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$\mathcal{PT}$-symmetric quantum mechanics has been considered an important theoretical framework for understanding physical phenomena in $\mathcal{PT}$-symmetric systems, with a number of $\mathcal{PT}$-symmetry related applications. This…

Quantum Physics · Physics 2019-12-25 Da-Jian Zhang , Qing-hai Wang , Jiangbin Gong

Any repeated use of a fixed experimental instrument is subject to memory effects. We design an estimation method uncovering the details of the underlying interaction between the system and the internal memory without having any experimental…

Quantum Physics · Physics 2015-10-21 Tomas Rybar , Mario Ziman

Symmetry-driven wave physics in open systems, exemplified by parity-time (PT) symmetry, has extended the landscape of crystalline phases in materials science to include gain-loss media. Given the growing interest in engineering disorder for…

Memoryless time evolutions are ubiquitous in nature but often correspond to a resolution-induced approximation, i.e. there are correlations in time whose effects are undetectable. Recent advances in the dynamical control of small quantum…

Symmetry of non-Hermitian matrices underpins many physical phenomena. In particular, chaotic open quantum systems exhibit universal bulk spectral correlations classified on the basis of time-reversal symmetry$^{\dagger}$ (TRS$^{\dagger}$),…

Quantum Physics · Physics 2025-10-29 Anish Kulkarni , Kohei Kawabata , Shinsei Ryu

Gaussian processes provide a flexible, non-parametric framework for the approximation of functions in high-dimensional spaces. The covariance kernel is the main engine of Gaussian processes, incorporating correlations that underpin the…

Machine Learning · Statistics 2024-03-20 Dionissios T. Hristopulos

More than a century after the inception of quantum theory, the question of which traits and phenomena are fundamentally quantum remains under debate. Here we give an answer to this question for temporal processes which are probed…

Memory is the fundamental form of temporal complexity: when present but uncontrollable, it manifests as non-Markovian noise; conversely, if controllable, memory can be a powerful resource for information processing. Memory effects arise…

Quantum Physics · Physics 2024-05-07 Philip Taranto , Marco Túlio Quintino , Mio Murao , Simon Milz

Non-hermitian systems have gained a lot of interest in recent years. However, notions of chaos and localization in such systems have not reached the same level of maturity as in the Hermitian systems. Here, we consider non-hermitian…

Disordered Systems and Neural Networks · Physics 2022-10-19 Soumi Ghosh , Sparsh Gupta , Manas Kulkarni

Discovering causal relations from observational time series without making the stationary assumption is a significant challenge. In practice, this challenge is common in many areas, such as retail sales, transportation systems, and medical…

Machine Learning · Computer Science 2024-07-11 Shanyun Gao , Raghavendra Addanki , Tong Yu , Ryan A. Rossi , Murat Kocaoglu

Temporal interfaces introduced by abrupt switching of the constitutive parameters of unbounded media enable unusual wave phenomena. So far, their explorations have been mostly limited to lossless media. Yet, non-Hermitian phenomena…

Optics · Physics 2021-10-08 Huanan Li , Shixiong Yin , Emanuele Galiffi , Andrea Alù

Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or nonlinearity.…

Methodology · Statistics 2021-09-06 Kang Du , Yu Xiang

Identifying and quantifying memory are often critical steps in developing a mechanistic understanding of stochastic processes. These are particularly challenging and necessary when exploring processes that exhibit long-range correlations.…

Statistical Mechanics · Physics 2016-04-20 Sarah E. Marzen , James P. Crutchfield

The duration, strength and structure of memory effects are crucial properties of physical evolution. Due to the invasive nature of quantum measurement, such properties must be defined with respect to the probing instruments employed. Here,…

Quantum Physics · Physics 2021-06-18 Yu Guo , Philip Taranto , Bi-Heng Liu , Xiao-Min Hu , Yun-Feng Huang , Chuan-Feng Li , Guang-Can Guo

We present a coherent and consistent framework for explicit time-dependence in non-Hermitian quantum mechanics. The area of non-Hermitian quantum mechanics has been growing rapidly over the past twenty years. This has been driven by the…

Quantum Physics · Physics 2020-02-07 Thomas Frith

Causal asymmetry is one of the great surprises in predictive modelling: the memory required to predict the future differs from the memory required to retrodict the past. There is a privileged temporal direction for modelling a stochastic…

Recently, apparent nonphysical implications of non-Hermitian quantum mechanics (NHQM) have been discussed in the literature. In particular, the apparent violation of the no-signaling theorem, discrimination of nonorthogonal states, and the…

Quantum Physics · Physics 2019-12-25 Chia-Yi Ju , Adam Miranowicz , Guang-Yin Chen , Franco Nori

Non-Hermiticity significantly enriches the properties of topological models, leading to exotic features such as the non-Hermitian skin effects and non-Bloch bulk-boundary correspondence that have no counterparts in Hermitian settings. Its…

Mesoscale and Nanoscale Physics · Physics 2022-10-19 Quan Lin , Tianyu Li , Lei Xiao , Kunkun Wang , Wei Yi , Peng Xue

In this work we intend to study a class of time-dependent quantum systems with non-Hermitian Hamiltonians, particularly those whose Hermitian counterpart are important for the comprehension of posed problems in quantum optics and quantum…

Quantum Physics · Physics 2007-05-23 A. de Souza Dutra , M. B. Hott , V. G. C. S dos Santos

Causal inference from observational data following the restricted structural causal models (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or…

Machine Learning · Computer Science 2024-05-30 Kang Du , Yu Xiang