English
Related papers

Related papers: Operationally induced preferred basis in unitary q…

200 papers

It is well-known in quantum information theory that a positive operator valued measure (POVM) is the most general kind of quantum measurement. Mathematically, a quantum probability is a normalised POVM, namely a function on certain subsets…

Quantum Physics · Physics 2022-12-06 Kyler S. Johnson , Michael J. Kozdron

Data-driven transformations that reformulate nonlinear systems in a linear framework have the potential to enable the prediction, estimation, and control of strongly nonlinear dynamics using linear systems theory. The Koopman operator has…

Optimization and Control · Mathematics 2021-02-11 Eurika Kaiser , J. Nathan Kutz , Steven L. Brunton

Von Neumann projections are the main operations by which information can be extracted from the quantum to the classical realm. They are however static processes that do not adapt to the states they measure. Advances in the field of adaptive…

Quantum Physics · Physics 2015-10-28 Amine Laghaout , Ulrik L. Andersen

Evaluating joint probabilities of potential outcomes and observed variables, and their linear combinations, is a fundamental challenge in causal inference. This paper addresses the bounding and identification of these probabilities in…

Machine Learning · Statistics 2026-02-24 Naoya Hashimoto , Yuta Kawakami , Jin Tian

We present a mathematical framework based on quantum interval-valued probability measures to study the effect of experimental imperfections and finite precision measurements on defining aspects of quantum mechanics such as contextuality and…

Quantum Physics · Physics 2018-05-30 Yu-Tsung Tai , Andrew J. Hanson , Gerardo Ortiz , Amr Sabry

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…

We investigate the bounding problem of causal effects in experimental studies in which the outcome is truncated by death, meaning that the subject dies before the outcome can be measured. Causal effects cannot be point identified without…

Methodology · Statistics 2024-04-29 Aixian Chen , Xia Cui , Guangren Yang

The quantum mechanics postulate called the Born Rule attributes a probabilistic meaning to a wave function. This paper derives the Born Rule from other quantum principles along with a model of the measurement process. The nondeterministic…

Quantum Physics · Physics 2025-12-22 Alan Schaum

Probabilities enter quantum mechanics via Born's rule, the uniqueness of which was proven by Gleason. Busch subsequently relaxed the assumptions of this proof, expanding its domain of applicability in the process. Extending this work to…

Quantum Physics · Physics 2017-04-25 Kieran Flatt , Stephen M. Barnett , Sarah Croke

It is shown that the operator space generated by peripheral eigenvectors of a unital completely positive map on a von Neumann algebra has a $C^*$-algebra structure. This extends the notion of non-commutative Poisson boundary by including…

Operator Algebras · Mathematics 2024-05-24 B. V. Rajarama Bhat , Samir Kar , Bharat Talwar

This paper investigates system identification problems with Gaussian inputs and quantized observations under fixed thresholds. By reinterpreting the nonlinear effects induced by quantization as the product of the unknown parameter and an…

Optimization and Control · Mathematics 2025-10-20 Xingrui Liu , Ying Wang , Yanlong Zhao

Starting with the first-order singular Lagrangian, the canonical structures of the noncommutative quantum system on a submanifold embedded in the higher-dimensional Euclidean space are investigated with the projection operator method (POM)…

High Energy Physics - Theory · Physics 2015-03-24 M. Nakamura

I argue that the rules of unitary quantum mechanics imply that observers who will themselves be subject to measurements in a linear combination of macroscopic states (``cat" measurements) cannot make reliable predictions on the results of…

Quantum Physics · Physics 2024-05-22 Alexios P. Polychronakos

Sequential experimental design to discover interventions that achieve a desired outcome is a key problem in various domains including science, engineering and public policy. When the space of possible interventions is large, making an…

Machine Learning · Computer Science 2023-08-17 Jiaqi Zhang , Louis Cammarata , Chandler Squires , Themistoklis P. Sapsis , Caroline Uhler

In quantum mechanics the statistics of the outcomes of a measuring apparatus is described by a positive operator valued measure (POVM). A quantum channel transforms POVM's into POVM's, generally irreversibly, thus loosing some of the…

Quantum Physics · Physics 2007-05-23 F. Buscemi , G. M. D'Ariano , M. Keyl , P. Perinotti , R. Werner

Projective measurement is used as a fundamental axiom in quantum mechanics, even though it is discontinuous and cannot predict which measured operator eigenstate will be observed in which experimental run. The probabilistic Born rule gives…

Quantum Physics · Physics 2017-08-09 Apoorva Patel , Parveen Kumar

We characterize a value of an observable by a `sum rule' for generally non-commuting observables and a `product rule' when restricted to a maximal commuting subalgebra of observables together with the requirement that the value is unity for…

Quantum Physics · Physics 2011-09-28 Akio Hosoya , Minoru Koga

In a quantum-Bayesian take on quantum mechanics, the Born Rule cannot be interpreted as a rule for setting measurement-outcome probabilities from an objective quantum state. But if not, what is the role of the rule? In this paper, we argue…

Quantum Physics · Physics 2009-06-12 Christopher A. Fuchs , Ruediger Schack

Computationalism provides a framework for understanding how a mathematically describable physical world could give rise to conscious observations without the need for dualism. A criterion is proposed for the implementation of computations…

Quantum Physics · Physics 2007-09-06 Jacques Mallah

Bayesian Optimization (BO) methods are useful for optimizing functions that are expen- sive to evaluate, lack an analytical expression and whose evaluations can be contaminated by noise. These methods rely on a probabilistic model of the…

Machine Learning · Statistics 2020-02-04 Eduardo C. Garrido-Merchán , Daniel Hernández-Lobato
‹ Prev 1 8 9 10 Next ›