Sampling Weak Values: A Non-Linear Bayesian Model for Non-Ideal Quantum Measurements

TL;DR

This paper introduces a non-linear Bayesian model for analyzing arbitrary-strength quantum measurements by sampling weak values, revealing how measurement strength influences data distribution and observable spectrum resolution.

Contribution

It presents a novel Bayesian framework that models quantum measurements as sampling weak values, connecting data statistics to weak value distributions and analyzing phase transition-like behavior.

Findings

Mean and variance relate directly to sampled weak values.

Distribution shape changes near a critical measurement strength.

Model explains qualitative changes in measurement outcomes.

Abstract

A model is proposed for the statistical analysis of arbitrary-strength quantum measurements, based on a picture of "sampling weak values" from different configurations of the system. The model is comprised of two elements: a "local weak value" and a "likelihood factor". The first describes the response of an idealized weak measurement situation where the back-reaction on the system is perfectly controlled. The second assigns a weight factor to possible configurations of the system. The distribution of the data in a measurement of arbitrary strength may the be viewed as the net result of interfering different samples weighted by the likelihood factor, each of which implements a weak measurement of a different local weak value. It is shown that the mean and variance of the data can be connected directly to the means and variances of the sampled weak values. The model is then applied to a situation similar to a phase transition, where the distribution of the data exhibits two qualitatively different shapes as the strength parameter is slightly varied away from a critical value: one below the critical point, where an unusual weak value is resolved, the other above the critical point, where the spectrum of the measured observable is resolved. In the picture of sampling, the transition corresponds to a qualitative change in the sampling profile brought about by the competition between the prior sampling distribution and the likelihood factor.