arXiv Machine Learning

Unifying quantum measurement constructions via a relative-entropy minimum change principle

arXiv:2608. 04055v1 Announce Type: cross Abstract: The minimum change principle provides an information-theoretic characterization of the Bayes reversal channel in classical probability theory and has recently been proposed as a framework for extending Bayes' rule to quantum information theory.

arXiv AI
Jul 8

Lean-Quantum: Toward AI-Assisted Formalization of Quantum Information

arXiv:2607. 05492v1 Announce Type: cross Abstract: Quantum information theory is built on entropic quantities; among them, the sandwiched R\'enyi relative entropy is a fundamental divergence with various applications, and its data processing inequality (DPI) under quantum channels is a cornerstone result.

By Kazumi Kasaura, Kei Tsukamoto, Kento Mori, Risa Mizuno, Takahiro Namatame, Yuta Oriike, Masaya Taniguchi, Sho Sonoda, Hayata Yamasaki
arXiv Machine Learning
Jun 19

Entropy Estimation in Multi-Qutrit Systems via Variational and Classical Neural Networks

arXiv:2606. 20504v1 Announce Type: cross Abstract: We present a systematic study of von Neumann entropy estimation in multi-qutrit quantum systems using two complementary approaches: variational quantum algorithms (VQAs) and classical convolutional neural networks (CNNs), evaluated using an ideal (noise-free) quantum simulator.

By Sai Sakunthala Guddanti, Anil Prabhakar, Ria Rushin Joseph