arXiv:2610. 41282v1 Announce Type: cross Abstract: Quantifying correlations is important for understanding how quantum states and channels support information processing.
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arXiv:2602. 18364v2 Announce Type: replace-cross Abstract: Maximum likelihood prediction (MLP) is a core task at the heart of modern large language models.
arXiv:2608. 05110v1 Announce Type: cross Abstract: Near-term quantum hardware limits circuit depth and often imposes geometrically local connectivity for quantum generative models, restricting the output distributions accessible to shallow unitary Born models.
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.
arXiv:2609.25044v1 Announce Type: cross Abstract: This paper presents a quantum semantic communication (QSemCom) framework combining quantum machine learning (QML) and semantic communication (SemCom)...
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.
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.