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Related papers: Causal Emergence in Quantum Mechanics

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Machine learning techniques are now routinely encountered in research laboratories across the globe. Impressive progress has been made through ML and AI techniques with regards to large data set processing. This progress has increased the…

Machine Learning · Computer Science 2026-02-27 Ilya Balabin , Thomas M. Kaiser

Recent advances in deep learning (DL) have prompted the development of high-performing early warning score (EWS) systems, predicting clinical deteriorations such as acute kidney injury, acute myocardial infarction, or circulatory failure.…

Machine Learning · Computer Science 2025-02-05 Yuxiao Cheng , Xinxin Song , Ziqian Wang , Qin Zhong , Kunlun He , Jinli Suo

We show, via explicit computation on a constrained bosonic model, that the presence of subsystem symmetries can lead to a quantum phase transition (QPT) where the critical point exhibits an emergent enhanced symmetry. Such a transition…

Strongly Correlated Electrons · Physics 2025-08-27 Anirudha Menon , Anwesha Chattopadhyay , K. Sengupta , Arnab Sen

To draw scientifically meaningful conclusions and build reliable models of quantitative phenomena, cause and effect must be taken into consideration (either implicitly or explicitly). This is particularly challenging when the measurements…

Machine Learning · Computer Science 2020-12-11 Max A. Little , Reham Badawy

Acausal features of quantum electrodynamic processes are discussed. While these processes are not present for the classical electrodynamic theory, in the quantum electrodynamic theory, acausal processes are well known to exist. For example,…

Quantum Physics · Physics 2007-05-23 A. Widom , Y. N. Srivastava , E. Sassaroli

Causal discovery aims to learn causal relationships between variables from targeted data, making it a fundamental task in machine learning. However, causal discovery algorithms often rely on unverifiable causal assumptions, which are…

Machine Learning · Computer Science 2025-10-15 Huiyang Yi , Yanyan He , Duxin Chen , Mingyu Kang , He Wang , Wenwu Yu

Non-perturbative theories of quantum gravity inevitably include configurations that fail to resemble physically reasonable spacetimes at large scales. Often, these configurations are entropically dominant and pose an obstacle to obtaining…

General Relativity and Quantum Cosmology · Physics 2008-11-26 Graham Brightwell , Joe Henson , Sumati Surya

Atomic three-grating Mach-Zehnder interferometry constitutes an important tool to probe fundamental aspects of the quantum theory. There is, however, a remarkable gap in the literature between the oversimplified models and robust numerical…

Atomic Physics · Physics 2015-01-08 A. S. Sanz , M. Davidovic , M. Bozic

The consistent histories formalism can be used to describe histories comprised of events across many systems, times, and places, plausibly rich enough to describe our experiences of the classical world; however, many consistent history sets…

Quantum Physics · Physics 2026-05-11 Nick Ormrod , Tein van der Lugt , Yìlè Yīng , Jarosław K. Korbicz

The formation of new phases close to itinerant electron quantum critical points has been observed experimentally in many compounds. We present a unified analytical model that explains the emergence of new types of order around itinerant…

Strongly Correlated Electrons · Physics 2012-04-09 Una Karahasanovic , Frank Krüger , Andrew G. Green

In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal inference, and go on to discuss…

Statistics Theory · Mathematics 2016-07-25 Edward H. Kennedy

We explain the quantum structure as due to the presence of two effects, (a) a real change of state of the entity under influence of the measurement and, (b) a lack of knowledge about a deeper deterministic reality of the measurement…

Quantum Physics · Physics 2015-06-26 Diederik Aerts

Quantum decoherence, which appears when a system interacts with its environment in an irreversible way, plays a fundamental role in the description of quantum-to-classical transitions and has been successfully applied in some important…

General Relativity and Quantum Cosmology · Physics 2015-03-17 Jieci Wang , Jiliang Jing

In real-life statistical data, it seems that conditional probabilities for the effect given their causes tend to be less complex and smoother than conditionals for causes, given their effects. We have recently proposed and tested methods…

Statistical Mechanics · Physics 2009-01-30 Dominik Janzing

This work extends causal inference with stochastic confounders. We propose a new approach to variational estimation for causal inference based on a representer theorem with a random input space. We estimate causal effects involving latent…

Machine Learning · Statistics 2021-01-26 Thanh Vinh Vo , Pengfei Wei , Wicher Bergsma , Tze-Yun Leong

A most simple theoretical argument is given in order to explain the quantitative estimate of the effect of collisional decoherence in matter-wave interferometry. The argument highlights the relevance of quantum and classical features in the…

Quantum Physics · Physics 2007-05-23 B. Vacchini

Quantum measurements of physical quantities are usually described as ideal measurements. However, only a few measurements fulfil the conditions of ideal measurements. The aim of the present work is to describe real position measurements…

Quantum Physics · Physics 2019-01-17 Klaus Wick

We introduce a qubit-based version of the quantum switch, consisting in a variation of the Fermi problem. Two qubits start in a superposition state where one qubit is excited and the other in the ground state, but it is undefined which is…

Quantum Physics · Physics 2022-05-10 Carlos Sabín

Fast moving classical variables can generate quantum mechanical behavior. We demonstrate how this can happen in a model. The key point is that in classically (ontologically) evolving systems one can still define a conserved quantum energy.…

Quantum Physics · Physics 2021-09-02 Gerard t Hooft

A widely applied approach to causal inference from a non-experimental time series $X$, often referred to as "(linear) Granger causal analysis", is to regress present on past and interpret the regression matrix $\hat{B}$ causally. However,…

Machine Learning · Statistics 2015-12-23 Philipp Geiger , Kun Zhang , Mingming Gong , Dominik Janzing , Bernhard Schölkopf