JILA Thesis Defense

Abstract: Breathomics aims to address the current unmet clinical needs by utilizing exhaled breath contents for non-invasive and real-time medical diagnostics. We demonstrate a frequency comb breathalyzer powered by machine learning for detecting COVID-19, finding 85 % accuracy among a 170-subject cohort. To enhance diagnostic power, we introduce Modulated Ringdown Comb Interferometry, a new technique enabling the quantification of “odor” of arbitrarily complex and unknown contents at new record sensing performance and requiring only simple instruments.

Neutral-atom arrays with single-particle detection and control are a powerful tool for quantum science. In this defense, I present results from two projects, both performed with the same tweezer-programmable neutral-strontium-array apparatus. First, we engineer Rydberg interactions to create entangled spin-squeezed states, whose measurement noise can outperform classical limits. In a synchronous optical-frequency comparison between two spin-squeezed ensembles of atoms, we realize a measurement with a stability better than the standard quantum limit.

Nonequilibrium quantum systems exhibit phenomena not seen in equilibrium but are also less well understood. To study these systems, quantum simulators hold much promise due to their broad tunability and access to measurement observables. In this defense, I present experiments engineering nonequilibrium quantum phases of matter using many strontium atoms in a high-finesse optical cavity. Observations include a first experimental realization of three dynamical phases in quenched BCS superconductors and insights into many-body gap protection in fermionic superfluids.

Abstract

Polyaromatic hydrocarbons (PAHs) are ubiquitous in astrochemistry and are si

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Abstract: Learning the expectation values of observables is an important task in quantum information, with applications to characterization of quantum devices and quantum optimization algorithms. We propose an estimation method called The Optimal Observable expectation value Learner, or TOOL, that can learn the expectation value of any given observable using the outcomes of any given measurement protocol. We prove that TOOL is minimax optimal for every observable and measurement protocol, and can dramatically outperform classical shadows for many observables of interest.

In this work, we develop theoretical tools to explore
quantum correlations within the AdS/CFT framework. We examine the
holographic realization of optimized correlation measures in two-dimensional
thermal states corresponding to spacetimes with black hole horizons, enhancing
our understanding of how geometry encodes entanglement in AdS/CFT. We
further introduce cutoff-independent regularization techniques to compute these
entropies - addressing divergences due to infinite volume near the AdS