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Mapping the Born Rule to the Fractal Geometry of Quantum Paths

Ervin Goldfain

Published Jan 1, 2019• 0 citations
Publisher

Abstract

Here we show that there is an approximate mapping between multifractal theory and the Born rule of Quantum Mechanics. The derivation is based on the fractal geometry of quantum mechanical paths, which replicates the geometry of unrestricted random walks in d 2 Euclidean dimensions.
DOI
10.13140/rg.2.2.30519.88480

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The Born Rule plays a critical role in quantum mechanics (QM) since it supplies the link between the mathematical formalism and experimental results in terms of probabilities. The Born Rule does not occur in ordinary probability theory. Where then does it come from? This has been a topic of considerable controversy in the literature. We take the approach of asking what is the simplest extension of ordinary probability theory where the Born rule appears. This is answered by showing that the Born Rule appears by adding the notion of superposition events (in addition to the ordinary discrete events) to finite probability theory. Hence the rule does not need any physics-based derivation. It is simply a feature of the mathematics of superposition when only superposition events are added to ordinary probability theory.
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It was recently shown that the Born rule for transition probabilities of a microscopic particle can be derived from the linear Schr{\"o}dinger equation with a Hamiltonian represented by random matrices from the Gaussian Unitary Ensemble, taken to be independent at different time points. Under such an evolution, the particle's state undergoes an isotropic random walk on the projective state space, and the relative frequencies of reaching different regions obey the Born rule. In this work, we extend these results to demonstrate how the same framework accounts for the emergence of Newtonian dynamics for macroscopic bodies on a submanifold of state space, thereby providing a unified geometric account of the quantum-classical transition.
This paper investigates the geometry of randomness, specifically focusing on characterizing fractal behavior using quantum simulation. The core claim is to develop a novel quantum simulation technique to unveil the underlying fractal structure of random systems, thereby providing a deeper understanding of their complex dynamics. The method aims to emulate the evolution of a random system through quantum mechanics, allowing us to directly observe and quantify the emergence of fractal patterns. The research will explore the potential of this approach to surpass current approximation methods, offering a fundamentally new pathway to analyze and quantify fractal geometry.
Multifractal dimensions allow for characterizing the localization properties of states in complex quantum systems. For ergodic states the finite-size versions of fractal dimensions converge to unity in the limit of large system size. However, the approach to the limiting behavior is remarkably slow. Thus, an understanding of the scaling and finite-size properties of fractal dimensions is essential. We present such a study for random matrix ensembles, and compare with two chaotic quantum systems-the kicked rotor and a spin chain. For random matrix ensembles we analytically obtain the finite-size dependence of the mean behavior of the multifractal dimensions, which provides a lower bound to the typical (logarithmic) averages. We show that finite statistics has remarkably strong effects, so that even random matrix computations deviate from analytic results (and show strong sample-to-sample variation), such that restoring agreement requires exponentially large sample sizes. For the quantized standard map (kicked rotor) the multifractal dimensions are found to follow the random matrix predictions closely, with the same finite statistics effects. For a XXZ spin-chain we find significant deviations from the random matrix prediction-the large-size scaling follows a system-specific path towards unity. This suggests that local many-body Hamiltonians are "weakly ergodic," in the sense that their eigenfunction statistics deviate from random matrix theory.