Probability density functions of quantum mechanical observable
uncertainties
- URL: http://arxiv.org/abs/2205.03193v1
- Date: Fri, 6 May 2022 13:17:38 GMT
- Title: Probability density functions of quantum mechanical observable
uncertainties
- Authors: Lin Zhang and Jinping Huang and Jiamei Wang and Shao-Ming Fei
- Abstract summary: We derive analytically the probability density functions (PDFs) of the uncertainties of arbitrary qubit observables.
The state-independent uncertainty relations are then transformed into the optimization problems over uncertainty regions.
- Score: 3.7298088649201353
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study the uncertainties of quantum mechanical observables, quantified by
the standard deviation (square root of variance) in Haar-distributed random
pure states. We derive analytically the probability density functions (PDFs) of
the uncertainties of arbitrary qubit observables. Based on these PDFs, the
uncertainty regions of the observables are characterized by the supports of the
PDFs. The state-independent uncertainty relations are then transformed into the
optimization problems over uncertainty regions, which opens a new vista for
studying state independent uncertainty relations. Our results may be
generalized to multiple observable case in higher dimensional spaces.
Related papers
- Uncertainty of quantum channels based on symmetrized \r{ho}-absolute variance and modified Wigner-Yanase skew information [6.724911333403243]
We present the uncertainty relations in terms of the symmetrized rho-absolute variance, which generalizes the uncertainty relations for arbitrary operator (not necessarily Hermitian) to quantum channels.
arXiv Detail & Related papers (2024-06-13T14:18:59Z) - Testing trajectory-based determinism via time probability distributions [44.99833362998488]
Bohmian mechanics (BM) has inherited more predictive power than quantum mechanics (QM)
We introduce a prescription for constructing a flight-time probability distribution within generic trajectory-equipped theories.
We derive probability distributions that are unreachable by QM.
arXiv Detail & Related papers (2024-04-15T11:36:38Z) - Foundations of non-commutative probability theory (Extended abstract) [1.8782750537161614]
Kolmogorov's setting for probability theory is given an original generalization to account for probabilities arising from Quantum Mechanics.
The sample space has a central role in this presentation and random variables, i.e., observables, are defined in a natural way.
arXiv Detail & Related papers (2023-06-01T20:34:01Z) - Full counting statistics as probe of measurement-induced transitions in
the quantum Ising chain [62.997667081978825]
We show that local projective measurements induce a modification of the out-of-equilibrium probability distribution function of the local magnetization.
In particular we describe how the probability distribution of the former shows different behaviour in the area-law and volume-law regimes.
arXiv Detail & Related papers (2022-12-19T12:34:37Z) - Parameterized Multi-observable Sum Uncertainty Relations [9.571723611319348]
We study uncertainty relations based on variance for arbitrary finite $N$ quantum observables.
The lower bounds of our uncertainty inequalities are non-zero unless the measured state is a common eigenvector of all the observables.
arXiv Detail & Related papers (2022-11-07T04:36:07Z) - Quantum Central Limit Theorems, Emergence of Classicality and
Time-dependent Differential Entropy [0.0]
We derive some Quantum Central Limit Theorems for expectation values of macroscopically coarse-grained observables.
These probability distributions open some pathway for an emergence of classical behaviours in the limit of infinitely large number of identical and non-interacting quantum constituents.
arXiv Detail & Related papers (2022-02-04T11:19:15Z) - Dense Uncertainty Estimation via an Ensemble-based Conditional Latent
Variable Model [68.34559610536614]
We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model.
We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation.
Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
arXiv Detail & Related papers (2021-11-22T08:54:10Z) - Uncertainty regions of observables and state-independent uncertainty
relations [6.597326596816639]
We present a framework for computing the tight uncertainty relations of variance or deviation.
We present state-independent uncertainty inequalities satisfied by the sum of variances or deviations of two, three and arbitrary many observables.
arXiv Detail & Related papers (2021-10-27T02:32:37Z) - Quantum backflow in the presence of a purely transmitting defect [91.3755431537592]
We analyse the quantum backflow effect and extend it, as a limiting constraint to its spatial extent, for scattering situations.
We make the analysis compatible with conservation laws.
arXiv Detail & Related papers (2020-07-14T22:59:25Z) - On the Estimation of Information Measures of Continuous Distributions [25.395010130602287]
estimation of information measures of continuous distributions based on samples is a fundamental problem in statistics and machine learning.
We provide confidence bounds for simple histogram based estimation of differential entropy from a fixed number of samples.
Our focus is on differential entropy, but we provide examples that show that similar results hold for mutual information and relative entropy as well.
arXiv Detail & Related papers (2020-02-07T15:36:10Z) - Towards a Kernel based Uncertainty Decomposition Framework for Data and
Models [20.348825818435767]
This paper introduces a new framework for quantifying predictive uncertainty for both data and models.
We apply this framework as a surrogate tool for predictive uncertainty quantification of point-prediction neural network models.
arXiv Detail & Related papers (2020-01-30T18:35:36Z)
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.