Analyzing Properties of Driven-Dissipative Quantum Systems from the Mean Field to Machine Learning

Graduate Thesis Or Dissertation

Citations

Citeable URL: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/7h149r52s

Abstract

Quantum systems are poised to play a large role in the emerging technologies of the near future. The areas of application are numerous, with major advances being achieved in recent years in the areas of metrology, sensing, and computing to name just a few. In order for these technologies to realize their full potential, significant challenges must be overcome in the areas of classical simulation, error correction, and device characterization.

In this thesis, we explore a number of approaches to tackling these challenges in the simulation and characterization of driven-dissipative quantum systems. We examine existing techniques for simulating these systems to make new observations about their properties that have potential applications in quantum metrology and sensing. We investigate the emergence of a time crystal in a system of two-level atoms and the conditions under which it arises, as well as the effect of single particle relaxation on spin squeezing in a similar system. We also propose a novel machine learning model for estimating physical parameters, with potential applications for detecting crosstalk in quantum information processors.

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