Learning, Verifying, and Erasing Errors on a Chaotic and Highly Entangled Programmable Quantum Simulator

Citation

Shaw, Adam Lawrence (2024) Learning, Verifying, and Erasing Errors on a Chaotic and Highly Entangled Programmable Quantum Simulator. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/t7jr-mf97. https://resolver.caltech.edu/CaltechTHESIS:04152024-182938221

Abstract

Controlled quantum systems have the potential to make major advancements in tasks ranging from computing to metrology. In recent years, quantum devices have experienced tremendous progress, reaching meaningful, intermediate-scale sizes and demonstrating advantage over their classical counterparts. Still, sensing, learning, verifying, and hopefully mitigating errors in these systems is an outstanding and ubiquitous challenge facing all modern quantum platforms.

Here we review and expound upon one such platform: arrays of Rydberg atoms trapped in optical tweezers. We demonstrate several key advancements, including the first experimental realization of erasure conversion to prepare two-qubit Bell states with a fidelity in excess of 0.999, and to cool atoms to their motional ground state. We further showcase the tools of universal quantum processing via arbitrary single-qubit gates, fixed two-qubit gates, and mid-circuit measurement, and discuss applications of these techniques for metrology and computing.

Then, we turn to the many-body regime, generating highly entangled states with up to 60 atoms through analog quench dynamics. We reveal the emergence of random behavior from unitary quantum evolution, and uncover a universal form of quantum ergodicity linking quantum and statistical mechanics. We exploit these discoveries to verify the global many-body fidelity and then realize practical applications like parameter estimation and noise learning. Finally, we compare against both state-of-the-art quantum and classical processors: we introduce a new proxy for the experimental mixed state entanglement which is comparable amongst all quantum platforms, and that reflects the classical complexity of quantum simulation.

Thesis Files