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
The paper studies how to reconstruct a quantum channel from classical data by formulating the fidelity optimization as a semidefinite program (SDP). When the fidelity can be expressed as a ratio of two quadratic forms—such as in mapping mixed to pure states, projective operators, or unitary learning—the SDP approach yields a convex optimization that can be efficiently solved with commercial solvers. Experiments show that the resulting channels often have a Kraus rank far below the maximum, indicating that a small Kraus rank suffices to capture the observed data, and the method is also applied to reconstruct projective operators and a classical computational model based on quantum channel transformation.
By Mikhail Gennadievich Belov, Victor Victorovich Dubov, Vadim Konstantinovich Ivanov, Alexander Yurievich Maslov, Olga Vladimirovna Proshina, Vladislav Gennadievich Malyshkin
arXiv:2607. 25492v2 Announce Type: replace Abstract: We study stochastic optimization with heavy-tailed gradient noise.
By Bin Luo, Chengchang Liu, Jonathan Allcock, Shengyu Zhang, John C. S. Lui
arXiv:2602. 01177v3 Announce Type: replace-cross Abstract: We develop an information-theoretic framework connecting stability, privacy, and generalization for quantum learning algorithms.
By Ayanava Dasgupta, Naqueeb Ahmad Warsi, Masahito Hayashi
arXiv:2602. 14735v2 Announce Type: replace-cross Abstract: The performance of quantum classifiers is typically analyzed through global state distinguishability or the trainability of variational models.
By Ait Haddou Marwan
arXiv:2606. 19947v1 Announce Type: cross Abstract: Reliable quantum control in the presence of decoherence requires policies that combat the effect of environmental noise on the controlled dynamics.
By Merijn Moody, Zier Mensch, Miranda C. N. Cheng, Peter G. Bolhuis, Max Welling
arXiv:2609.00372v1 Announce Type: cross
Abstract: With quantum sensors, simulators and networks emerging, a future of quantum technology may produce quantum states as data---that is, coherently rathe...
By Robin Lorenz, Eric Brunner, Marcello Benedetti