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We consider an analytic way to make the interacting N-body problem tractable by using harmonic oscillators in place of the relevant two-body interactions. The two body terms of the N-body Hamiltonian are approximated by considering the…

Quantum Gases · Physics 2012-05-16 J. R. Armstrong , N. T. Zinner , D. V. Fedorov , A. S. Jensen

We present a systematic derivation of the Heisenberg evolution of a trilinear bosonic Hamiltonian system in presence of a strong drive beyond the standard approximation of a classical, undepleted driving field. We employ a perturbative…

Quantum Physics · Physics 2026-04-01 Hanzhong Zhang , Avi Pe'er

The Poisson-Boltzmann equation is widely used to model molecular electrostatics; however, it is usually solved in linearised form because the sinh nonlinearity is challenging, limiting its applicability in highly charged systems such as…

Computational Physics · Physics 2026-04-20 Mauricio Guerrero-Montero , Michal Bosy , Christopher D. Cooper

We construct a Hamiltonian for a quantum-mechanical model of nonrelativistic particles in three dimensions interacting via the creation and annihilation of a second type of nonrelativistic particles, which are bosons. The interaction…

Mathematical Physics · Physics 2020-01-08 Jonas Lampart

Recently, a class of efficient spectral Monte-Carlo methods was developed in \cite{Feng2025ExponentiallyAS} for solving fractional Poisson equations. These methods fully consider the low regularity of the solution near boundaries and…

Numerical Analysis · Mathematics 2025-10-07 Lisen Ding , Mingyi Wang , Dongling Wang

Automatic amortized resource analysis (AARA) is a type-based technique for inferring concrete (non-asymptotic) bounds on a program's resource usage. Existing work on AARA has focused on bounds that are polynomial in the sizes of the inputs.…

Programming Languages · Computer Science 2020-03-09 David M Kahn , Jan Hoffmann

We consider a framework for the construction of iterative schemes for operator equations that combine low-rank approximation in tensor formats and adaptive approximation in a basis. Under fairly general assumptions, we obtain a rigorous…

Numerical Analysis · Mathematics 2014-03-17 Markus Bachmayr , Wolfgang Dahmen

We construct a new model for relativistic particle on the noncommutative surface in $(2+1)$ dimensions, using the symplectic formalism of constrained systems and embedding the model on an extended phase space. We suggest a short cut to…

High Energy Physics - Theory · Physics 2015-07-10 Salman Abarghouei Nejad , Mehdi Dehghani , Majid Monemzadeh

Continuous phase estimation is known to be superior in accuracy as compared to static estimation. The estimation process is, however, desired to be made robust to uncertainties in the underlying parameters. Here, homodyne phase estimation…

Optimization and Control · Mathematics 2015-09-14 Shibdas Roy , Obaid Ur Rehman , Ian R. Petersen , Elanor H. Huntington

Extensions of the fractional Brownian fields are constructed over a complete Riemannian manifold. This construction is carried out for the full range of the Hurst parameter $\alpha\in(0,1)$. In particular, we establish existence,…

Probability · Mathematics 2013-02-19 Zachary Gelbaum

In this thesis we study adaptive nonparametric regression with noise misspecification and the complexity of approximation of random fields in dependence of the dimension. First, we consider the problem of pointwise estimation in…

Statistics Theory · Mathematics 2012-08-15 Nora Serdyukova

Considerable attention has been paid to the study of the quantum geometry of nonrotating black holes within the framework of Loop Quantum Cosmology. This interest has been reinvigorated since the introduction of a novel effective model by…

General Relativity and Quantum Cosmology · Physics 2023-11-03 Beatriz Elizaga Navascués , Guillermo A. Mena Marugán , Andrés Mínguez Sánchez

For processes with gauge bosons in the final state we show how to continuously connect with a single Born-improved amplitude the resonant region, where resummation effects are important, with the asymptotic region far away from the…

High Energy Physics - Phenomenology · Physics 2009-10-31 Joannis Papavassiliou

We discuss that in the random phase approximation (RPA) the first derivative of the energy with respect to the Green's function is the self-energy in the GW approximation. This relationship allows us to derive compact equations for the RPA…

Chemical Physics · Physics 2017-03-22 Benjamin Ramberger , Tobias Schäfer , Georg Kresse

The coherent potential approximation (CPA) is extended to describe satisfactorily the motion of particles in a random potential which is spatially correlated and smoothly varying. In contrast to existing cluster-CPA methods, the present…

Disordered Systems and Neural Networks · Physics 2009-10-20 Roland Zimmermann , Christoph Schindler

We recently demonstrated a connection between the random phase approximation (RPA) and coupled cluster theory [J. Chem. Phys. 129, 231101 (2008)]. Based on this result, we here propose and test a simple scheme for introducing long-range RPA…

Materials Science · Physics 2009-11-13 Benjamin G. Janesko , Thomas M. Henderson , Gustavo E. Scuseria

Iterative phase estimation has long been used in quantum computing to estimate Hamiltonian eigenvalues. This is done by applying many repetitions of the same fundamental simulation circuit to an initial state, and using statistical…

Quantum Physics · Physics 2019-07-25 Ian D. Kivlichan , Christopher E. Granade , Nathan Wiebe

We consider the problem of self-adjoint extension of Hamilton operators for charged quantum particles in the pure Aharonov-Bohm potential (infinitely thin solenoid). We present a pragmatic approach to the problem based on the…

Quantum Physics · Physics 2016-09-08 Juergen Audretsch , Ulf Jasper , Vladimir D. Skarzhinsky

A variational approach to finite connectivity spin-glass-like models is developed and applied to describe the structure of optimal solutions in random satisfiability problems. Our variational scheme accurately reproduces the known replica…

Disordered Systems and Neural Networks · Physics 2009-10-31 Giulio Biroli , Remi Monasson , Martin Weigt

The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty. Existing…

Machine Learning · Statistics 2017-03-02 Kurt Cutajar , Edwin V. Bonilla , Pietro Michiardi , Maurizio Filippone