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Related papers: Slow Mixing of Quantum Gibbs Samplers

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Systems with long-range interactions display a short-time relaxation towards Quasi Stationary States (QSS) whose lifetime increases with the system size. In the paradigmatic Hamiltonian Mean-field Model (HMF) out-of-equilibrium phase…

Statistical Mechanics · Physics 2016-02-15 Gabriele Martelloni , Gianluca Martelloni , Pierre de Buyl , Duccio Fanelli

It has previously been suggested that small subsystems of closed quantum systems thermalize under some assumptions; however, this has been rigorously shown so far only for systems with very weak interaction between subsystems. In this work,…

Quantum Physics · Physics 2021-03-31 Markus P. Mueller , Emily Adlam , Lluis Masanes , Nathan Wiebe

Probing correlated states of many-body systems is one of the central tasks for quantum simulators and processors. A promising approach to state preparation is to realize desired correlated states as steady states of engineered dissipative…

Quantum Physics · Physics 2025-02-05 Jerome Lloyd , Alexios Michailidis , Xiao Mi , Vadim Smelyanskiy , Dmitry A. Abanin

The preparation of quantum Gibbs state is an essential part of quantum computation and has wide-ranging applications in various areas, including quantum simulation, quantum optimization, and quantum machine learning. In this paper, we…

Quantum Physics · Physics 2021-11-23 Youle Wang , Guangxi Li , Xin Wang

In this work, we study the problems of certifying and learning quantum Ising Hamiltonians. Our main contributions are as follows: Certification of Ising Hamiltonians. We show that certifying an Ising Hamiltonian in normalized Frobenius norm…

Many-body quantum systems with local interactions undergo ``sudden death of entanglement" at high temperatures, whereby thermal states become classical mixtures of product states. We investigate whether symmetry constraints can prevent this…

Quantum Physics · Physics 2025-10-06 Amir-Reza Negari , Leonardo A. Lessa , Subhayan Sahu

It is well understood that many-body systems driven at high frequency heat up only exponentially slowly and exhibit a long prethermalization regime. We prove rigorously that a certain relevant class of systems heat very slowly under weak…

Statistical Mechanics · Physics 2019-11-06 Wojciech De Roeck , Victor Verreet

The Markov property entails the conditional independence structure inherent in Gibbs distributions for general classical Hamiltonians, a feature that plays a crucial role in inference, mixing time analysis, and algorithm design. However,…

Quantum Physics · Physics 2025-04-04 Chi-Fang Chen , Cambyse Rouzé

The Z2 gauge-Higgs model in three dimensions has two different types of phase transition, confinement-deconfinement and Higgs magnetization. Here they are explored through two order parameters, the Coulomb magnetization which is a local…

High Energy Physics - Lattice · Physics 2021-09-13 Michael Grady

The Hidden Markov Model (HMM) is a widely-used statistical model for handling sequential data. However, the presence of missing observations in real-world datasets often complicates the application of the model. The EM algorithm and Gibbs…

Machine Learning · Statistics 2026-01-06 Dongrong Li , Tianwei Yu , Xiaodan Fan

Restricted Boltzmann Machines are a class of undirected graphical models that play a key role in deep learning and unsupervised learning. In this study, we prove a phase transition phenomenon in the mixing time of the Gibbs sampler for a…

Machine Learning · Computer Science 2024-10-14 Youngwoo Kwon , Qian Qin , Guanyang Wang , Yuchen Wei

Given a uniform, frustration-free family of local Lindbladians defined on a quantum lattice spin system in any spatial dimension, we prove a strong exponential convergence in relative entropy of the system to equilibrium under a condition…

Quantum Physics · Physics 2021-06-04 Ángela Capel , Cambyse Rouzé , Daniel Stilck França

This paper provides a stabilizing preparation method for quantum Gaussian states by utilizing continuous measurement. The stochastic evolution of the open quantum system is described in terms of the quantum stochastic master equation. We…

Quantum Physics · Physics 2021-09-28 Liying Bao , Bo Qi , Daoyi Dong

We present quantum algorithms for solving two problems regarding stochastic processes. The first algorithm prepares the thermal Gibbs state of a quantum system and runs in time almost linear in $\sqrt{N \beta/{\cal Z}}$ and polynomial in…

Quantum Physics · Physics 2017-01-11 Anirban Narayan Chowdhury , Rolando D. Somma

This paper considers a non-standard problem of generating samples from a low-temperature Gibbs distribution with \emph{constrained} support, when some of the coordinates of the mode lie on the boundary. These coordinates are referred to as…

Statistics Theory · Mathematics 2026-02-27 Ruixiao Wang , Xiaohong Chen , Sinho Chewi

Conventional methods of quantum simulation involve trade-offs that limit their applicability to specific contexts where their use is optimal. In particular, the interaction picture simulation has been found to provide substantial asymptotic…

Quantum Physics · Physics 2022-08-17 Abhishek Rajput , Alessandro Roggero , Nathan Wiebe

Gibbs sampling, as a model learning method, is known to produce the most accurate results available in a variety of domains, and is a de facto standard in these domains. Yet, it is also well known that Gibbs random walks usually have…

Machine Learning · Statistics 2018-04-20 Mark Kozdoba , Shie Mannor

When a quantum system is placed in thermal environments, we often assume that the system relaxes to the Gibbs state in which decoherence takes place in the system energy eigenbasis. However, when the coupling between the system and the…

Quantum Physics · Physics 2019-12-03 Ketan Goyal , Ryoichi Kawai

We demonstrate that it is possible to construct operators that stabilize the constraint-satisfying subspaces of computational problems in their Ising representations. We provide an explicit recipe to construct unitaries and associated…

Gibbs sampling methods are standard tools to perform posterior inference for mixture models. These have been broadly classified into two categories: marginal and conditional methods. While conditional samplers are more widely applicable…

Methodology · Statistics 2023-02-21 Pierpaolo De Blasi , María F. Gil-Leyva
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