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Gaussian Processes (GPs) are a powerful tool for probabilistic modeling, but their performance is often constrained in complex, large-scale real-world domains due to the limited expressivity of classical kernels. Quantum computing offers…

Multiagent Systems · Computer Science 2026-05-13 Meet Gandhi , George P. Kontoudis

We analyze Gaussian analytic functions (GAFs) defined as power series with coefficients modeled by discrete stationary Gaussian processes, utilizing their spectral measures. We revisit some limit theorems for random analytic functions and…

Probability · Mathematics 2025-01-08 Tomoyuki Shirai

In this paper we shall introduce the mathematical framework for the description of measurements of quantum processes. Using this framework the process estimation problems can be treated in the similar way as the state estimation problems,…

Quantum Physics · Physics 2009-11-13 Mario Ziman

Non-Gaussian operations in continous variable (CV) quantum key distribution (QKD) have been limited to photon subtraction on squeezed vacuum states only. This is mainly due to the ease of calculating the covariance matrix representation of…

Quantum Physics · Physics 2021-11-24 Jaskaran Singh , Soumyakanti Bose

In this paper we propose a general methodology, based on multiple testing, for testing that the mean of a Gaussian vector in R^n belongs to a convex set. We show that the test achieves its nominal level, and characterize a class of vectors…

Statistics Theory · Mathematics 2007-06-13 Yannick Baraud , Sylvie Huet , Beatrice Laurent

Quantum optical measurement techniques offer a rich avenue for quantum control of mechanical oscillators via cavity optomechanics. In particular, a powerful yet little explored combination utilizes optical measurements to perform heralded…

Algorithmic discrepancy theory seeks efficient algorithms to find those two-colorings of a set that minimize a given measure of coloring imbalance in the set, its {\it discrepancy}. The {\it Euclidean discrepancy} problem and the problem of…

Quantum Physics · Physics 2021-03-17 Jiří Lebl , Asif Shakeel

Gaussian quantum mechanics is a powerful tool regularly used in quantum optics to model linear and quadratic Hamiltonians efficiently. Recent interest in qubit-CV hybrid models has revealed a simple, yet important gap in our knowledge,…

Quantum Physics · Physics 2025-06-19 Nicholas Funai

Bayesian quantum estimation provides a robust framework for quantum technologies, especially in scenarios with limited data and minimal prior information. Yet, its application to continuous-variable Gaussian systems has remained limited and…

Quantum Physics · Physics 2026-05-19 Edward Gandar , Jesús Rubio

In this paper, we explore (2+1)D quantum electrodynamics (QED) at finite density on a quantum computer, including two fermion flavors. Our method employs an efficient gauge-invariant ansatz together with a quantum circuit structure that…

High Energy Physics - Lattice · Physics 2025-09-26 Emil Otis Rosanowski , Arianna Crippa , Lena Funcke , Paulo Vitor Itaborai , Karl Jansen , Simran Singh

Off-diagonal hypervirial relationships, combined with quantum mechanical sum rules of charge-current conservation, offer a way for testing electronic excited-state transition energies and moments, which does not need any external reference.…

Chemical Physics · Physics 2021-02-16 Francesco Ferdinando Summa , Guglielmo Monaco , Riccardo Zanasi , Paolo Lazzeretti

We explore error distributions in Epoch of Reionization 21-cm power spectrum estimators using a combination of mathematical analysis and numerical simulations. We provide closed form solutions for the error distributions of individual bins…

Cosmology and Nongalactic Astrophysics · Physics 2023-04-20 Michael J. Wilensky , Jordan Brown , Bryna J. Hazelton

A representation of polymer self-consistent field theory equivalent to quantum density functional theory is given in terms of non-orthogonal basis sets. Molecular integrals and self-consistent equations for spherically symmetric systems…

Atomic Physics · Physics 2026-02-05 Phil A. LeMaitre , Russell B. Thompson

In this paper, we give an update on divergent problems concerning the radiative corrections of quantum electrodynamics in $(3+1)$ dimensions. In doing so, we introduce a geometric adaptation for the covariant photon propagator by including…

High Energy Physics - Theory · Physics 2022-06-17 David Montenegro

Signal inference problems with non-Gaussian posteriors can be hard to tackle. Through using the concept of Gibbs free energy these posteriors are rephrased as Gaussian posteriors for the price of computing various expectation values with…

Methodology · Statistics 2016-11-18 Reimar H. Leike , Torsten A. Enßlin

In this article, we propose a novel method for sampling potential functions based on noisy observation data of a finite number of observables in quantum canonical ensembles, which leads to the accurate sampling of a wide class of test…

Numerical Analysis · Mathematics 2020-04-08 Ziheng Chen , Zhennan Zhou

In continuous-variable systems, non-Gaussian resources are essential for achieving universal quantum computation that lies beyond classical simulation. Among the candidate states, the cubic phase state stands out as the simplest form of…

Quantum Physics · Physics 2025-09-22 Boxuan Jing , Feng-Xiao Sun , Qiongyi He

We propose a combined mathematical framework of order statistics and random matrix theory for multicarrier continuous-variable (CV) quantum key distribution (QKD). In a multicarrier CVQKD scheme, the information is granulated into Gaussian…

Quantum Physics · Physics 2014-10-31 Laszlo Gyongyosi

We introduce a unified Gaussian quantum operator representation for fermions and bosons. The representation extends existing phase-space methods to Fermi systems as well as the important case of Fermi-Bose mixtures. It enables simulations…

Other Condensed Matter · Physics 2009-11-11 P. D. Drummond , J. F. Corney

Data in many applications follows systems of Ordinary Differential Equations (ODEs). This paper presents a novel algorithmic and symbolic construction for covariance functions of Gaussian Processes (GPs) with realizations strictly following…

Machine Learning · Computer Science 2022-08-29 Andreas Besginow , Markus Lange-Hegermann