arXiv AI By Timothy Heightman, Elena Orlova, Philip Mantrov, Aleksei Ustimenko

Topology Obstructs Pure Foundation Neural Quantum States

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Machine Learning
Jun 16

Learning ground state observables from quantum computing experiments

arXiv:2606. 15983v1 Announce Type: cross Abstract: Recent theoretical progress has established conditions under which machine learning models can efficiently predict ground-state properties of gapped local Hamiltonians when trained on quantum-generated data.

By Ben Jaderberg, Freya Shah, Minjun Jeon, M. Emre Sahin, Christa Zoufal, Kunal Sharma
arXiv Machine Learning
Jul 14

Learning Topological Quantum Phases from Limited Subsystems

arXiv:2607. 10656v1 Announce Type: cross Abstract: Characterizing quantum topological phases requires measuring non-local string order parameters, demanding access to the full system, which is often experimentally unfeasible.

By Mehran Khosrojerdi, Sougato Bose, Alessandro Cuccoli, Paola Verrucchi, Abolfazl Bayat, Leonardo Banchi
arXiv AI
Jun 9

Exploring the Effect of Basis Rotation on NQS Performance

arXiv:2512. 17893v2 Announce Type: replace-cross Abstract: Neural Quantum States (NQS) are powerful variational representations of quantum many-body wavefunctions, yet their performance depends sensitively on the chosen basis.

By Sven Benjamin Ko\v{z}i\'c, Vinko Zlati\'c, Fabio Franchini, Salvatore Marco Giampaolo