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Information causality was proposed as a physical principle to put upper bound on the accessible information gain in a physical bi-partite communication scheme. Intuitively, the information gain cannot be larger than the amount of classical…

Quantum Physics · Physics 2015-07-27 I-Ching Yu , Feng-Li Lin

The principle of `information causality' can be used to derive an upper bound---known as the `Tsirelson bound'---on the strength of quantum mechanical correlations, and has been conjectured to be a foundational principle of nature. To date,…

History and Philosophy of Physics · Physics 2018-11-20 Michael E. Cuffaro

Information causality states that the information obtainable by a receiver cannot be greater than the communication bits from a sender, even if they utilize no-signaling resources. This physical principle successfully explains some…

Quantum Physics · Physics 2017-02-10 Satoshi Ishizaka

The strength of quantum correlations is bounded from above by Tsirelson's bound. We establish a connection between this bound and the fact that correlations between two systems cannot increase under local operations, a property known as the…

Quantum Physics · Physics 2012-07-04 Oscar C. O. Dahlsten , Daniel Lercher , Renato Renner

Information Causality is a physical principle which states that the amount of randomly accessible data over a classical communication channel cannot exceed its capacity, even if the sender and the receiver have access to a source of…

Quantum Physics · Physics 2021-06-09 Nikolai Miklin , Marcin Pawłowski

Quantum mechanics is not the unique no-signaling theory which is endowed with stronger-than-classical correlations, and there exists a broad class of no-signaling theories allowing even stronger-than-quantum correlations. The principle of…

Quantum Physics · Physics 2023-07-07 Yang Xiang , Wei Ren

Why does nature only allow nonlocal correlations up to Tsirelson's bound and not beyond? We construct a channel whose input is statistically independent of its output, but through which communication is nevertheless possible if and only if…

Quantum Physics · Physics 2018-03-02 Avishy Y. Carmi , Daniel Moskovich

Information-theoretic generalization bounds based on the supersample construction are a central tool for algorithm-dependent generalization analysis in the batch i.i.d.~setting. However, existing supersample conditional mutual information…

Machine Learning · Statistics 2026-05-13 Futoshi Futami , Masahiro Fujisawa

Generalized mutual information (GMI) is used to compute achievable rates for fading channels with various types of channel state information at the transmitter (CSIT) and receiver (CSIR). The GMI is based on variations of auxiliary channel…

Information Theory · Computer Science 2023-05-23 Gerhard Kramer

The principle called information causality has been used to deduce Tsirelson's bound. In this paper we derive information causality from monotonicity of divergence and relate it to more basic principles related to measurements on…

Quantum Physics · Physics 2020-02-10 Peter Harremoës

Selective rationalization improves neural network interpretability by identifying a small subset of input features -- the rationale -- that best explains or supports the prediction. A typical rationalization criterion, i.e. maximum mutual…

Machine Learning · Computer Science 2020-03-24 Shiyu Chang , Yang Zhang , Mo Yu , Tommi S. Jaakkola

In this work, we introduce novel information-theoretic generalization bounds using the conditional $f$-information framework, an extension of the traditional conditional mutual information (MI) framework. We provide a generic approach to…

Machine Learning · Statistics 2024-10-31 Ziqiao Wang , Yongyi Mao

Principle of information causality, proposed as a generalization of no signaling principle, has efficiently been applied to outcast beyond quantum correlations as unphysical. In this letter we show that this principle when utilized properly…

We correct claims about lower bounds on mutual information (MI) between real-valued random variables made in A. Kraskov {\it et al.}, Phys. Rev. E {\bf 69}, 066138 (2004). We show that non-trivial lower bounds on MI in terms of linear…

Data Analysis, Statistics and Probability · Physics 2013-05-29 David V. Foster , Peter Grassberger

Meta-learning optimizes an inductive bias---typically in the form of the hyperparameters of a base-learning algorithm---by observing data from a finite number of related tasks. This paper presents an information-theoretic bound on the…

Machine Learning · Computer Science 2021-02-09 Arezou Rezazadeh , Sharu Theresa Jose , Giuseppe Durisi , Osvaldo Simeone

The mutual information is bounded from above by a decreasing affine function of the square of the distance between the input distribution and the set of all capacity-achieving input distributions $\Pi_{\mathcal{A}}$, on small enough…

Information Theory · Computer Science 2025-04-24 Barış Nakiboğlu , Hao-Chung Cheng

The idea that non-local correlations stronger than quantum correlations between two no-signaling systems could theoretically exist is based on an incorrect statistical interpretation of the no-signaling condition. This article shows that…

Quantum Physics · Physics 2021-07-23 Pierre Uzan

Quantum correlations can be stronger than anything achieved by classical systems, yet they are not reaching the limit imposed by relativity. The principle of information causality offers a possible explanation for why the world is quantum…

Quantum Physics · Physics 2014-11-12 Martin Ringbauer , Alessandro Fedrizzi , Dominic W. Berry , Andrew G. White

We present a characterization of the set of non-signaling correlations in terms of a two dimensional representation that involves the maximal value of a Bell functional and the mutual information between the parties. In particular, we apply…

Recent experimental studies on human cognition, particularly where non-separable or entangled cognitive states have been found, show that in many such cases Bell or CHSH inequalities have been violated. The implications are that greater…

General Physics · Physics 2023-04-18 Stuart Kauffman , Sudip Patra
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