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Irreversibility in quantum measurements is considered from the point of quantum information theory. For that purpose the information transfer between the measured object S and measuring system O is analyzed. It's found that due to the…

Quantum Physics · Physics 2012-05-29 S. N. Mayburov

Excitation energy transfer in light-harvesting aggregates is highly efficient, yet whether quantum coherence plays an operational role in transport remains debated. A central challenge is that coherence is usually inferred from…

Quantum Physics · Physics 2026-03-11 Julia Liebert , Gregory D. Scholes

This paper is concerned with parameter identification problem for finite impulse response (FIR) systems with binary-valued observations under low computational complexity. Most of the existing algorithms under binary-valued observations…

Systems and Control · Electrical Eng. & Systems 2024-12-09 Tianning Han , Ying Wang , Yanlong Zhao

Conventional quantum mechanics with a complex Hilbert space and the Born Rule is derived from five axioms describing properties of probability distributions for the outcome of measurements. Axioms I,II,III are common to quantum mechanics…

Quantum Physics · Physics 2015-05-20 Daniel I. Fivel

Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a key step in applying reinforcement learning to real-world…

Machine Learning · Computer Science 2021-03-11 Yihao Feng , Ziyang Tang , Na Zhang , Qiang Liu

When observed decisions depend only on observed features, off-policy policy evaluation (OPE) methods for sequential decision making problems can estimate the performance of evaluation policies before deploying them. This assumption is…

Machine Learning · Statistics 2020-03-13 Hongseok Namkoong , Ramtin Keramati , Steve Yadlowsky , Emma Brunskill

We study boundary inference at $H=3/4$ for mixed fractional Brownian motion and mixed fractional Ornstein--Uhlenbeck models under high-frequency observation. This boundary is economically important because it separates the critical and…

Statistics Theory · Mathematics 2026-04-03 Chunhao Cai , Yiwu Shang , Weilin Xiao , Cong Zhang

Debiased recommendation with a randomized dataset has shown very promising results in mitigating the system-induced biases. However, it still lacks more theoretical insights or an ideal optimization objective function compared with the…

Information Retrieval · Computer Science 2023-03-22 Dugang Liu , Pengxiang Cheng , Zinan Lin , Xiaolian Zhang , Zhenhua Dong , Rui Zhang , Xiuqiang He , Weike Pan , Zhong Ming

We study the transformed path measure arising from the self-interaction of a three-dimensional Brownian motion via an exponential tilt with the Coulomb energy of the occupation measures of the motion by time $t$. The logarithmic asymptotics…

Probability · Mathematics 2017-10-25 Wolfgang Koenig , Chiranjib Mukherjee

The subjective Bayesian interpretation of probability asserts that the rules of the probability calculus follow from the normative principle of Dutch-book coherence: A decision-making agent should not assign probabilities such that a series…

Quantum Physics · Physics 2022-08-02 John B. DeBrota , Christopher A. Fuchs , Jacques L. Pienaar , Blake C. Stacey

Preferential Bayesian optimization allows optimization of objectives that are either expensive or difficult to measure directly, by relying on a minimal number of comparative evaluations done by a human expert. Generating candidate…

We look at certain thought experiments based upon the 'delayed choice' and 'quantum eraser' interference experiments, which present a complementarity between information gathered from a quantum measurement and interference effects. It has…

Quantum Physics · Physics 2007-05-23 O. J. E. Maroney

Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness. We introduce a novel probabilistic framework for…

Machine Learning · Computer Science 2021-01-07 Wanqian Yang , Lars Lorch , Moritz A. Graule , Himabindu Lakkaraju , Finale Doshi-Velez

We address the problem of optimal experimental design (OED) for Bayesian nonlinear inverse problems governed by PDEs. The goal is to find a placement of sensors, at which experimental data are collected, so as to minimize the uncertainty in…

Optimization and Control · Mathematics 2015-11-04 Alen Alexanderian , Noemi Petra , Georg Stadler , Omar Ghattas

In the past decade, the toolkit of quantum information has been expanded to include processes in which the basic operations do not have definite causal relations. Originally considered in the context of the unification of quantum mechanics…

Quantum Physics · Physics 2024-07-22 Lee A. Rozema , Teodor Strömberg , Huan Cao , Yu Guo , Bi-Heng Liu , Philip Walther

Causal inference often hinges on strong assumptions - such as no unmeasured confounding or perfect compliance - that are rarely satisfied in practice. Partial identification offers a principled alternative: instead of relying on…

Machine Learning · Computer Science 2025-08-20 Tobias Maringgele

Contrary to general relativity, quantum theory treats space and time in fundamentally different ways. In particular, while joint probabilities associated with spacelike separated measurements are defined in terms of the Born rule, joint…

Quantum Physics · Physics 2026-01-13 James Fullwood , Zhihao Ma , Zhen Wu

This paper provides a general method for defining a generalized quantum observable (or POVM) that supplies properly normalized conditional probabilities for the time of occurrence (i.e., of detection). This method treats the time of…

Quantum Physics · Physics 2017-04-25 Thomas Pashby

I argue that the marquis characteristics of the quantum-mechanical double-slit experiment (point detection, random distribution, Born rule) can be explained using Schroedinger's equation alone, if one takes into account that, for any atom…

General Physics · Physics 2019-05-02 Jonathan F. Schonfeld

Predictive algorithms inform consequential decisions in settings with selective labels: outcomes are observed only for units selected by past decision makers. This creates an identification problem under unobserved confounding -- when…

Econometrics · Economics 2025-11-07 Ashesh Rambachan , Amanda Coston , Edward Kennedy