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Quantum many-body simulation provides a straightforward way to understand fundamental physics and connect with quantum information applications. However, suffering from exponentially growing Hilbert space size, characterization in terms of…

Quantum many-body systems pose a formidable computational challenge due to the exponential growth of their Hilbert space. While machine learning (ML) has shown promise as an alternative paradigm, most applications remain at the…

Disordered Systems and Neural Networks · Physics 2026-02-03 Yilun Gao , Alberto Rodríguez , Rudolf A. Römer

Machine learning (ML) holds great promise for extracting insights from complex quantum many-body data obtained in quantum experiments. This approach can efficiently solve certain quantum problems that are classically intractable, suggesting…

Quantum Physics · Physics 2025-09-18 Koki Chinzei , Quoc Hoan Tran , Norifumi Matsumoto , Yasuhiro Endo , Hirotaka Oshima

The law of statistical physics dictates that generic closed quantum many-body systems initialized in nonequilibrium will thermalize under their own dynamics. However, the emergence of many-body localization (MBL) owing to the interplay…

We present an introductory review of nonergodic dynamics in interacting many-body quantum systems, focusing on the phenomenon of many-body localization (MBL). We describe aspects of MBL and summarize the evidence for a crossover from the…

Quantum Physics · Physics 2026-04-15 Jakub Zakrzewski

Detecting many-body localization (MBL) typically requires the calculation of high-energy eigenstates using numerical approaches. This study investigates methods that assume the use of a quantum device to detect disorder-induced…

Disordered Systems and Neural Networks · Physics 2022-10-31 Kazue Kudo

Disorder and interactions can lead to the breakdown of statistical mechanics in certain quantum systems, a phenomenon known as many-body localization (MBL). Much of the phenomenology of MBL emerges from the existence of $\ell$-bits, a set…

Disordered Systems and Neural Networks · Physics 2021-05-19 Eli Chertkov , Benjamin Villalonga , Bryan K. Clark

Quantum emulators, owing to their large degree of tunability and control, allow the observation of fine aspects of closed quantum many-body systems, as either the regime where thermalization takes place or when it is halted by the presence…

Investigating many-body localization (MBL) using exact numerical methods is limited by the exponentialgrowth of the Hilbert space. However, localized eigenstates display multifractality and only extend over a vanishing fraction of the…

Disordered Systems and Neural Networks · Physics 2022-01-13 Francesca Pietracaprina , Nicolas Laflorencie

Many-body localization (MBL), characterized by the absence of thermalization and the violation of conventional thermodynamics, has elicited much interest both as a fundamental physical phenomenon and for practical applications in quantum…

Disordered Systems and Neural Networks · Physics 2019-12-17 Pai Peng , Zeyang Li , Haoxiong Yan , Ken Xuan Wei , Paola Cappellaro

Many-body localization (MBL) describes a quantum phase where an isolated interacting system subject to sufficient disorder displays non-ergodic behavior, evading thermal equilibrium that occurs under its own dynamics. Previously, the…

We review methods that allow one to detect and characterise quantum correlations in many-body systems, with a special focus on approaches which are scalable. Namely, those applicable to systems with many degrees of freedom, without…

Quantum Physics · Physics 2023-09-28 Irénée Frérot , Matteo Fadel , Maciej Lewenstein

While many studies point towards the existence of many-body localization (MBL) in one dimension, the fate of higher-dimensional strongly disordered systems is a topic of current debate. The latest experiments as well as several recent…

Disordered Systems and Neural Networks · Physics 2024-05-13 Joey Li , Amos Chan , Thorsten B. Wahl

Variational quantum algorithms (VQAs) represent a promising pathway toward achieving practical quantum advantage on near-term hardware. Despite this promise, for generic, expressive ans\"atze, their scalability is critically hindered by…

Quantum Physics · Physics 2026-02-26 Chenfeng Cao , Yeqing Zhou , Swamit Tannu , Nic Shannon , Robert Joynt

Thermalizing quantum systems are conventionally described by statistical mechanics at equilibrium. However, not all systems fall into this category, with many body localization providing a generic mechanism for thermalization to fail in…

Disordered Systems and Neural Networks · Physics 2019-05-29 Dmitry A. Abanin , Ehud Altman , Immanuel Bloch , Maksym Serbyn

Quantum many-body systems with sufficiently strong disorder can exhibit a non-equilibrium phenomenon, known as the many-body localization (MBL), which is distinct from conventional thermalization. While the MBL regime has been extensively…

We introduce a method to efficiently study the dynamical properties of many-body localized systems in the regime of strong disorder and weak interactions. Our method reproduces qualitatively and quantitatively the real-time evolution with a…

Disordered Systems and Neural Networks · Physics 2019-07-03 Giuseppe De Tomasi , Frank Pollmann , Markus Heyl

When a system thermalizes it loses all local memory of its initial conditions. This is a general feature of open systems and is well described by equilibrium statistical mechanics. Even within a closed (or reversible) quantum system, where…

Many-body localization (MBL) provides a mechanism by which interacting quantum systems evade thermalization, leading to persistent memory of initial conditions and slow entanglement growth. Probing these dynamical signatures in large…

Quantum Physics · Physics 2026-03-16 Kazuma Nagao , Tomonori Shirakawa , Rongyang Sun , Peter Prelovšek , Seiji Yunoki

In contrast with Anderson localization where a genuine localization is observed in real space, the many-body localization (MBL) problem is much less understood in the Hilbert space, support of the eigenstates. In this work, using exact…

Disordered Systems and Neural Networks · Physics 2019-11-06 Nicolas Macé , Fabien Alet , Nicolas Laflorencie
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