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We introduce an extension of the time-dependent variational Monte Carlo (tVMC) method that adaptively controls the expressivity of the variational quantum state during the simulation of the dynamics. This adaptive tVMC (atVMC) approach is…

Quantum Physics · Physics 2026-01-09 Raffaele Salioni , Rocco Martinazzo , Davide Emilio Galli , Christian Apostoli

Despite advances in deep probabilistic models, learning discrete latent representations remains challenging. This work introduces a novel method to improve inference in discrete Variational Autoencoders by reframing the inference problem…

Machine Learning · Computer Science 2025-06-11 María Martínez-García , Grace Villacrés , David Mitchell , Pablo M. Olmos

Stochastic quantization in physics has been considered to provide a path integral representation of a probability distribution for Ito processes. It has been indicated that the stochastic quantization can involve a potential term, if the…

Systems and Control · Computer Science 2020-05-05 Masakazu Sano

Interpretable machine learning is rapidly becoming a crucial tool for scientific discovery. Among existing approaches, variational autoencoders (VAEs) have shown promise in extracting the hidden physical features of some input data, with no…

We show convexity of solutions to a class of convex variational problems in the Gauss and in the Wiener space. An important tool in the proof is a representation formula for integral functionals in this infinite dimensional setting, that…

Analysis of PDEs · Mathematics 2012-05-29 Antonin Chambolle , Michael Goldman , Matteo Novaga

Inverse optimization (IO) is used to estimate unknown parameters of an optimization model from observed decisions. In the data-driven context, the estimated parameters are inherently uncertain, yet quantifying this uncertainty has received…

Optimization and Control · Mathematics 2026-05-26 Timothy C. Y. Chan , Nathan Sandholtz , Nasrin Yousefi

Conditional Monte Carlo or pre-integration is a powerful tool for reducing variance and improving the regularity of integrands when using Monte Carlo and quasi-Monte Carlo (QMC) methods. To select the variable to pre-integrate, one must…

Computation · Statistics 2023-07-26 Sifan Liu

Two generalizations of It\^o formula to infinite-dimensional spaces are given. The first one, in Hilbert spaces, extends the classical one by taking advantage of cancellations, when they occur in examples and it is applied to the case of a…

Probability · Mathematics 2016-11-15 Franco Flandoli , Francesco Russo , Giovanni Zanco

The domain of definition of the divergence operator \delta on an abstract Wiener space (W, H, \mu) is extended to include W-valued and W\otimesW-valued "integrands". The main properties and characterizations of this extension are derived…

Probability · Mathematics 2007-12-20 E. Mayer-Wolf , M. Zakai

We study essentially bounded quantum random variables and show that the Gelfand spectrum of such a quantum random variable coincides with the hypoconvex hull of its essential range. Moreover, a notion of operator-valued variance is…

Quantum Physics · Physics 2015-10-07 Douglas Farenick , Michael J. Kozdron , Sarah Plosker

The integration-by-parts formula discovered by Malliavin for the Ito map on Wiener space is proved using the two-parameter stochastic calculus. It is also shown that the solution of a one-parameter stochastic differential equation driven by…

Probability · Mathematics 2009-03-24 J. R. Norris

In his 2005 paper, S.T. Smith proposed an intrinsic Cram\'er-Rao bound on the variance of estimators of a parameter defined on a Riemannian manifold. In the present technical note, we consider the special case where the parameter lives in a…

Systems and Control · Computer Science 2015-09-17 Silvère Bonnabel , Axel Barrau

Complex arithmetic random waves are stationary Gaussian complex-valued solutions of the Helmholtz equation on the two-dimensional flat torus. We use Wiener-It\^o chaotic expansions in order to derive a complete characterization of the…

Probability · Mathematics 2023-02-01 Federico Dalmao , Ivan Nourdin , Giovanni Peccati , Maurizia Rossi

This article gives dual representations for convex integral functionals on the linear space of regular processes. This space turns out to be a Banach space containing many more familiar classes of stochastic processes and its dual can be…

Probability · Mathematics 2017-01-18 Teemu Pennanen , Ari-Pekka Perkkiö

Using the theory of stochastic integration for processes with values in a UMD Banach space developed recently by the authors, an Ito formula is proved which is applied to prove the existence of strong solutions for a class of stochastic…

Probability · Mathematics 2008-04-03 Z. Brzezniak , J. M. A. M. van Neerven , M. C. Veraar , L. Weis

We extend the Ito -to- Stratonovich analysis or quantum stochastic differential equations, introduced by Gardiner and Collett for emission (creation), absorption (annihilation) processes, to include scattering (conservation) processes.…

Mathematical Physics · Physics 2009-11-11 John Gough

The It\^o formula, also known as the change-of-variables formula, is a cornerstone of It\^o stochastic calculus. Over time, this formula has been extended to apply to random processes for which classical calculus is insufficient. Since…

Probability · Mathematics 2025-09-30 Nannan Li , Xing Gao

We emphasize the fact the evolution of quantum states in the inverted oscillator (IO) is reduced to classical equations of motion, stressing that the corresponding tunnelling and reflexion coefficients addressed in the literature are…

Quantum Physics · Physics 2017-02-01 Carla M. Q. Flores

The system of oscillator interacting with vacuum is considered as a problem of random motion of quantum reactive harmonic oscillator (QRHO). It is formulated in terms of a wave functional regarded as complex probability process in the…

Quantum Physics · Physics 2007-05-23 Alexander V. Bogdanov , Ashot S. Gevorkyan

We propose probabilistic representations for inverse Stein operators (i.e. solutions to Stein equations) under general conditions; in particular we deduce new simple expressions for the Stein kernel. These representations allow to deduce…

Probability · Mathematics 2019-06-21 Marie Ernst , Gesine Reinert , Yvik Swan