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We describe two procedures which, given access to one copy of a quantum state and a sequence of two-outcome measurements, can distinguish between the case that at least one of the measurements accepts the state with high probability, and…

Quantum Physics · Physics 2017-04-18 Aram W. Harrow , Cedric Yen-Yu Lin , Ashley Montanaro

A phenomenological Hamiltonian of a closed (i.e., unitary) quantum system is assumed to have an $N$ by $N$ real-matrix form composed of a unperturbed diagonal-matrix part $H^{(N)}_0$ and of a tridiagonal-matrix perturbation…

Mathematical Physics · Physics 2021-06-01 Miloslav Znojil

We consider quantum dynamics of the order parameter in the discrete pairing model (Richardson model) in thermodynamic equilibrium. The integrable Richardson Hamiltonian is represented as a direct sum of Hamiltonians acting in different…

Superconductivity · Physics 2014-11-20 Victor Galitski

Quantum theory has evolved from a set of provisional rules to an indispensable framework that underlies much of modern technology and infrastructure. Yet, after a century, Born's probability postulate remains at odds with the theory's…

Quantum Physics · Physics 2025-08-19 Lionel Brits

The Born rule, a foundational axiom used to deduce probabilities of events from wavefunctions, is indispensable in the everyday practice of quantum physics. It is also key in the quest to reconcile the ostensibly inconsistent laws of the…

In this work we explore deep generative models of text in which the latent representation of a document is itself drawn from a discrete language model distribution. We formulate a variational auto-encoder for inference in this model and…

Computation and Language · Computer Science 2016-10-17 Yishu Miao , Phil Blunsom

Dynamic discrete choice models are widely employed to answer substantive and policy questions in settings where individuals' current choices have future implications. However, estimation of these models is often computationally intensive…

Methodology · Statistics 2025-04-11 Ebrahim Barzegary , Hema Yoganarasimhan

The work analyzes the stability of the quantum eigenstates when they are submitted to fluctuations by using the stochastic generalization of the Madelung quantum hydrodynamic approach. In the limit of sufficiently slow kinetics, the quantum…

Quantum Physics · Physics 2020-12-01 Simone Chiarelli , Piero Chiarelli

This article presents a concrete mathematical framework for the generation of entangled quantum states from classical stochastic processes. We demonstrate that any density operator $\rho_{AB}$ of a composite system can be derived from the…

Quantum Physics · Physics 2026-01-27 Andrei Khrennikov

Quantum many-body systems realise many different phases of matter characterised by their exotic emergent phenomena. While some simple versions of these properties can occur in systems of free fermions, their occurrence generally implies…

Strongly Correlated Electrons · Physics 2019-10-04 Samuel Spillard , Christopher J. Turner , Konstantinos Meichanetzidis

Representation learning plays a central role in structuring internal embeddings to capture the statistical properties of language, influencing the coherence and contextual consistency of generated text. Statistical Coherence Alignment is…

Computation and Language · Computer Science 2025-08-11 Jonathan Gale , Godfrey Aldington , Harriet Thistlewood , Thomas Tattershall , Basil Wentworth , Vincent Enoasmo

A realist description of our universe requires a twofold concept of locality. On one hand, there are the strictly Einstein-local interactions which generate the time evolution. On the other hand, the quantum state space calls for a…

Quantum Physics · Physics 2013-08-27 Andreas O. Tell

We present models in which the indeterministic feature of Quantum Mechanics is represented in the form of definite physical mechanisms. Our way is completely different from so-called hidden parameter models, namely, we start from a certain…

Quantum Physics · Physics 2007-05-23 Jiri Soucek

A recently proposed class of models attempts to learn latent dynamics from high-dimensional observations, like images, using priors informed by Hamiltonian mechanics. While these models have important potential applications in areas like…

Machine Learning · Statistics 2021-11-12 Irina Higgins , Peter Wirnsberger , Andrew Jaegle , Aleksandar Botev

We present a deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks. Specifically, we employ latent variable models to construct…

Machine Learning · Statistics 2019-06-26 Yibo Yang , Paris Perdikaris

We extend the recent latent recurrent modeling to sequential input streams. By interleaving fast, recurrent latent updates with self-organizational ability between slow observation updates, our method facilitates the learning of stable…

Machine Learning · Computer Science 2026-04-23 Shota Takashiro , Masanori Koyama , Takeru Miyato , Yusuke Iwasawa , Yutaka Matsuo , Kohei Hayashi

A common problem in time series analysis is to predict dynamics with only scalar or partial observations of the underlying dynamical system. For data on a smooth compact manifold, Takens theorem proves a time delayed embedding of the…

Machine Learning · Computer Science 2023-04-12 Charles D. Young , Michael D. Graham

Quantum mechanics has lacked a widely recognized interpretation since its birth. Many of these are still under consideration because interpretations are tough or impossible to disprove experimentally. We show how to distinguish…

Quantum Physics · Physics 2022-12-29 Jakub M. Ratajczak

We derive a closed equation of motion for the current density of an inhomogeneous quantum many-body system under the assumption that the time-dependent wave function can be described as a geometric deformation of the ground-state wave…

Other Condensed Matter · Physics 2015-05-14 Xianlong Gao , Jianmin Tao , G. Vignale , I. V. Tokatly

Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity on locally predictable spans while under-allocating…

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