Bayesian Searches and Quantum Oscillators

TL;DR

This paper explores using quantum oscillators as analog computers to improve Bayesian inference and model selection, potentially surpassing classical computational limits and quantum noise barriers.

Contribution

It proposes a novel approach of employing quantum oscillator arrays for Bayesian inference, addressing intractability and noise issues in traditional methods.

Findings

Quantum oscillators can serve as analog computers for Bayesian inference.

Potential to detect weak signals below quantum noise threshold.

Addresses intractability of Bayesian model selection with classical computers.

Abstract

A new LLNL Strategic Initiative is focused on developing improved methods for Bayesian inference when the input data depends on hidden parameters. Part of this effort involves investigating the idea of using an array of quantum oscillators (viz microwave cavities) as an analog computer for implementing Bayesian model selection. The practical motivations are twofold: 1) Bayesian model selection problems are often intractable using conventional digital computers, and 2) quantum information processing may allow detection of weak analog signals below the usual quantum noise threshold.