhood Khan, Emmanuel Lorin

2024 arXiv arXiv preprint