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Over the past years, machine learning has emerged as a powerful computational tool to tackle complex problems over a broad range of scientific disciplines. In particular, artificial neural networks have been successfully deployed to…

Quantum Physics · Physics 2021-01-28 Juan Carrasquilla , Giacomo Torlai

Non-Hermitian quantum systems exhibit fascinating characteristics such as non-Hermitian topological phenomena and skin effect, yet their studies are limited by the intrinsic difficulties associated with their eigenvalue problems, especially…

Mesoscale and Nanoscale Physics · Physics 2024-01-23 Guang Yang , Yongkang Li , Yongxu Fu , Zhenduo Wang , Yi Zhang

Learning many-body quantum states and quantum phase transitions remains a major challenge in quantum many-body physics. Classical machine learning methods offer certain advantages in addressing these difficulties. In this work, we propose a…

Quantum Physics · Physics 2026-02-03 Xin Li , Zhang-Qi Yin

Experimental quantum simulators have become large and complex enough that discovering new physics from the huge amount of measurement data can be quite challenging, especially when little theoretical understanding of the simulated model is…

Quantum Physics · Physics 2020-12-08 Alexander Lidiak , Zhexuan Gong

The explorations of non-Hermiticity have been devoted to investigate the disorder-induced many-body localization (MBL). However, the sensitivity of the spatial boundary conditions and the interplay of the non-Hermitian skin effect with…

Disordered Systems and Neural Networks · Physics 2022-12-09 Kuldeep Suthar , Yi-Cheng Wang , Yi-Ping Huang , H. H. Jen , Jhih-Shih You

Characterizing quantum many-body phase structure is a major goal for quantum simulation. Here, we employ an unsupervised learning approach based on diffusion maps to learn phase transitions in bosonic lattice systems described by…

Computational Physics · Physics 2026-05-04 Bihui Zhu

Drawing the quantum phase diagram of a many-body system in the parameter space of its Hamiltonian can be seen as a learning problem, which implies labelling the corresponding ground states according to some classification criterium that…

Quantum Physics · Physics 2025-10-17 Mehran Khosrojerdi , Alessandro Cuccoli , Paola Verrucchi , Leonardo Banchi

A lossy quantum system harboring the non-Hermitian skin effect can in certain conditions exhibit anomalously high loss at the boundaries of the system compared to the bulk, a phenomenon termed the non-Hermitian edge burst. We uncover…

Quantum Physics · Physics 2025-03-20 Jin Ming Koh , Wen-Tan Xue , Tommy Tai , Dax Enshan Koh , Ching Hua Lee

The non-Hermitian Hamiltonian describes the effective dynamics of a system coupled to a continuously measured bath, and can exhibit anti-unitary symmetries that give rise to exceptional points and broken phases with complex eigenvalues,…

Quantum Physics · Physics 2025-12-23 Sejal Ahuja , Keshav Das Agarwal , Aditi Sen De

Quantum many-body systems realise many different phases of matter characterised by their exotic emergent phenomena. While some simple versions of these properties can occur in systems of free fermions, their occurrence generally implies…

Strongly Correlated Electrons · Physics 2019-10-04 Samuel Spillard , Christopher J. Turner , Konstantinos Meichanetzidis

Non-Hermitian quantum mechanics with parity-time (PT) symmetry offers a powerful framework for exploring the complex interplay of dissipation and coherent interactions in open quantum systems. While PT-symmetry breaking has been studied in…

Current quantum simulation experiments are starting to explore non-equilibrium many-body dynamics in previously inaccessible regimes in terms of system sizes and time scales. Therefore, the question emerges which observables are best suited…

Quantum Gases · Physics 2022-05-24 A. Bohrdt , S. Kim , A. Lukin , M. Rispoli , R. Schittko , M. Knap , M. Greiner , J. Léonard

Dissipation in open systems enriches the possible symmetries of the Hamiltonians beyond the Hermitian framework allowing the possibility of novel non-Hermitian topological phases, which exhibit long-living end states that are protected…

Quantum Physics · Physics 2023-04-05 Wojciech Brzezicki , Matti Silveri , Marcin Płodzień , Francesco Massel , Timo Hyart

Non-Hermitian (NH) systems provide a fertile platform for quantum technologies, owing in part to their distinct dynamical phases. These systems can be characterized by the preservation or spontaneous breaking of parity-time reversal…

Quantum phase transitions in many-body systems are fundamentally characterized by complex correlation structures, which pose computational challenges for conventional methods in large systems. To address this, we propose a hybrid…

Quantum Physics · Physics 2026-02-03 Jin-Long Chen , Xin Li , Zhang-Qi Yin

The classification of phases and the detection of phase transitions are central and challenging tasks in diverse fields. Within physics, it relies on the identification of order parameters and the analysis of singularities in the free…

Photonic structures have an inherent advantage to realize PT-phase transition through modulating the refractive index or gain-loss. However, quantum PT properties of these photonic systems have not been comprehensively studied yet. Here, in…

Non-Hermitian physics exhibits unique physical properties beyond those of traditional Hermitian systems, such as symmetry breaking, the emergence of exceptional points, topological phase transitions, and more. These phenomena have been…

Quantum Gases · Physics 2026-02-10 Ya-Jun Wang , Jun Zhang , Dong-Sheng Ding

Quantum devices characterized by non-Hermitian topology are predicted to show highly robust and potentially useful properties, but realizing them has remained a daunting experimental task. This is because non-Hermiticity is often associated…

Despite the widespread use and success of machine-learning techniques for detecting phase transitions from data, their working principle and fundamental limits remain elusive. Here, we explain the inner workings and identify potential…

Disordered Systems and Neural Networks · Physics 2023-11-20 Julian Arnold , Niels Lörch , Flemming Holtorf , Frank Schäfer