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Related papers: Occam's Quantum Razor: How Quantum Mechanics can r…

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A comparison of structural features of quantum and classical physical theories, such as the information capacity of systems subject to these theories, requires a common formal framework for the presentation of corresponding concepts (such…

Quantum Physics · Physics 2011-01-04 Paul Busch

The same set of physically motivated axioms can be used to construct both the classical ensemble Hamilton-Jacobi equation and Schrodingers equation. Crucial roles are played by the assumptions of universality and simplicity (Occam's Razor)…

Quantum Physics · Physics 2015-06-26 Rajesh R. Parwani

How can relevant information be extracted from a quantum process? In many situations, only some part of the total information content produced by an information source is useful. Can one then find an efficient encoding, in the sense of…

Quantum Physics · Physics 2016-07-27 Arne L. Grimsmo , Susanne Still

The correspondence principle states that classical mechanics emerges from quantum mechanics in the appropriate limits. However, beyond this heuristic rule, an information-theoretic perspective reveals that classical mechanics is a…

Quantum Physics · Physics 2025-03-12 Krzysztof Sienicki

Quantum computing promises transformational gains for solving some problems, but little to none for others. For anyone hoping to use quantum computers now or in the future, it is important to know which problems will benefit. In this paper,…

Data Structures and Algorithms · Computer Science 2023-10-25 Sukwoong Choi , William S. Moses , Neil Thompson

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 article applies the principle of Occam's Razor to non-parametric model building of statistical data, by finding a model with the minimal number of bits, leading to an exceptionally effective regularization method for probability…

Machine Learning · Statistics 2020-06-18 Peter Kövesarki

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.…

This paper derives an objective Bayesian "prior" based on considerations of entropy/information. By this means, it produces a quantitative measure of goodness of fit (the "H-statistic") that balances higher likelihood against the number of…

Astrophysics · Physics 2008-11-26 Rafael D. Sorkin

Predicting properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description of a quantum state using very few…

Quantum Physics · Physics 2021-04-20 Hsin-Yuan Huang , Richard Kueng , John Preskill

All fields of science depend on mathematical models. Occam's razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can…

Machine Learning · Computer Science 2023-09-08 Botond B Antal , Anthony G Chesebro , Helmut H Strey , Lilianne R Mujica-Parodi , Corey Weistuch

Quantum statistical methods that are commonly used for the derivation of classical thermodynamic properties are extended to classical mechanical properties. The usual assumption that every real motion of a classical mechanical system is…

Quantum Physics · Physics 2015-08-07 Petr Hajicek

Without wasting time and effort on philosophical justifications and implications, we write down the conditions for the Hamiltonian of a quantum system for rendering it mathematically equivalent to a deterministic system. These are the…

Quantum Physics · Physics 2020-06-09 Gerard t Hooft

Although several models have been proposed towards assisting machine learning (ML) tasks with quantum computers, a direct comparison of the expressive power and efficiency of classical versus quantum models for datasets originating from…

Quantum Physics · Physics 2020-01-09 Javier Alcazar , Vicente Leyton-Ortega , Alejandro Perdomo-Ortiz

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently…

Quantum Physics · Physics 2018-05-14 Jacob Biamonte , Peter Wittek , Nicola Pancotti , Patrick Rebentrost , Nathan Wiebe , Seth Lloyd

In the past decade quantum algorithms have been found which outperform the best classical solutions known for certain classical problems as well as the best classical methods known for simulation of certain quantum systems. This suggests…

Quantum Physics · Physics 2007-05-23 David A. Meyer

Occam's Razor tells us to pick the simplest model that fits our observations. In order to make sense of his process mathematically, we interpret it in the context of posets of functions. Our approach leads to some unusual new combinatorial…

Combinatorics · Mathematics 2015-04-29 William Ralph

Faster algorithms, novel cryptographic mechanisms, and alternative methods of communication become possible when the model underlying information and computation changes from a classical mechanical model to a quantum mechanical one. Quantum…

Quantum Physics · Physics 2009-12-29 Eleanor G. Rieffel

In stochastic modeling, the excess entropy -- the mutual information shared between a process's past and future -- represents the fundamental lower bound of the memory needed to simulate its dynamics. However, this bound cannot be saturated…

Quantum Physics · Physics 2026-02-27 Kelvin Onggadinata , Andrew Tanggara , Mile Gu , Dagomir Kaszlikowski

Stochastic models are highly relevant tools in science, engineering, and society. Recent work suggests emerging quantum computing technologies can substantially decrease the memory requirements for simulating stochastic models. Here we show…

Quantum Physics · Physics 2019-06-04 John Realpe-Gómez , Nathan Killoran