arXiv AI By Xinge Wu, Huaxin Wang, Jiajun Liu, Ruiqing He, Jiandong Shang, Hengliang Guo, Qiang Chen

Sparsified Kolmogorov-Arnold Networks for Interpretable Quantum State Tomography

Read the original on arXiv AI →

arXiv:2606. 11814v1 Announce Type: cross Abstract: Machine-learning approaches to quantum state tomography can achieve high reconstruction fidelity, but the physical structure used by the trained model often remains implicit.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
Aug 21

Quantum Gaussian processes for prediction of channel observations

arXiv:2608. 19306v1 Announce Type: cross Abstract: Given a set of input states, we consider the task of predicting the expectation value of a Pauli observable at the output of an unknown quantum evolution, using only a limited number of measurements.

By Jonas J\"ager, Yaroslav Khmelnitskiy, Paolo Braccia, Artur Miroszewski, Diego Garc\'ia-Mart\'in, M. Cerezo, Piotr Czarnik
arXiv Machine Learning
Jun 26

Tailor Made Embeddings for Quantum Machine Learning

arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.

By Aldo Lamarre, Dominik \v{S}afr\'anek