Engineering optical lattices for ultracold atoms with spatial features and periodicity below the diffraction limit & Dual-species optical tweezer arrays for Rubidium and Ytterbium for Rydberg-interaction-mediated quantum simulations
Dissertation Committee Chair: Prof. Steven Rolston (co-advisor)
Committee:
Prof. Trey Porto (co-chair/co-advisor)
Prof. Ian Spielman
Prof. Nathan Schine
Prof. Ronald Walsworth
Abstract: This dissertation is based on two independent projects.
Constructing an ergodic theory of quantum information dynamics
Dissertation Committee Chair: Victor Galitski
Committee:
Paulo Bedaque
Alexey Gorshkov
Christopher Jarzynski
Nicole Yunger Halpern
Spectral Statistics, Hydrodynamics, and Quantum Chaos
Dissertation Committee Chair: Brian Swingle, Victor Galitski
Committee:
Maissam Barkeshli
Jay Sau
Jonathan Rosenberg (Dean’s representative)
Attacking Quantum Models with AI: When Can Truncated Neural Networks Deliver Results?
Currently, computing technologies are rapidly evolving and reshaping how we imagine the future. Quantum computing is taking its first toddling steps toward delivering practical results that promise unprecedented abilities. Meanwhile, artificial intelligence remains in public conversation as it’s used for everything from writing business emails to generating bespoke images or songs from text prompts to producing deep fakes.
Controlling quantum ergodicity in molecules large and small: From C60 to ultracold alkali dimers
Quantum ergodicity refers to the remarkable ability of quantum systems to explore their entire state space allowed by symmetry. Mechanisms for violating ergodicity are of fundamental interest in statistical and molecular physics and can offer novel insights into decoherence phenomena in complex molecular qubits. I will discuss the recent experimental observation of ergodicity breaking in rapidly rotating C60 fullerene molecules as a function of rotational angular momentum [1].
A DMRG Study of Excitons in the 2D t-J Model
Antiferromagnetic materials with microscopic behavior resembling that of the Fermi-Hubbard model are expected to host excitons, or bound electron-hole pairs. In order to investigate such behavior, we have optimized states of the t-J model in the single-particle-single-hole sector using the density matrix renormalization group (DMRG).
QCVV: Making Quantum Computers Less Broken
Abstract: Quantum computing hardware capabilities have grown tremendously over the past decade, as evidenced by demonstrations of both quantum advantage and error-corrected logical qubits. These breakthroughs have been driven, in part, by advances in quantum characterization, verification, and validation (QCVV). I will discuss how QCVV provides a hardware-agnostic framework for assessing the performance of quantum computers; I will describe in detail how specific QCVV protocols (such as gate set tomography and robust phase estimation) have been used to characterize and sig
Quantum Circuits for Chiral Topological Order
Quantum simulation stands as an important application of quantum computing, offering insights into quantum many-body systems that are beyond the reach of classical computational methods. For many quantum simulation applications, accurate initial state preparation is typically the first step for subsequent computational processes. This dissertation specifically focuses on state preparation procedures for quantum states with chiral topological order, states that are notable for their robust edge modes and topological properties.
JQI Researchers Win 2023 UMD Quantum Invention of the Year Award
A team of JQI researchers and their colleagues have won in the quantum category of the UMD Invention of the Year Award. They are honored for developing a new method for counting particles of light—photons—without destroying them.