Uncorrelated problem-specific samples of quantum states from zero-mean Wishart distributions
Rui Han, Weijun Li, Shrobona Bagchi, Hui Khoon Ng, Berthold-Georg, Englert

TL;DR
This paper introduces a two-step quantum state sampling algorithm using Wishart distributions that produces uncorrelated samples efficiently for low-dimensional systems, improving over existing methods.
Contribution
The authors develop a novel two-step sampling method combining Wishart distributions with rejection sampling, overcoming autocorrelation issues and enhancing efficiency for quantum state sampling.
Findings
High acceptance rates for one- and two-qubit states
Reasonably efficient for three-qubit states
Limited efficiency for four-qubit structured distributions
Abstract
Random samples of quantum states are an important resource for various tasks in quantum information science, and samples in accordance with a problem-specific distribution can be indispensable ingredients. Some algorithms generate random samples by a lottery that follows certain rules and yield samples from the set of distributions that the lottery can access. Other algorithms, which use random walks in the state space, can be tailored to any distribution, at the price of autocorrelations in the sample and with restrictions to low-dimensional systems in practical implementations. In this paper, we present a two-step algorithm for sampling from the quantum state space that overcomes some of these limitations. We first produce a CPU-cheap large proposal sample, of uncorrelated entries, by drawing from the family of complex Wishart distributions, and then reject or accept the entries in…
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Taxonomy
TopicsStatistical Mechanics and Entropy · Quantum Computing Algorithms and Architecture · Bayesian Modeling and Causal Inference
