arXiv Machine Learning By Alexander G. Abanov, Luca Candelori, Harold C. Steinacker, Martin T. Wells, Jerome R. Busemeyer, Cameron J. Hogan, Vahagn Kirakosyan, Nicola Marzari, Sunil Pinnamaneni, Dario Villani, Mengjia Xu, Kharen Musaelian

Quantum Geometry of Data

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arXiv Machine Learning
Jul 16

Quantum Topological Data Encoding

arXiv:2607. 13847v1 Announce Type: cross Abstract: Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representations.

By Adam Weso{\l}owski, Dimitrios Thanos, Daniel Leykam, Lirand\"e Pira
arXiv Machine Learning
Jul 13

Is data-efficient learning feasible with quantum models?

arXiv:2508. 19437v2 Announce Type: replace-cross Abstract: The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics remains elusive.

By Alona Sakhnenko, Christian B. Mendl, Jeanette M. Lorenz
arXiv Machine Learning
Aug 20

Learning Topological Features of $\widehat Z$-invariants

arXiv:2608. 18570v1 Announce Type: cross Abstract: Machine learning and data analysis techniques have recently emerged as powerful tools for identifying patterns and formulating conjectures in mathematical research, most notably in the field of low-dimensional topology.

By Brandon Robinson, Shimal Harichurn, Fabian Ruehle, Sergei Gukov, Rak-Kyeong Seong, Miranda C. N. Cheng