The paper introduces Fermi-Dirac thermal measurements as a new framework for quantum hypothesis testing and semidefinite optimization. By treating measurement eigenmodes as independent fermionic modes, the authors show that minimizing fermionic free energy yields optimal measurements whose eigenvalues follow Fermi‑Dirac distributions. These measurements can be learned with classical or hybrid quantum‑classical algorithms, leading to a new quantum machine‑learning model—Fermi‑Dirac machines—and a novel approach to semidefinite optimization on quantum computers.
By Nana Liu, Mark M. Wilde
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:2602. 18364v2 Announce Type: replace-cross Abstract: Maximum likelihood prediction (MLP) is a core task at the heart of modern large language models.
By Sreejith Sreekumar, Nir Weinberger
arXiv:2608. 14941v1 Announce Type: new Abstract: Counting the global optima of a classical optimization problem is a #P-hard task.
By Malay Marut Das, Mark A. Novotny, Yaroslav Koshka
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:2305. 06177v1 Announce Type: cross Abstract: We present a thermodynamic analysis of a quantum engine that uses a single quantum particle as its working fluid, inspired by Szilard's classical single-particle engine.
By Srinivasa Rao. P