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 3529-3540 of 3,957

Machine learning engineering of quantum currents

Tobias Haug, Rainer Dumke, Leong-Chuan Kwek, Christian Miniatura, Luigi Amico

2019 arXiv arXiv preprint

Machine learning for molecular simulation

Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi

2019 arXiv arXiv preprint

Machine learning for optimal parameter prediction in quantum key distribution

Wenyuan Wang, Hoi-Kwong Lo

2019 Crossref Physical Review A

Machine learning logical gates for quantum error correction

Hongxiang Chen, Michael Vasmer, Nikolas P. Breuckmann, Edward Grant

2019 arXiv arXiv preprint

Many-body calculations for periodic materials via quantum machine learning

Shu Kanno, Tomofumi Tada

2019 arXiv arXiv preprint

On the Classical Hardness of Spoofing Linear Cross-Entropy Benchmarking

Scott Aaronson, Sam Gunn

2019 arXiv arXiv preprint

On the convergence of projective-simulation-based reinforcement learning in Markov decision processes

Walter L. Boyajian, Jens Clausen, Lea M. Trenkwalder, Vedran Dunjko, Hans J. Briegel

2019 arXiv arXiv preprint

Pembelajaran Tematik Terpadu dengan Menggunakan Model Quantum Teaching

Nadya Yolanda, Reinita Reinita

2019 Crossref Journal of Elementary School (JOES)

PENGARUH MODEL QUANTUM LEARNING TERHADAP PEMAHAMAN KONSEP DAN HASIL BELAJAR SISWA KELAS X

Anisa Anisa, Rosane Medriati, Desy Hanisa Putri

2019 Crossref Jurnal Kumparan Fisika

Point-ahead demonstration of a transmitting antenna for satellite quantum communication

Xuan Han, Hai-Lin Yong, Ping Xu, Wei-Yang Wang, Kui-Xing Yang, Hua-Jian Xue, Wen-Qi Cai, Ji-Gang Ren, Cheng-Zhi Peng, Jian-Wei Pan

2019 arXiv arXiv preprint

Portfolio rebalancing experiments using the Quantum Alternating Operator Ansatz

Mark Hodson, Brendan Ruck, Hugh Ong, David Garvin, Stefan Dulman

2019 arXiv arXiv preprint

Prediction of Major Regio-, Site-, and Diastereoisomers in Diels-Alder Reactions by Using Machine-Learning: The Importance of Physically Meaningful Descriptors.

Beker W, Gajewska EP, Badowski T, Grzybowski BA

2019 PubMed Angewandte Chemie (International ed. in English)