English
Related papers

Related papers: Structural constraint on delayed-choice quantum er…

200 papers

It is demonstrated that 'quantum eraser' (QE) experiments do not erase any information. Nor do they demonstrate retrocausation or 'temporal nonlocality' in their 'delayed choice' form, beyond standard EPR correlations. It is shown that the…

Quantum Physics · Physics 2019-08-06 R. E. Kastner

For two causal structures with the same set of visible variables, one is said to observationally dominate the other if the set of distributions over the visible variables realizable by the first contains the set of distributions over the…

Machine Learning · Statistics 2025-02-24 Marina Maciel Ansanelli , Elie Wolfe , Robert W. Spekkens

Large-scale quantum systems require optical coherence between distant quantum devices, necessitating spectral indistinguishability. Scalable solid-state platforms offer promising routes to this goal. However, environmental disorders,…

Causal structure discovery from observational data is fundamental to the causal understanding of autonomous systems such as medical decision support systems, advertising campaigns and self-driving cars. This is essential to solve well-known…

We present a novel quantum circuit that genuinely implements the Scully-Dr\"uhl-type delayed-choice quantum eraser, where the two recorders of the which-way information directly interact with the signal qubit and remain spatially separated.…

Quantum Physics · Physics 2025-07-15 Bo-Hung Chen , Dah-Wei Chiou , Hsiu-Chuan Hsu

The correlations that can be observed between a set of variables depend on the causal structure underpinning them. Causal structures can be modeled using directed acyclic graphs, where nodes represent variables and edges denote functional…

Quantum Physics · Physics 2015-01-08 Rafael Chaves , Christian Majenz , David Gross

Constraint-based causal discovery methods leverage conditional independence tests to infer causal relationships in a wide variety of applications. Just as the majority of machine learning methods, existing work focuses on studying…

Machine Learning · Statistics 2024-05-27 Siyuan Guo , Viktor Tóth , Bernhard Schölkopf , Ferenc Huszár

Causal disentanglement aims to learn about latent causal factors behind data, holding the promise to augment existing representation learning methods in terms of interpretability and extrapolation. Recent advances establish identifiability…

Machine Learning · Computer Science 2024-12-25 Ryan Welch , Jiaqi Zhang , Caroline Uhler

We analyze statistical consequences of a conjecture that there exists a fundamental (indivisible) quant of time. We study particle dynamics with discrete time. We show that a quantum-like interference pattern could appear as a statistical…

Quantum Physics · Physics 2007-05-23 Andrei Khrennikov , Yaroslav Volovich

We introduce and investigate a simple model of conditional quantum dynamics. It allows for a discussion of the information-theoretic aspects of quantum measurements, decoherence, and environment-induced superselection (einselection).

Quantum Physics · Physics 2017-09-27 W. H. Zurek

In recent years, the development of technologies for causal inference with privacy preservation of distributed data has gained considerable attention. Many existing methods for distributed data focus on resolving the lack of subjects…

Methodology · Statistics 2024-01-18 Yuji Kawamata , Ryoki Motai , Yukihiko Okada , Akira Imakura , Tetsuya Sakurai

Considering the delayed-choice quantum eraser using a Mach-Zehnder interferometer with a nonsymmetric beam splitter, we explicitly demonstrate that it shares exactly the same formal structure with the Einstein-Podolsky-Rosen-Bohm (EPR-Bohm)…

Quantum Physics · Physics 2023-06-07 Dah-Wei Chiou

The conventional explanation of delayed-choice experiments appears to violate our causal intuition at the quantum level. I reanalyze these experiments using time-reversed and time-symmetric formulations of quantum mechanics. The…

Quantum Physics · Physics 2021-01-01 Michael B. Heaney

Observational studies enable causal inferences when randomized controlled trials (RCTs) are not feasible. However, integrating sensitive medical data across multiple institutions introduces significant privacy challenges. The data…

Different approaches in quantifying environmentally-induced decoherence are considered. We identify a measure of decoherence, derived from the density matrix of the system of interest, that quantifies the environmentally induced error,…

Mesoscale and Nanoscale Physics · Physics 2010-10-12 Arkady Fedorov , Leonid Fedichkin , Vladimir Privman

It is known in the context of decentralised control that there exist control strategies consistent with the requirements of a given information structure, yet physically unimplementable through any amount of passive common randomness. This…

Systems and Control · Electrical Eng. & Systems 2023-06-21 Shashank A. Deshpande , Ankur A. Kulkarni

The problem of using observed correlations to infer causal relations is relevant to a wide variety of scientific disciplines. Yet given correlations between just two classical variables, it is impossible to determine whether they arose from…

Recent frameworks describing quantum mechanics in the absence of a global causal order admit the existence of causally indefinite processes, where it is impossible to ascribe causal order for events A and B. These frameworks even allow for…

Quantum Physics · Physics 2021-05-11 Simon Milz , Dominic Jurkschat , Felix A. Pollock , Kavan Modi

We consider linear non-Gaussian structural equation models that involve latent confounding. In this setting, the causal structure is identifiable, but, in general, it is not possible to identify the specific causal effects. Instead, a…

Machine Learning · Statistics 2024-08-12 Daniela Schkoda , Elina Robeva , Mathias Drton

In this paper we present a comprehensive view of prominent causal discovery algorithms, categorized into two main categories (1) assuming acyclic and no latent variables, and (2) allowing both cycles and latent variables, along with…

Artificial Intelligence · Computer Science 2017-09-13 Karamjit Singh , Garima Gupta , Vartika Tewari , Gautam Shroff