A solution of the quantum time of arrival problem via mathematical probability theory
- URL: http://arxiv.org/abs/2508.11368v1
- Date: Fri, 15 Aug 2025 10:02:52 GMT
- Title: A solution of the quantum time of arrival problem via mathematical probability theory
- Authors: Maik Reddiger,
- Abstract summary: Time of arrival refers to the time a particle takes after emission to impinge upon a suitably idealized detector surface.<n>No generally accepted solution exists so far for the corresponding probability distribution of arrival times.<n>We construct the ideal detector model via mathematical probability theory.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Time of arrival refers to the time a particle takes after emission to impinge upon a suitably idealized detector surface. Within quantum theory, no generally accepted solution exists so far for the corresponding probability distribution of arrival times. In this work we derive a general solution for a single body without spin impacting on a so called ideal detector in the absence of any other forces or obstacles. A solution of the so called screen problem for this case is also given. We construct the ideal detector model via mathematical probability theory, which in turn suggests an adaption of the Madelung equations in this instance. This detector model assures that the probability flux through the detector surface is always positive, so that the corresponding distributions can be derived via an approach originally suggested by Daumer, D\"urr, Goldstein, and Zangh\`i. The resulting dynamical model is, strictly speaking, not compatible with quantum mechanics, yet it is well-described within geometric quantum theory. Geometric quantum theory is a novel adaption of quantum mechanics, which makes the latter consistent with mathematical probability theory. Implications to the general theory of measurement and avenues for future research are also provided. Future mathematical work should focus on finding an appropriate distributional formulation of the evolution equations and studying the well-posedness of the corresponding Cauchy problem.
Related papers
- Generative modeling using evolved quantum Boltzmann machines [13.706331473063882]
Born-rule generative modeling is a central task in quantum machine learning.<n>I propose a practical solution that trains quantum Boltzmann machines for Born-rule generative modeling.
arXiv Detail & Related papers (2025-12-02T12:56:02Z) - An Analytic Theory of Quantum Imaginary Time Evolution [12.82619168949495]
Quantum imaginary time evolution (QITE) algorithm is one of the most promising variational quantum algorithms (VQAs)<n>Here, we show that QITE can be interpreted as a form of a general VQA trained with Quantum Natural Gradient Descent (QNGD)<n>We prove that QITE always converges faster than GD-based VQA, though this advantage is suppressed by the exponential growth of Hilbert space dimension.
arXiv Detail & Related papers (2025-10-26T01:43:55Z) - Many Retrocausal Worlds: A Foundation for Quantum Probability [1.5229257192293202]
Recent accounts of probability in the many worlds interpretation of quantum mechanics are vulnerable due to their dependence on probability theory per se.<n>I argue that self-locating probabilities centered in time-extended worlds can solve the incoherence problem.<n>I then outline a time-symmetric version of quantum mechanics - the Fixed Point Formulation - which, interpreted within a time-symmetric Everettian framework, can provide the foundation for a theory of quantum probability.
arXiv Detail & Related papers (2025-10-02T19:21:08Z) - An analysis of Wigner's friend in the framework of quantum mechanics based on the principle of typicality [0.0]
We make an analysis of the Wigner's friend paradox in the framework of quantum mechanics based on the principle of typicality.<n>We also make a prediction, which is testable in principle, about its variant proposed by Deutsch.
arXiv Detail & Related papers (2025-09-09T15:06:58Z) - A Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrodinger Equation [8.648660469053342]
A central problem in quantum mechanics involves solving the Electronic Schrodinger Equation for a molecule or material.<n>We propose a solution via an ansatz which is cheap to sample from, yet satisfies the requisite quantum mechanical properties.
arXiv Detail & Related papers (2024-05-28T15:42:15Z) - On the applicability of Kolmogorov's theory of probability to the description of quantum phenomena. Part I: Foundations [0.0]
It is regarded a generalization of the "classical probability theory" due to Kolmogorov.<n>This work argues in favor of the latter position.<n>It shows how to construct a mathematically rigorous theory for non-relativistic $N$-body quantum systems.
arXiv Detail & Related papers (2024-05-09T12:11:28Z) - Testing trajectory-based determinism via time probability distributions [41.99844472131922]
We introduce a prescription for constructing an arrival-time probability distribution within generic trajectory-equipped theories.<n>We derive a conditional probability distribution that is unreachable by quantum mechanics.<n>Our results can be tested experimentally, thereby assessing the validity of trajectory-based determinism.
arXiv Detail & Related papers (2024-04-15T11:36:38Z) - Quantum Conformal Prediction for Reliable Uncertainty Quantification in
Quantum Machine Learning [47.991114317813555]
Quantum models implement implicit probabilistic predictors that produce multiple random decisions for each input through measurement shots.
This paper proposes to leverage such randomness to define prediction sets for both classification and regression that provably capture the uncertainty of the model.
arXiv Detail & Related papers (2023-04-06T22:05:21Z) - Fourier-Flow model generating Feynman paths [21.67472055005712]
Feynman path integrals generalize the classical action principle to a probabilistic perspective.
The underlying difficulty is to tackle the whole path manifold from finite samples.
Modern generative models in machine learning can handle learning and representing probability distribution.
arXiv Detail & Related papers (2022-11-07T11:31:40Z) - Quantum dynamics corresponding to chaotic BKL scenario [62.997667081978825]
Quantization smears the gravitational singularity avoiding its localization in the configuration space.
Results suggest that the generic singularity of general relativity can be avoided at quantum level.
arXiv Detail & Related papers (2022-04-24T13:32:45Z) - Quasiprobability fluctuation theorem behind the spread of quantum information [10.640597124563614]
We theoretically uncover the quantum fluctuation theorem behind the informational inequality.
The fluctuation theorem quantitatively predicts the statistics of the underlying quantum process.
We experimentally apply an interference-based method to measure the amplitudes composing the quasiprobability.
arXiv Detail & Related papers (2022-01-02T17:45:50Z) - Generalized Probabilistic Theories in a New Light [0.0]
A new answer to the question of why our universe is quantum mechanical rather than classical will be presented.
This paper shows that there is still a possibility that there might be a deterministic level from which our universe emerges.
arXiv Detail & Related papers (2021-03-08T21:28:19Z) - Towards a Probabilistic Foundation of Relativistic Quantum Theory: The One-Body Born Rule in Curved Spacetime [0.0]
This work is based on generalizing the quantum-mechanical Born rule for determining particle position probabilities to curved spacetime.
A principal motivator for this research has been to overcome internal mathematical problems of quantum field theory.
The main contribution of this work to the mathematical physics literature is the development of the Lagrangian picture.
arXiv Detail & Related papers (2020-12-09T18:22:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.