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In the context of chaotic quantum many-body systems, we show that operator growth, as diagnosed by out-of-time-order correlators of local operators, also leaves a sharp imprint in out-of-time-order correlators of global operators. In…

Quantum Physics · Physics 2023-07-19 Tianci Zhou , Brian Swingle

Despite the fact that power-law interactions occur in a plethora of physical systems, their many-body dynamics is far less understood than that of nearest-neighbor interacting systems. Here, we study information scrambling in strongly…

This thesis makes considerable contributions to the realm of machine learning, specifically in the context of open-world scenarios where systems face previously unseen data and contexts. Traditional machine learning models are usually…

Machine Learning · Computer Science 2023-10-11 Yiyou Sun

Non-classical correlations can be regarded as resources for quantum information processing. However, the classification problem of non-classical correlations for quantum states remains a challenge, even for finite-size systems. Although…

Scrambling, a process in which quantum information spreads over a complex quantum system becoming inaccessible to simple probes, happens in generic chaotic quantum many-body systems, ranging from spin chains, to metals, even to black holes.…

Quantum Physics · Physics 2020-02-10 Shenglong Xu , Brian Swingle

Quantum regression theorem is a very useful result in open quantum system and extensively used for computing multi-point correlation functions. Traditionally it is derived for two-time correlators in the Markovian limit employing the…

Quantum Physics · Physics 2022-09-14 Sakil Khan , Bijay Kumar Agarwalla , Sachin Jain

Discretizing the $\lambda \phi^4$ scalar field theory on a lattice yields a system of coupled anharmonic oscillators with quadratic and quartic potentials. We begin by analyzing the two coupled oscillators in the second quantization method…

High Energy Physics - Theory · Physics 2026-05-12 Wung-Hong Huang

Quantum nonlinear spectroscopy (QNS) via a quantum sensor can access $2^{n-1}$ types of $n$-th order contour-time-ordered correlations (CTOCs) arising from different orderings of quantum operators, while classical nonlinear spectroscopy can…

Quantum Physics · Physics 2026-02-10 Li Sun , Chong Chen , Ren-Bao Liu

Non-KAM (Kolmogorov-Arnold-Moser) systems, when perturbed by weak time-dependent fields, offer a fast route to classical chaos through an abrupt breaking of invariant phase space tori. In this work, we employ out-of-time-order correlators…

Chaotic Dynamics · Physics 2024-01-10 Naga Dileep Varikuti , Abinash Sahu , Arul Lakshminarayan , Vaibhav Madhok

We explore quantum phase transitions using two probes of quantum chaos: out-of-time-order correlators (OTOCs) and the $r$-parameter obtained from the level spacing statistics. In particular, we address $p$-spin models associated with…

High Energy Physics - Theory · Physics 2021-09-01 Kyoung-Bum Huh , Kazuki Ikeda , Viktor Jahnke , Keun-Young Kim

The growth of information scrambling, captured by out-of-time-order correlation functions (OTOCs), is a central indicator of the nature of many-body quantum dynamics. Here, we compute analytically the complete time dependence of the OTOC…

High Energy Physics - Theory · Physics 2025-11-21 Antonio M. García-García , Chang Liu , Lucas Sá , Jacobus J. M. Verbaarschot , Jie-ping Zheng

We study universal chaotic dynamics of a large class of periodically driven critical systems described by spatially inhomogeneous conformal field theories. By employing an effective curved spacetime approach, we show that the onset of…

Strongly Correlated Electrons · Physics 2025-09-24 Bastien Lapierre , Tokiro Numasawa , Titus Neupert , Shinsei Ryu

In many-body localized systems, propagation of information forms a light cone that grows logarithmically with time. However, local changes in energy or other conserved quantities typically spread only within a finite distance. Is it…

Disordered Systems and Neural Networks · Physics 2019-08-07 Yichen Huang , Yong-Liang Zhang , Xie Chen

We report a numerical observation where the infinite-temperature out-of-time-order correlators (OTOCs) directly probe quantum phase transitions at zero temperature, in contrast to common intuition where low energy quantum effects are washed…

Quantum Physics · Physics 2020-04-21 Ceren B. Dağ , L. -M. Duan , Kai Sun

There is a remarkable interest in the study of Out-of-time ordered correlators (OTOCs) that goes from many body theory and high energy physics to quantum chaos. In this latter case there is a special focus on the comparison with the…

Quantum Physics · Physics 2019-10-30 Pablo D. Bergamasco , Gabriel G. Carlo , Alejandro M. F. Rivas

This paper investigates the transverse Ising model on a discretization of two-dimensional anti-de Sitter space. We use classical and quantum algorithms to simulate real-time evolution and measure out-of-time-ordered correlators (OTOC). The…

Quantum Physics · Physics 2023-12-05 Muhammad Asaduzzaman , Simon Catterall , Yannick Meurice , Goksu Can Toga

Out-of-time-order correlators (OTOC), vigorously being explored as a measure of quantum chaos and information scrambling, is studied here in the natural and simplest multi-particle context of bipartite systems. We show that two strongly…

Quantum Physics · Physics 2020-03-18 Ravi Prakash , Arul Lakshminarayan

The field of information scrambling has seen significant growth over the last decade, where the out-of-time-ordered correlator (OTOC) has emerged as a prominent tool to probe it. In this work, we use bipartite OTOC, a particular form of…

Quantum Physics · Physics 2024-09-17 Baibhab Bose , Devvrat Tiwari , Subhashish Banerjee

The out-of-time order correlator (OTOC) has been widely studied in closed quantum systems. However, there are very few studies for open systems and they are mainly focused on isolating the effects of scrambling from those of decoherence.…

Quantum Physics · Physics 2023-08-09 Pablo D. Bergamasco , Gabriel G. Carlo , Alejandro M. F. Rivas

Machine Learning has become a pervasive tool in climate science applications. However, current models fail to address nonstationarity induced by anthropogenic alterations in greenhouse emissions and do not routinely quantify the uncertainty…

Machine Learning · Computer Science 2024-02-22 Simon Dräger , Maike Sonnewald