VictorJuiz/thesis huggingface dataset:
Exploring the Limits of Classical Simulation: From Computational Many-Body Dynamics to Quantum Advantage

For many years after the dawn of computing machines, it seemed to be the case that the dynamics of any physical system (including all computers, which of course are physical systems themselves) could be efficiently simulated by a simple model of a computer called the Turing machine. A consequence was that computational problems that were found to be hard for Turing machines---those requiring a runtime superpolynomial in the size of the input---remained hard no matter what machine was built to solve them. But in the latter part of the 20th century, an intriguing counterexample emerged: quantum mechanics. The simulation of straightforward quantum systems seemed to have an exponential computational cost. This led to a provocative question: what if one were to build a computer from quantum mechanical components? Could that machine outperform the Turing machine, and efficiently simulate arbitrary quantum processes? And are there other hard problems that such a machine could efficiently solve?/nIn this dissertation we explore several questions stemming from those ideas. First, classical simulation of quantum many-body physics may be hard, but modern supercomputers are extremely powerful---with cutting-edge innovations in both hardware and algorithms, what quantum simulations can be achieved, and what physics can we learn from them? Second, while there has been astounding progress in the development of quantum computers, they are still small and noisy---what can we do on these near-term devices, that cannot be done with the powerful classical supercomputers just described? Furthermore, if we do a quantum mechanical computation that seems to be infeasible for even the fastest classical machines, how do we check that the result is actually correct? Answering these questions requires deeply exploring the physical nature of computing; at heart, it comes down to the beautiful puzzle of organizing the physical world around us to process information.

Advisor: Yao, Norman Y
A Second-Generation Energy Decomposition Analysis for Møller-Plesset Perturbation Theory Using a Global Virtual Space

In this thesis, a newly developed second-generation energy decomposition analysis (EDA)based on second order Møller-Plesset perturbation theory (MP2) is presented. EDA’s have become widely used to aid in understanding the nature of intermolecular interactions and are commonly based on density functional theory (DFT) and self-consistent field (SCF) calculations. However, using correlated post-SCF methods, such as MP2, for EDA is less common, partly due to the complexity associated with defining suitable approaches. As such, this thesis focuses on presenting a new approach for EDA’s for post-SCF methods through the implementation of restricted and unrestricted MP2 EDA calculations. This newly developed MP2 EDA builds upon the SCF-level second-generation absolutely localized molecular orbital EDA approach (ALMO-EDA-II) and provides distinct energy contributions for a frozen interaction energy (containing permanent electrostatics and Pauli repulsions), a polarized energy (yielding induced electrostatics), a dispersion-corrected energy, and the fully relaxed energy (which yields charge transfer). Importantly, the theory has been designed such that there are stable basis set limits for each term, evidenced by basis set stability calculations on a wide variety of systems as well as the S22 and Ionic43 datasets. Additionally, MP2 ALMO-EDA-II has been applied to four non-covalently bonded classes of complexes; a class of conventional hydrogen bonded systems, a class of non-conventionally hydrogen bonded systems, a class of tetrel bonded systems, and a “solvent-resistant” halogen bonded system. Through these systems, the usefulness of MP2 ALMO-EDA-II is explored, especially through its treatment of the correlation component of the interaction energy.

Advisor: Head-Gordon, Martin
Understanding Galactic Al-26 with the Compton Spectrometer and Imager

Aluminum-26 (Al-26) is a radioactive isotope produced in massive star processes. High-resolution spectroscopy of its 1.809 MeV gamma-ray (γ-ray) decay signature constrains the dynamics of its emission as it is ejected from its progenitor sites and incorporated into the interstellar medium of the Milky Way Galaxy. Imaging reveals dominant emission in the Inner Galaxy and emission in localized regions of massive star activity. Taken together, spectroscopy and imaging of Al-26 shed light on the chemical evolution of the Galaxy over millions of years./nThe Compton Spectrometer and Imager (COSI) is a compact Compton telescope designed to measure astrophysical γ-rays of energy 0.2–5 MeV. Its high-purity germanium detectors track incident photons as they Compton scatter throughout the detector volume. In 2016, COSI flew on a NASA ultra-long duration balloon for 46 days. This dissertation details the first analysis of Al-26 in the flight data and reports a measurement of 3.7σ significance above background and an Inner Galaxy flux of (8.6 ± 2.5) × 10−4 ph cm−2 s−1. All scientific achievements from the flight are predicated on calibrations performed before launch. These calibration procedures were repeated in advance of an intended 2020 balloon flight. Analyzing calibration data validates instrument performance and informs studies of detector effects, including charge sharing and charge trapping, that complicate the measurement process./nThe next generation of COSI as a NASA Small Explorer satellite is slated for launch in 2027. It is anticipated to strengthen the spectroscopic measurement of Al-26 in the balloon flight and yield the most detailed images of Al-26 to date. Extensive testing of the imaging algorithm in COSI’s new analysis toolkit is presented as a first step towards producing these images. The desired culmination of these efforts is an enhanced understanding of Galactic Al-26 by way of thorough calibrations, novel insight into undesirable detector effects, and advanced analyses of high-resolution data from the COSI balloon and satellite instruments.

Advisor: Tomsick, John
Bale, Stuart
Expanding the Atomistic Study of the Optical and Electronic Properties of Nanomaterials

The optical and electronic properties of semiconductor nanomaterials have long attracted significant interest due to the strong absorption and tunable spectra caused by quantum confinement. These materials have potential applications ranging from solar energy conversion and lighting to single photon sources and quantum computing. However, to realize any of these applications the role of the atomistic detail of these materials cannot be ignored. Understanding role of defects, traps, and structural distortion on the excited states of these materials remains a great challenge for modern computational science. Semiemperical pseudopotential models represent the leading way to understand the complexity of nanoscale systems in atomistic detail. In this dissertation we expand the applicability of these models to new materials where additional effects, like strong spin-orbit coupling must be considered. We also use these methods to help examine the dynamics of excited states in these nanomaterials, revealing the crucial roles of defects, distortions, and traps./nWe develop a formulation of the semiempirical pseudopotential method that includes the effects of spin-orbit coupling and other nonlocal terms in the potential. By using a separable form of these non-local terms we maintain a favorable computational scaling and thus keep the ability to investigate nanomaterials of experimentally relevant sizes. We apply this method to lead halide perovskite nanocrystals (NCs), promising materials for solar energy conversion that are known for their strong spin-orbit coupling. The atomistic study of these systems allows for an understanding of how distortion of the NC structure impacted the exciton fine structure, determining that contrary to some suggestions the ground state exciton is a dark state./nThe results of atomistic electronic structure methods also aid in developing kinetic models of excited state species in various nanomaterials. The dynamics of the transfer of holes from multi-excitonic II-VI NCs is explored as a competition between transfer, trapping, and non-radiative Auger recombination (AR). Pseudopotential calculations provide crucial insight in the AR rates and how those are impacted by the presence of trapped species. A similar kinetic model describing carrier recombination in few-layer black phosphorous is informed by density functional theory calculations of surface oxygen defects./nThis dissertation shows both the expansion of the semiemperical pseudopotential method and the application of the method to inform studies of material properties and design principles. The combination of theoretical development and experimental collaboration shows the utility of these models to solve practical problems of broad scientific import. By expanding the applicability of these methods to new materials, these detail atomistic calculations can now be applied to even more experimentally relevant systems.

Advisor: Rabani, Eran
Interplay of symmetry breaking and topological order

The two most prevalent classes of ordered states in quantum materials are those arising from spontaneous symmetry breaking (SSB) and from topological order. However, a systematic study for their coexistence in interacting systems is still lacking. In this thesis, we will investigate how the topological configuration in order parameter spaces from SSB interplays with the symmetry protected/enriched topological orders in two spatial dimensions. We start from a phenomenological model of the domain wall structure in chiral spin liquids with domains of opposite chiralities. Based on a standard model, we obtain a spatially varying, self-consistent mean-field solution for the spinons that describes both the gapless edge modes and the change of chirality at the domain wall. We derive the non-universal properties, such as the velocity of the topologically protected domain wall edge states and its modification to domain wall tension. Then we discuss the interplay between topological orders from a more formal point of view. We consider two-dimensional topologically ordered systems with coexisting long-range orders, where the only gapless excitations in the spectrum are Goldstone modes of spontaneously broken continuous symmetries. We show that the universal properties of point defects and textures are determined by the remnant symmetry enriched topological order in the symmetry-breaking ground state with non-fluctuating order parameters, and provide a classification for their properties using algebraic topology. We further investigate the quantum dynamics that can happen at the domain wall of topological orders, where particle-hole scattering leads to linear-in-temperature resistivity of edge modes. Finally, we introduce an effective edge network theory to characterize the boundary topology of coupled edge states generated from various types of topological insulators.

Advisor: Moore, Joel
Interrogating the Tensor Network Regression Model

There has been growing interest in using tensor networks as machine learning models, inspired by their successes in quantum many-body physics and tensor analysis. These models operate by first mapping input data into an exponentially-large vector space, and then performing linear regression on the resulting feature set. It is well-known that the expressive power of a tensor network regression algorithm originates from its tensor-product featurization, but it is unclear how the tensors in the network are able to convert such a high-dimensional and unstructured intermediate into a useful output. We explore this question by probing the properties of tensor network models on three fronts. First, we assess how the performance of a tensor network classifier degrades when the the size and complexity of the expanded feature space is reduced, and find that most of the space is not effectively utilized by the model. Next, we characterize how the rank of a tensor network impacts the class of regression functions that it can represent, demonstrating that even quadratic polynomials can be impossible to fully realize in most cases. Finally, we use a novel neural network algorithm to determine whether classical images possess correlation structures that mirror those found in quantum wavefunctions, and find evidence of area law scaling in the MNIST and Tiny Images datasets. Taken together, these results demonstrate how mathematical tools from tensor analysis and quantum physics can be leveraged to gain deep insight into the inner workings of tensor network machine learning models.

Advisor: Whaley, K. Birgitta
Expanding the Operational Environments of UAVs: Design, Control, and Motion Planning for a Tensegrity Aerial Vehicle and an Uncrewed Underwater Aerial Vehicle

This dissertation explores methodologies for enhancing the operational capabilities of Uncrewed Aerial Vehicles (UAVs). By tightly integrating design, control strategy, and planning algorithms, we have enabled UAVs to operate in environments that pose significant challenges for traditional flying robots./nThe dissertation comprises three parts. The first part focuses on collision-resilient UAVs designed for cluttered environments with obstacles that are difficult to detect and/or avoid. We introduce a vehicle featuring an icosahedron tensegrity structure for resilience to high-speed collisions. The design of the vehicle is guided by a model-based approach, which employs dynamics simulation to predict structural stresses in the system. Furthermore, an autonomous re-orientation controller is presented to facilitate post-collision flight resumption, enabling the vehicle to rotate from any given orientation to ones ready for takeoff./nThe second part presents a collision-inclusive, sampling-based motion planning algorithm for narrow and cluttered environments. Incorporating collisions into planning yields two benefits. First, collisions can be exploited for quick changes of movement directions. Second, the allowance of collisions results in more efficient sampling in confined spaces, thereby reducing the planning time needed. The algorithm's effectiveness in narrow environments is demonstrated through Monte Carlo simulations, and the trajectories generated by the algorithm are validated by tracking experiments using the tensegrity aerial vehicle./nThe final part broadens UAVs' use to multi-domain environments with a miniature Uncrewed Aerial Underwater Vehicle (UAUV), capable of operation both in air and underwater. A pressure-based depth estimator and a control strategy for water breaching have been developed to facilitate the water-air transition. These tools help the UAUV to create a transition window, ensuring all propellers are exposed to air, and to determine the ideal timing to switch controller modes from water to air, thereby guaranteeing a successful transition.

Advisor: Mueller, Mark
Optimizing for the Future Smart Grid: Efficient Methods for Nonconvex AC Power Flow Problems

The increased electrification of society and calls for improvements in electrical grid reliability lend renewed attention to the domain of power systems optimization. For the future smart grid, it will be crucial both to find better solutions to classical power grid optimization problems such as optimal power flow (OPF) and state estimation (SE) as well as to develop new optimization methods for the updated physical systems. Due to the nonlinearity of alternating current (AC), many optimization problems in power systems are nonlinear and nonconvex and thus are hard to solve with existing fast convex methods. In this dissertation, we propose some efficient methods related to the OPF, post-contingency OPF, SE, and power flow (PF) problems. These methods provide new optimization and machine learning frameworks for ensuring the reliable and efficient operation of the power grid.

Advisor: Sojoudi, Somayeh
Towards Understanding Human Behavior for Autonomous Driving and Robotics

Understanding human behavior plays a significant role in many industrial applications, such as autonomous driving and robotics, that entail human participation. Having an efficacious human behavior model facilitates intelligent agents in arriving at high-quality decisions while aligning with human values. In the past decades, while we have witnessed the remarkable progress of human behavior modeling with modern deep learning approaches, it remains a challenging task due to the inherent stochasticity of human behavior and the intricate structural design of deep learning models./nThis dissertation focuses on learning-based human behavior prediction and generation for autonomous driving and robotics from both the model and learning algorithm design perspective. In particular, the dissertation is concerned with two manifestations of human behaviors: human driving behavior and 3D human motion. The dissertation is divided into two parts. Part I focuses on improving the generalization of human driving behavior prediction problems in different problem settings. Chapter 2 introduces a transferable human driving behavior prediction model leveraging domain-knowledge-based representation. In Chapter 3, a neural memory system based on generative models is proposed to enable the capability of continual learning for multi-agent vehicle trajectory prediction. In Chapter 4, an uncertainty analysis of trajectory prediction in terms of static and dynamic information is provided, and a static-information-only-based approach for difficult instance discovery is proposed to improve the adaptability of the prediction model. Part II focuses on two properties of human behavior generation: diversity and feasibility. Chapter 5 introduces a novel generative model exploring diverse 3D human motion behavior and enables testing-time adjustment of prediction and generation. Chapter 6 introduces a differentiable safety-critical control framework to ensure the feasibility and generalizability of generated motions, and we demonstrate the efficiency of the proposed approach in the collision avoidance application.

Advisor: Tomizuka, Masayoshi
Advances in Zero-Knowledge Proofs: Bridging the Gap between Theory and Practices

This dissertation presents a series of novel zero-knowledge proof (ZKP) protocols—Libra, deVirgo, Orion, and Pianist—each achieving significant improvements in proof generation speed. Zero-knowledge proofs are critical cryptographic tools, enabling secure and privacy-preserving transactions without revealing sensitive information. However, their practical adoption is hindered by the inefficiency of existing proof generation methods./nOur research proposes four distinct protocols, each making substantial contributions to the efficiency of ZKP generation. We begin with the Libra protocol, which introduces a more efficient proof construction method compared to state-of-the-art ZKPs at that time. Next, we present the deVirgo protocol, which builds upon Libra's design and further optimizes proof generation by leveraging the power of parallelization./nThe third protocol, Orion, takes a different approach, resulting in significant improvements in proof generation speed. We detail the innovative design and methodology of Orion, highlighting its unique features and performance gains./nFinally, we introduce the Piano protocol, which employs parallel computation strategies to achieve remarkable improvements in proof generation speed and it is compatible with existing popular protocol Plonk. Piano builds upon the foundation established by Plonk, while incorporating novel techniques to enhance performance./nThe dissertation includes a comprehensive comparative analysis of the four proposed protocols, evaluating their scalability, security, and practicality. In conclusion, our research contributes to the field of cryptography by providing a series of innovative ZKP protocols that significantly enhance proof generation speed, paving the way for more widespread adoption of privacy-preserving technologies.

Advisor: Song, Dawn
Development of New Catalytic Systems for the Silylation and Borylation of C–H Bonds

The following dissertation discussed the development of new catalytic systems for the silylation and borylation of C–H bonds. These reactions involve iridium complexes that catalyze the cleavage of a C–H bond in either aryl of alkyl substrates and the subsequent formation of C–B or C–Si bond with high chemo- and regioselectivity. The development of a new catalytic system has increased the reliability of the C–H borylation in both large- and small-scale reactions. The development of a new catalytic system has expanded the scope of substrates that can undergo C–H silylation./nChapter 1 is a comprehensive review of the applications C–H silylation and C–H borylation reactions in the synthesis and functionalization of complex molecules. Advances in both undirected and directed methods for the transition metal-catalyzed silylation and borylation of C–H bonds have led to their widespread adoption in the early-, mid-, and late-stage synthesis of complex molecules. This chapter outlines the state of the art of the transition-metal catalyzed silylation and borylation of C–H bonds and identifies reaction types within these areas that are prospects for future progress./nChapter 2 describes the development of an iridium-catalyzed, stereoselective conversion of secondary alcohols or ketones to anti-1,3-diols by the silylation of secondary C–H bonds to oxygen and oxidation of the resulting oxasilolane. The silylation of secondary C–H bonds in secondary silyl ethers derived from alcohols or ketones is enabled by a catalyst formed from a simple bisamidine ligand. The silylation occurs with high selectivity at a secondary C–H bond to oxygen over distal primary or proximal secondary C–H bonds. Initial mechanistic investigations suggest the source of the newly achieved reactivity is a long catalyst lifetime resulting from the high binding constant of the strongly electron-donating bisamidine ligand./nChapter 3 describes the development of a new, single-component iridium precatalyst for the borylation of aryl C–H bonds. The new precatalyst is air-stable and catalyzes the borylation of C–H bonds at comparable rates to the commonly used two-component system, which employs an air-sensitive precatalyst. The air-stable precatalyst shares the capabilities of the two-component system with respect to regioselectivity and substrate scope of C–H borylation. The air-stability of the precatalyst improves the batch-to-batch reproducibility of the yield of the borylation of C–H bonds and is being used for the borylation of C–H bonds on nanomole-scale at AbbVie.

Advisor: Hartwig, John F