Quantum Machine Learning
416 papers for year 2023
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 205-216 of 416
Multithreaded parallelism for heterogeneous clusters of QPUs
Philipp Seitz, Manuel Geiger, Christian B. Mendl
Mutual information maximizing quantum generative adversarial networks
Mingyu Lee, Myeongjin Shin, Junseo Lee, Kabgyun Jeong
Neural Networks for Programming Quantum Annealers
Samuel Bosch, Bobak Kiani, Rui Yang, Adrian Lupascu, Seth Lloyd
New circuits and an open source decoder for the color code
Craig Gidney, Cody Jones
Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits
Zhan Yu, Qiuhao Chen, Yuling Jiao, Yinan Li, Xiliang Lu, Xin Wang, Jerry Zhijian Yang
Non-Linear Transformations of Quantum Amplitudes: Exponential Improvement, Generalization, and Applications
Arthur G. Rattew, Patrick Rebentrost
Non-Markovian cost function for quantum error mitigation with Dirac Gamma matrices representation.
Ahn D.
Nuclear Physics Opportunities at European Small-Scale Facilities
Jelena Vesić, Matjaž Vencelj
On fundamental aspects of quantum extreme learning machines
Weijie Xiong, Giorgio Facelli, Mehrad Sahebi, Owen Agnel, Thiparat Chotibut, Supanut Thanasilp, Zoë Holmes
On Neural Quantum Support Vector Machines
Lars Simon, Manuel Radons
On the Applicability of Quantum Machine Learning
Sebastian Raubitzek, Kevin Mallinger
On the connection between least squares, regularization, and classical shadows
Zhihui Zhu, Joseph M. Lukens, Brian T. Kirby