Physics Theses and Dissertations

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HIGH PERFORMANCE NANOPHOTONIC CAVITIES AND INTERCONNECTS FOR OPTICAL PARAMETRIC OSCILLATORS AND QUANTUM EMITTERS
(2024) Perez, Edgar; Srinivasan, Kartik; Hafezi, Mohammad; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Integrated photonic devices like photonic crystals, microring resonators, and quantum emitters produce useful states of light, like solitons or single photons, through carefully engineered light-matter interactions. However, practical devices demand advanced integration techniques to meet the needs of cutting-edge technologies. High performance nanophotonic cavities and interconnects present opportunities to solve outstanding issues in the integration of nanophotonic devices. In this dissertation I develop three core tools required for the comprehensive integration of quantum emitters: wavelength-flexible excitation sources with sufficient pump power to drive down stream systems, photonic interconnects to spatially link the excitation sources to emitters, and cavities that can Purcell enhance quantum emitters without sacrificing other performance metrics. To create wavelength-flexible excitation sources, a high-performance χ(3)microring Optical Parametric Oscillator (OPO) is realized in silicon nitride. Microring OPOs are nonlinear frequency conversion devices that can extend the range of a high-quality on-chip (or off-chip) laser source to new wavelengths. However, parasitic effects normally limit the output power and conversion efficiency of χ(3)microring OPOs. This issue is resolved by using a microring geometry with strongly normal dispersion to suppress parasitic processes and multiple spatial mode families to satisfy the phase and frequency matching conditions. Our OPO achieves world-class performance with a conversion efficiency of up to 29% and an on-chip output power of over 18 mW. To create photonic interconnects, Direct Laser Writing (DLW) is used to fabricate 3-dimensional (3D) nanophotonic devices that can couple light into and out of photonic chips. In particular, polymer microlenses of 20 μm diameter are fabricated on the facet of photonic chips that increase the tolerance of the chips to misaligned input fibers by a factor of approximately 4. To do so, we develop the on-axis DLW method for photonic chips, which avoids the so-called "shadowing" effect and uses barcodes for automated alignment with machine vision. DLW is also used to fabricate Polymer Nanowires (PNWs) with diameters smaller than 1 μm that can directly couple photons from quantum emitters into Gaussian-like optical modes. Comparing the same quantum emitter system before and after the fabrication of a PNW, a (3 ± 0.7)× increase in the fiber-coupled collection efficiency is measured in the system with the PNW. To refine the design of quantum emitter cavities, a toy model is used to understand the underlying mechanisms that shape the emission profiles of Circular Bragg Gratings (CBGs). Insights from the toy model are used to guide the Bayesian optimization of high-performance CBG cavities suitable for coupling to single-mode fibers. I also demonstrate cavity designs with quality factors (Q) greater than 100000, which can be used in future experiments in cavity quantum electrodynamics or nonlinear optics. Finally, I show that these cavities can be optimized for extraction to a cladded PNW while producing a Purcell enhancement factor of 100 with efficient extraction into the fundamental PNW mode. The tools developed in this dissertation can be used to integrate individual quantum emitter systems or to build more complex systems, like quantum networks, that require the integration of multiple quantum emitters with multiple photonic devices.
TRAPPED IONS: FROM ERROR CORRECTION TO SIMULATION OF QUANTUM FIELD THEORIES
(2024) Nguyen, Hong Nhung; Linke, Norbert M; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Trapped ions stand out as a leading platform for quantum computing due to their long coherence times, high-fidelity quantum gates, and the ability to precisely control individual qubits, enabling scalable and precise quantum computations. This dissertation reports advances in quantum computing with trapped ions, focusing on robust and high-fidelity entanglement generation, logical qubit encoding, and applications in quantum simulations of high-energy physics. In particular, we report the implementation of a novel pulse optimization scheme forachieving high-fidelity entangling gates in our setup. The scheme enables a balanced trade- off between robustness to experimental drift, laser power, and gate duration, without the need for expensive optimization. We also demonstrate the implementation of the Shor code with different code distances on our trapped-ion quantum computer, highlighting the fault-tolerant preparation of a logical qubit with high fidelity and showcasing the potential for reliable quantum computing. Finally, we detail an experimental quantum simulation of the Schwinger model, a quantum electrodynamics theory in 1+1 dimensions, using two, four, and six qubits, demonstrating non-perturbative effects such as pair creation over extended periods of time. We study the gate requirement for two formulations of the model using a quantum simulation algorithm, considering the trade-offs between Hamiltonian term ordering, the number of time steps, and experimental errors. We employ a symmetry-protection protocol with random unitaries and a symmetry based post-selection technique to minimize errors. This work emphasizes the importance of the integrated approach between theory, algorithms, and experiments for efficient simulation of complex physical systems like lattice gauge theories.
Optimized simulations of fermionic systems on a quantum computer
(2024) Wang, Qingfeng; Monroe, Christopher; Chemical Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Quantum computing holds promise for simulating microscopic phenomena, offering profound implications across disciplines such as chemistry, condensed matter physics, and high-energy physics, particularly in the accurate simulation of fermions. However, practical implementation requires the optimization of quantum programs to mitigate quantum noise and decoherence effects. Given the constraints of near-term quantum computers, the Variational Quantum Eigensolver (VQE) emerges as a key approach for estimating molecular ground state energies, crucial for determining chemical properties. This work aims to present advancements in optimizing VQE simulations to minimize quantum computational resources. Specifically, this work explores various optimization strategies, including the utilization of second-order perturbation correction to recover additional energy beyond VQE estimates and select critical ansatz terms. Additionally, circuit optimization techniques are investigated, focusing on achieving shorter equivalent ansatz circuits, particularly for physically-inspired VQE ansatz, through methods such as generalized fermion-to-qubit transformations and Pauli string orderings. Furthermore, this work demonstrates the advantage of a better initial state on a trapped-ion quantum computer.
THE ROLE OF THE PROTEIN-LIPID BOUNDARY IN THE GATING OF THE MECHANOSENSITIVE CHANNEL MSCS, AND THE THERMODYNAMICS OF ARGININE-PHOSPHATE INTERACTIONS
(2024) Britt, Madolyn; Sukharev, Sergei; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Bacteria are exceptionally adaptive to a wide range of conditions. The E. coli mechanosensitive channel MscS is a low-threshold osmolyte release valve that provides environmental stability by regulating turgor pressure to prevent cell swelling and lysis in response to hypoosmotic shock. MscS is an adaptive multi-state channel that gates directly in response to tension in the surrounding lipid bilayer. Although MscS has three functional states (closed, open, inactivated), there are only two classes of structures: (1) nonconductive, characterized by splayed lipid-facing helices, kinked pore-lining helices, and lipid perturbations at the cytoplasmic interface and (2) semi-open conductive, characterized by an expanded pore that does not fully satisfy the experimental conductance. Currently, there is no consensus on how to relate these structural states to functional states. By default, the nonconductive structure is regularly assumed closed in the literature. In this thesis, I contribute to the body of existing experimental evidence that strongly suggests that the nonconductive structure corresponds to the inactivated state, rather than the closed state. Specifically, I focus on the channel as a membrane-embedded physical object and look to examine how lipids mediate tension-driven conformational dynamics. I use mutagenesis and patch-clamp electrophysiology to determine how MscS mutants with different protein-lipid interactions alter functional state distributions and transition rates. I then leverage these data to inform structure interpretation. Correctly identifying the structures of MscS that correspond to each functional state and the physical factors that stabilize them is critical towards understanding the underlying mechanism for MscS mechanosensitivity and its adaptive functional cycle. Chapter 2 explores how mutations of conserved anchor residues R46 and R74, interacting with lipid phosphates, affect gating transitions. We find that mutations at these positions predominantly alter the kinetics and voltage dependence of slow inactivation transitions, suggesting that extensive lipid rearrangement around these residues is a structural feature of inactivation. We also identify membrane potential as a factor regulating MscS state distribution. Chapter 3 investigates the role of protein-lipid interactions at both cytoplasmic and periplasmic interfaces in MscS functional behavior. Results indicate that MscS requires TM helix mobility at the periplasmic interface, but helix stability at the cytoplasmic interface for proper state transitions. We also find an interesting mutant, R46L/R74L, that is highly predisposed toward the inactivated state in giant spheroplasts, but apparently distributed normally into the closed state in actively metabolizing bacteria, providing evidence that the MscS population is under metabolic control. Finally, Chapter 4 aims to improve methods for examining these interactions in silico. The thermodynamics of arginine small peptide interactions with POPC, POPA, and POPG phospholipids is determined using ITC, and the affinity is found to depend on the accessibility of the lipid phosphate group. I also identify the ensemble of peptide-membrane bound states by constructing Markov state models from clustered trajectory data, revealing discrepancies between experimental and simulation results. These data are the first steps toward improving FF descriptions of arginine-phosphate interactions within membranes.
How Non-Hermitian Superfluids are Special? Theory and Experiments
(2024) Tao, Junheng; Spielman, Ian Bairstow; Chemical Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Ultracold atoms emerge as a promising advanced platform for researching the principles of quantum mechanics. Its development of scientific understanding and technology enriches the toolbox for quantum simulations and quantum computations. In this dissertation work, we describe the methods we applied to build our new high-resolution 87Rb Bose-Einstein condensate (BEC) machine integrated with versatile quantum control and measurement tools. Then we describe the applications of these tools to the research of novel superfluidity and non-Hermitian physics. Superfluids and normal fluids were often studied in the context of Landau’s two-fluid model, where the normal fluid stemmed from thermally excited atoms in a superfluid background. But can there be normal fluids in the ground state of a pure BEC, at near zero temperature? Our work addressed the understanding of this scenario, and then measured the anisotropic superfluid density in a density-modulated BEC, where the result matched the prediction of the Leggett formula proposed for supersolids. We further considered and measured this BEC in rotation and found a non-classical moment of inertia that sometimes turns negative. We distinguished the roles of superfluid and normal fluid flows, and linked some features to the dipolar and spin-orbit coupled supersolids. As a second direction, we describe our capability to create non-Hermiticity with Raman lasers, digital-micromirror device (DMD), and microwave, and present our work in engineering the real space non-Hermitian skin effect with a spin-orbit coupled BEC. By use of a spin-dependent dissipative channel, we realized an imaginary gauge potential which led to nonreciprocal transport in the flat box trap. We studied the system dynamics by quenching the dissipation, and further prepared stationary edge states. We link our discoveries to a non-Hermitian topological class characterized by a quantized winding number. Finally, we discuss the exciting promises of using these tools to study many-body physics open quantum systems.
Machine Learning For Predicting Non-Stationary Dynamical Systems, and Global Subseasonal-to-Seasonal Weather Phenomena
(2024) Patel, Dhruvit Paresh; Ott, Edward; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In this thesis, we are interested in modeling (1) the long-term statistical behavior of non-stationary dynamical systems, and (2) global weather patterns on the Subseasonal-to-Seasonal (S2S) time scale (2 weeks - 6 months). The first part of this thesis is primarily concerned with the situation where we have available to us measured time series data of the past states of the system of interest, and in some cases, a (perhaps inaccurate) scientific-knowledge-based model of the system. The central problem here lies in predicting a future behavior of the system that may be fundamentally different than that observed in the measured time series of its past. We develop machine learning -based methods for accomplishing this task and test it in various challenging scenarios (e.g., predicting future abrupt changes in dynamics mediated by bifurcations encountered that were not included in the training data). We also investigate the effects of dynamical noise in the training data on the predictability of such systems. For the second part of this thesis, we modify a machine learning -based global climate model to predict various weather phenomena on the S2S time scale. The model is a hybrid between a purely data-driven machine learning component and an atmospheric general circulation model. We begin by formulating a purely machine learning approach that utilizes the measured time series of the past states of the target non-stationary system, as well as knowledge of the time-dependence of the non-stationarity inducing time-dependent system parameter. We demonstrate that this method can enable the prediction of the future behavior of the non-stationary system even in situations where the future behavior is qualitatively and quantitatively different from the behavior in the training data. For situations where the training data contains dynamical noise, we develop a scheme to enable the trained machine learning model to predict trajectories which mimic the effects of dynamical noise on typical trajectories of the target system. We test our methods on the discrete time logistic map, the continuous time Lorenz system, and the spatiotemporal Kuramoto-Sivashinsky system, and for a variety of non-stationary scenarios. Next, we study the ability of our approach to not only extrapolate to previously unseen dynamics, but also to regions previously unexplored by the training data of the target system's state space. We find that while machine learning models can exhibit some capabilities to extrapolate in state space, they fail quickly as the amount of extrapolation required increases (as expected of any purely data-driven extrapolation method). We explore ways in which such failures can be mitigated. For instance, we show that a hybrid model which combines machine learning with a knowledge-based component can provide substantial improvements in extrapolation. We test our methods on the Ikeda map, the Lorenz system, and the Kuramoto-Sivashinsky system, under challenging scenarios (e.g., predicting future hysteretic transitions in dynamical behavior). Finally, we modify a machine learning -based hybrid global climate model to forecast global weather patterns on the S2S time scale. Predicting on this time scale is crucial for many domains (e.g., land, water, and energy management), yet it remains a difficult period to obtain useful predictions for. We demonstrate that our model has useful skill in predicting a number of phenomena including global precipitation anomalies, the El Nino Southern Oscillation and its related teleconnections, and various equatorial waves.
Understanding Allosteric Communication in Biological Systems using Molecular Dynamics Simulations
(2024) Samanta, Riya; Matysiak, Silvina; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Allostery is critical to survival in living organisms due to its biological relevance in signal transduction, metabolism, and drug discovery. However, the molecular details of this phenomenon remain unclear. In this thesis, I present my work on two allosteric protein systems, each representative of structure-based (E. coli Biotin Protein Ligase) and dynamics-based (B. taurus S100B) allostery. I examined the structural and dynamic features of the proteins and associated variants subjected to various allosteric triggers (ligand/salt/mutations) to study how external/internal perturbations transmit across large distances using Molecular Dyanmic simulations in conjunction with the experiments carried out by our collaborators. Additionally, I carried out Network analyses on the two systems to characterize communication pathways on the protein/ protein family levels. Together, the structural and dynamic features would help us elucidate the underlying mechanism of allostery. The first chapter introduces the two systems with a brief dive into the history of allostery. In the second chapter, my work on E. coli Biotin Protein Ligase and its variants reveal one possible mechanism by which disorder-to-order transitions at the functional surfaces transmit via local changes around the critical residues in the allosteric network. The third chapter explores how the protein network reconfigures to adopt a new allosteric function by studying the allosteric and non-allosteric Biotin Protein Ligases. The fourth chapter elucidates the structural and dynamical markers in bovine S100B, which help to relay information about an allosteric signal by varying two allosteric triggers - ionic strength and target peptide. The final chapter sums up my conclusions, where I propose additional experiments and computational analyses that could be carried out to further our understanding of allostery.
Mathematical Modeling of Cellular Exhaustion to Guide Future Immunotherapy Research
(2024) Simmons, Tyler; Levy, Doron; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Cellular exhaustion is a dysfunction found in various adaptive immune cells. In chronic settings, like cancer, antigen persistence and prolonged stimulation initiates the development of T cell exhaustion. The exhausted T cell population is a distinct lineage consisting of progenitor exhausted CD8+ T cells and terminally exhausted CD8+ T cells and is characterized by an upregulation of inhibitory receptor frequencies and diminished effector functions. The hypofunctionality of exhausted T cells prevents proper immunity and fails to eradicate the tumor. Recent years have shown a growing interest in targeting T cell exhaustion, attempting to reinvigorate effector functions, as a form of immunotherapy. Though beneficial responses have been reported in clinical settings, patient responses are inconsistent. Complementing the current biological understanding of T cell exhaustion and to advance immunotherapeutic efforts, novel research using mathematical modeling offers valuable insight. Constructing a foundational framework of an exhausted immune response to cancer provides an alternative approach to understanding the tumor-immune system. Presented here is the construction of a mathematical model detailing the development of progenitor and terminally exhausted CD8+ T cell populations in response to a growing tumor. Parameterization and simulation of this model captures biological dynamics observed in experimental and clinical settings. Analysis and conclusions of this model suggest population size and maintenance of progenitor exhausted CD8+ T cells should be a pillar of immunotherapy efforts. Stemming from these conclusions, it was theorized that targeting exhausted CD4+ helper T cells, which, under normal non-chronic conditions, contribute heavily to CD8+ T cell responses, would be a new and effective approach for immunotherapy. To test this hypothesis, the previously constructed model of CD8+ T cell exhaustion was expanded to incorporate CD4+ helper 1 T cells as well as immunosuppressive regulatory T cells. Simulation and analysis of this expanded model further emphasize the need to maintain progenitor exhausted CD8+ T cell numbers. Additionally, model analysis also indicated that the functionality of CD4+ T cells, both regulatory and exhausted CD4+ helper 1 T cells, played a crucial role in tumor persistence. From this work, research regarding CD4+ T cell exhaustion is strongly encouraged. With a better understanding of this dysfunction, CD4+ T cells may be a potentially effective target for future immunotherapy strategies.
Photon-Mediated Interactions in Lattices of Coplanar Waveguide Resonators
(2024) Amouzegar, Maya; Kollár, Alicia; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Circuit quantum electrodynamics (circuit QED) has become one of the main platforms for quantum simulation and computation. One of its notable advantages is its ability to facilitate the study of new regimes of light-matter interactions. This is achieved due to the native strong coupling between superconducting qubits and microwave resonators, and the ability to lithographically define a large variety of resonant microwave structures, for example, photonic crystals. Such geometries allow the implementation of novel forms of photon-mediated qubit-qubit interaction, cross-Kerr qubit-mediated interactions, and studies of many-body physics. In this dissertation, I will show how coplanar waveguide (CPW) lattices can be used to create engineered photon-mediated interactions between superconducting qubits. I will discuss the design and fabrication of a quasi one-dimensional lattice of CPW resonators with unconventional bands, such as gapped and ungapped flat bands. I will then present experimental data characterizing photon-mediated interactions between tunable transmon qubits and qubit-mediated non-linear photon-photon interactions in the said lattice. Our results indicate the realization of unconventional photon-photon interactions and qubit-qubit interactions, therefore, demonstrating the utility of this platform for probing novel interactions between qubits and photons. In future design iterations, one can extend the study of these interactions to two-dimensional flat and hyperbolic lattices.
TURBULENCE AND SUPERFLUIDITY IN THE ATOMIC BOSE-EINSTEIN CONDENSATE
(2024) Zhao, Mingshu; Spielman, Ian; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In this dissertation I investigate turbulence in atomic Bose-Einstein condensates (BECs), focusing on the challenge of quantifying velocity field measurements in quantum fluids. Turbulence, a universal phenomenon observed across various scales and mediums – from classical systems like Earth's oceans and atmosphere to quantum fluids including neutron stars, superfluid helium, and atomic BECs – exhibits complex fluid motion patterns spanning a wide range of length scales. While classical turbulence has been extensively studied, quantum systems present many open questions, particularly regarding the existence of an inertial scale and the applicability of Kolmogorov scaling laws. I introduce a novel velocimetry technique, analogous to particle image velocimetry (PIV), using spinor impurities as tracer particles. This method enables the direct measurement of the velocity field and thereby the velocity structure functions (VSFs) in turbulent atomic BECs. The technique overcomes limitations of existing experimental approaches that rely on time of flight (TOF) measurements, offering a clearer connection to VSFs and enabling a more direct comparison of turbulence in atomic gases with other fluids. The cold-atom PIV technique enables directly measuring the velocity field, leading to a detailed analysis of both VSFs and the velocity increment probability density functions (VI-PDF). Key findings include the observation of superfluid turbulence conforming to Kolmogorov theory from VSFs, and intermittency from high order of VSFs and the non-Gaussian fat tail in the VI-PDF.
Quantum Computing and Machine Learning Approaches to Quantum Many-Body Physics
(2024) Sheng, Shiyi; Bedaque, Paulo F.; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Lattice field theory provides a framework for which to explore properties of quantum field theories non-perturbatively. However for certain lattice calculations, for example when considering real-time dynamics or fermionic systems at finite density, sign problems occur which render those calculations intractable. One approach to solving the sign problem is to avoid it altogether by instead considering a simulation of the field theory on a quantum computer. For bosonic field theories, a procedure of qubitizing the bosonic fields is a necessary first step. The infinite-dimensional Hilbert space of the bosonic fields must be properly truncated as to encode those fields on a finite-dimensional Hilbert space spanned by the qubits on the quantum computer. This thesis first discusses various strategies of making such a truncation. Ideally, the truncation yields a discrete spin system that contains a critical point in the same universality class as the untruncated field theory. That way, the physics of the original field theory is reproduced in the continuum limit of the truncated theory without needing to take a second limit of removing the truncation. Simulations of different models arising from various truncation strategies of the (1+1)-dimensional O(3) nonlinear sigma model are performed and different qubitizations for SU(2) gauge fields are considered and proposed. Due to a lack of an efficient method for solving many-body systems in more than one dimension, numerical simulations of these SU(2) qubitizations are unavailable. The second half of the thesis explores the use of machine learning techniques in providing effective ways to solve quantum many-body problems. Neural network structures, such as feed-forward networks and restricted Boltzmann machines are universal approximators for continuous and discrete functions respectively. Therefore, they can be used as flexible wave function ansatze. Gradient descent algorithms can be applied to variationally search the general functional space spanned by neural-network-based ansatze for ground states of interacting, many-body systems. An ansatz is constructed explicitly for a system of indistinguishable bosons in one dimension and tested by comparing numerical results with analytic solutions of several exactly-solvable models. An extension of these neural-network ansatze to systems of identical bosons and fermions and discrete spin systems in higher dimensions would allow for concrete simulations of systems ranging from nuclei and qubitization models.
THE SEARCH FOR COINCIDENT GAMMA-RAY EMISSION FROM FAST RADIO BURSTS WITH THE HAWC OBSERVATORY
(2024) Willox, Elijah J; Goodman, Jordan A; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In 2007 a new class of radio transients was discovered, coming from outside of our galaxy with high fluence emitted in the radio band on millisecond timescales. These bursts of radio waves emitted within an order of magnitude of the power of the least bright gamma- ray bursts. These fast radio bursts (FRBs) have since become the target of many searches across radio observatories and multiwavelength follow-up campaigns, but their origin re- mains unknown. In order to understand more about these fascinating events, continued multiwavelength follow-ups are necessary to provide a more complete picture. The High Altitude Water Cherenkov (HAWC) observatory is a very-high-energy gamma-ray detector covering the range of 100 GeV to 300 TeV that is well suited to the detection of transient phenomena due its high live-time and wide field of view, and in particular for a follow- up search on FRBs to determine possible very high energy gamma-ray coincidences. The search for gamma-ray signals from FRBs consists of two searches: first is a persistent source search to identify if FRB emission ever comes from TeV gamma-ray emitting galax- ies, and a transient search centered on the reported burst time and location. The results of the FRB search within the HAWC data sets the most constraining limits on the widestpopulation of FRBs ever searched in the VHE band.
Analyzing and Enhancing Molecular Dynamics Through the Synergy of Physics and Artificial Intelligence
(2024) Wang, Dedi; Tiwary, Pratyush; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Rapid advances in computational power have made all-atom molecular dynamics (MD) a powerful tool for studying systems in biophysics, chemical physics and beyond. By solving Newton's equations of motion in silico, MD simulations allow us to track the time evolution of complex molecular systems in an all-atom, femtosecond resolution, enabling the evaluation of both their thermodynamic and kinetic properties. Though MD simulations are powerful, their effectiveness is often hampered by the large amount of data they produce. For instance, a standard microsecond-long simulation of a protein can easily generate hundreds of gigabytes of data, which can be difficult to analyze. Moreover, the time required to conduct these simulations can be prohibitively long. Microsecond-long simulations often take weeks to complete, whereas the processes of interest may occur on the timescale of milliseconds or even hundreds of seconds. These factors collectively pose significant challenges in leveraging MD simulations for comprehensive analysis and exploration of chemical and biological systems. In this thesis, I address these challenges by leveraging physics-inspired insights to learn unique, useful, and also meaningful low-dimensional representations of complex molecular systems. These representations enable effective analysis and interpretation of the vast amount of data generated from experiments and simulations. These representations have proven to be valuable in providing mechanistic insights into some fundamental problems within theoretical chemistry and biophysics, such as understanding the interplay between long-range and short-range forces in ion pair dissociation and the transformation of proteins from unstable random coils to structured forms. Furthermore, these physics-informed representations play a crucial role in enhancing MD simulations. They facilitate the construction of simplified kinetic models, enabling the generation of dynamical trajectories spanning significantly longer time scales than those accessible by conventional MD simulations. Additionally, they can serve as blueprints to guide the sampling process in combination with existing enhanced sampling methods. Through this thesis, I showcase how the synergy between physics and AI can advance our understanding of molecular systems and facilitate more efficient and insightful analysis in the fields of computational chemistry and biophysics.
Feedback experiments using entangled photons for polarization control in future quantum networks
(2024) Dowling, Evan; Murphy, Thomas E; Roy, Rajarshi; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Control of the measurement frames that project on polarization entangled photons is an important experimental task for near term fiber-based quantum networks. Because of the changing birefringence in optical fiber arising from temperature fluctuations or external vibrations, the polarization projection direction at the end of a fiber channel is unpredictable and varies with time. This polarization drift can cause errors in quantum information protocols, like quantum key distribution, that rely on the alignment of measurement bases between users sharing a quantum state. Polarization control within fiber is typically accomplished using feedback measurements from classical power alignment signals, multiplexed in time or wavelength with the quantum signal that coexist in the same fiber. This thesis explores ways to use only measurements on the entangled photons for polarization control and perform entanglement measures without multiplexing alignment signals. This approach is experimentally less complex and can reduce the noise within the quantum channel arising from the alignment signals. In the first part of this dissertation, we study how to use distributed measurements on polarization entangled photons for polarization drift correction in a 7.1 km deployed fiber between the University of Maryland and the Laboratory of Telecommunication Sciences for two individuals sharing a near maximally entangled Bell state, $\hat \rho = |\Psi^-\rangle\langle\Psi^-|$. In the second part of the dissertation, we examine how to use feedback measurements to maximize the violation of a Bell's inequality used as an entanglement measure. Both polarization drift correction and the maximization of a Bell's inequality violation use iterative optimization algorithms to actuate upstream polarization controllers. In the Bell's inequality investigation, three numerical methods: Bayesian optimization, Nelder-Mead simplex optimization, and stochastic gradient descent are implemented and compared against each other. For complete polarization control and Bell's inequality violation experiments, we developed a polarization and time multiplexed detection system that reduced the number of photon detectors needed and mitigated the demand on the coincidence counting electronics for real-time feedback and control.
NONLINEAR PROPAGATION OF ORBITAL ANGULAR MOMENTUM LIGHT IN TURBULENCE AND FIBER
(2024) Elder, Henry; Sprangle, Phillip; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Light that carries orbital angular momentum (OAM), also referred to as optical vortices or twisted light, is characterized by a helical or twisted wavefront ∝exp[imφ]. In contrast to spin angular momentum (SAM), where photons are limited to two states, OAM allows for, in principle, an infinite set of spatially orthogonal states. OAM-carrying light has found applications ranging from quantum key distribution in free space and guided-wave communication systems, particle trapping and optical tweezers, nanoscopy, and remote sensing. Understanding how OAM light propagates through complex environments, and how to efficiently generate particular OAM states, is critical for any such application. In the first part of this dissertation, we describe how OAM light propagates through a turbulent atmosphere. We build analytic models which describe (1) the OAM mode mixing caused by turbulence, (2) the evolution of short, high-power OAM pulses undergoing the effects of self-phase modulation (SPM) and group velocity dispersion (GVD), and (3) the evolution of high-power Gaussian pulses including SPM, GVD, and turbulence. The models are validated against both experimental data and nonlinear, turbulent pulse propagation simulation programs, the latter of which we have made freely available. We also explore how self-focusing can minimize certain deleterious effects of turbulence for OAM light. The second part of this dissertation considers nonlinear effects of OAM light propagating in azimuthally symmetric waveguides. Such waveguides have so-called spin-orbit (SO) modes, which are quantized based on their total angular momentum (TAM). We develop a generalized theory of four wave mixing-based parametric amplification of SO modes and show that these processes conserve TAM, but under certain circumstances can be taken to conserve SAM and OAM independently. Our theory is validated against a nonlinear multimode beam propagation simulation program which we developed and, again, have made freely available.
Aspects of Unconventional Transport and Quasiparticles in Condensed Matter Systems
(2023) Wu, Huan-Kuang; Sau, Jay D; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Transport in condensed matter system serve as one of the main properties for characterizing its phases and topological properties. A cornerstone in the theoretical efforts in transport is the Boltzmann transport equation (BTE), which describes the low frequency responses of weakly correlated systems through distribution functions of particle-like carriers. The BTE is extremely successful in describing transports in classical systems where the collision is largely uncorrelated and the quasiparticles wave lengths are significantly less than the mean free path. However, such major assumptions made by the Boltzmann transport does not hold in many aspects of modern condensed matter systems. Examples of such scenarios include quantum ballistic transport on the edge of a quantum Hall system, variable-range hopping in an Anderson localized system, and transport above the Mott-Ioeffe-Regel limit in bad metals. In this thesis, we discuss three example systems that exhibit transport properties beyond the conventional Boltzmann framework. First, we will introduce a new family of systems for Majorana zero mode that does not require an external magnetic field. Our proposal is based on a planar Josephson junction setup with a quasi two dimensional spin-orbit coupled electron gas and the usual role of the magnetic field that breaks the time reversal symmetry is taken by three phase biased superconductors. This idea is then simulated with HgTe as the two dimensional material together with realistic parameters. Our result demonstrates a large parameter regime of Majorana zero mode and a topological gap at the order of the superconducting gap. In the second part, we will discuss the physics of plasmonic mode in a Josephson junction chain in its insulating regime. Utilizing the Luther-Emery point of the sine-Gordon model, the polarizability can then be interpreted by the responses of soliton and anti-soliton pairs. we consider the system in both the clean limit and with disorder. Our theory suggests the existence of coherent phase mode in the insulating regime and provides a natural explanation for the frequency-dependent broadening of such a mode. The results is consistent with the recent experiments on the reflection spectrum of Josephson junction chains. Finally, we will look into the problem of Planckian thermal diffusion bound where a mechanism for sub-Planckian thermal diffusion is introduced. We will study a lattice system with large degrees of freedom per unit cell but has limited channel for heat conduction. In the highly-nonlinear regime, the thermal diffusivity can be solved accurately through applications of fluctuation-dissipation theorem. Through numerical simulations, our proposal demonstrate a modification of the lower bound to $D_P/N$ , where $D_P$ is the Planckian diffusivity and $N$ is the per-unit-cell degrees of freedom.
SEARCH FOR BOOSTED SEMI-VISIBLE JETS IN ALL-HADRONIC FINAL STATES WITH THE CMS EXPERIMENT
(2024) Nabili, Sara; Eno, Sarah S.E.; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This dissertation presents a search for the dark sector beyond the standard model (BSM)to be carried out using the Compact Muon Solenoid (CMS) experiment of the Large Hadron Collider (LHC) at CERN. It is developed and evaluated in this work using simulated Monte Carlo data from CMS in preparation for searching the actual Run2 CMS data. The search is focused on the strongly coupled Hidden Valley models that couple with the standard model (SM) via a leptophobic (fully hadronic) Z′ mediator. The final state of resonant production consists of one large jet composed of both visible and invisible particles. This search focuses on the lower mediator mass range (mZ′ ≤ 550 GeV ) with the boosted topology that recoils against the initial state radiation (ISR) jet such that its decay products are contained within a single large-diameter “semi-visible” jet. The main parameters of our model are the mediator mass, the mass of the dark mesons, and the fraction of invisible stable particles. In the event of no discovery, the exclusion limits for mediator mass of 275 to 550 GeV are expected.
CHARGE ORDER AND STRUCTURAL TRANSITION IN TOPOLOGICAL SEMIMETAL FAMILY AAL4
(2023) Saraf, Prathum; Paglione, Johnpierre; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The BaAl$_4$-type structure hosts a variety of interesting and exotic properties, with descendant crystal structure resulting numerous interesting ground states of matter including magnetic, super-conducting and strongly correlated electron phenomena. BaAl$_4$ itself has recently been shown to host a non-trivial topological band structure, but is otherwise a paramagnetic metal. However, the other members of the 1-4 family, such as SrAl$_4$ and EuAl$_4$, exhibit symmetry-breaking ground states including charge density wave (CDW) and magnetic order, respectively. SrAl$_4$ hosts a second transition at 94K that is hysteretic in temperature and is a structural transition to a monoclinic structure. Here I report on the charge density wave in SrAl$_4$ and the effect of the structural transition on the physical and electronic properties of the material. The structural transition is extremely subtle with deviation of around 0.5 degrees from the tetragonal structure but shows significant changes in resistivity, Hall and magnetic susceptibility measurements. This transition is extremely sensitive to disorder and can be suppressed completely by substituting 1$\%$ Ba nominally or using less pure Sr during crystal growth. Furthermore, magnetoresistance in this material is extremely large, and can be up to 140 times at 2K. A combination of magnetoresistance and Hall measurements are used to fit the data to a two band model to extract carrier density and mobility of the charge carriers at 2K. Finally, work was done on the evolution of the charge-ordered state in high quality single crystals of the solid solution series Ba$_{1-x}$Sr$_x$Al$_4$, using transport, thermodynamic and scattering experiments to track the 243 K CDW order in SrAl$_4$ as it is suppressed with Ba substitution until its demise at x =0.5. Neutron and x-ray diffraction measurements reveal a nearly commensurate CDW state in SrAl$_4$ with ordering vector (0,0,0.097) that evolves with Ba substitution to (0,0,0.18) and (0,0,0.21) for x=0.8 and x=0.55, respectively. DFT calculations show a softening of phonons in SrAl$_4$ hinting at electron phonon coupling strength being the source of the charge order in this material. Similar calculations are done on the Ba substitutions to investigate the nature of the charge density waves. With very little change in the lattice parameters in this series, this evolution raises important questions about the nature of the electronic structure that directs a dramatic change in charge ordering.
Analyzing Dynamical Processes with Local Molecular Field Theory
(2023) Zhao, Renjie; Weeks, John D; Chemical Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Local molecular field (LMF) theory provides a framework for describing the collective response of a system to long-range interactions in nonuniform liquids. Based on this theory, different roles played by the short and long-range components of the intermolecular interactions can be disentangled in determining relevant structural and thermodynamic properties in equilibrium. Furthermore, in dynamical processes, nonlocal long-range interactions are often associated with long relaxation times, and can contribute significantly to the stability of the system in different phases. In this thesis, LMF theory is utilized to quantify and analyze the dynamical effects arising from long-range Coulomb interactions in aqueous solutions, while elucidating how they are connected to strong local forces and fluctuations. The first half of the work concerns ionic and dipolar solvation dynamics, which plays an essential role in many solution phase chemical reactions. The physical models of Gaussian-smoothed charge and dipole distributions are conceptualized from LMF theory to investigate the molecular origins of linear and nonlinear effects in solvation dynamics. The long-range component of the solute-solvent electrostatic interaction is shown to underlie the linear response behavior of the system, while the short-range interactions introduce additional nonlinear effects. The LMF-based solvation models further demonstrate their functionality in probing the intrinsic dielectric dispersion of solvent water. The second half of the work is focused on the nucleation processes in the aqueous environment. Simulating crystal nucleation from solutions requires efficient treatments for intermolecular interactions to drive the transitions on time scales affordable to molecular dynamics simulations. For this purpose, a LMF-based molecular model is employed to capture the renormalized long-range interactions, and well-tempered metadynamics is adopted to enhance the fluctuations arising from short-range interactions. By comparing to a short-range reference model, the necessity of long-range interactions in explaining metastability is revealed. Temporal fluctuations and direct evidence for the two-step nucleation mechanism are observed through the analysis using a deep learning-based approach. The results about these two types of dynamical processes contribute to a deeper understanding of the roles of short and long-ranges interactions in the aqueous systems.
MULTI-GEV LASER WAKEFIELD ACCELERATION IN OPTICALLY GENERATED PLASMA WAVEGUIDES
(2023) Shrock, Jaron E; Milchberg, Howard M; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Plasma based electron accelerators offer a promising path to overcoming the significant technological and economic challenges facing the evolution to higher energies by radiofrequency (RF) accelerator technology. In particular, laser-driven wakefield acceleration (LWFA) in plasmas can produce accelerating gradients 1000 times larger than linear RF accelerators, enabling the production of GeV-scale electron bunches in just a few centimeters of acceleration. Efficient LWFA of electrons to this energy scale requires the use of optical guiding to maintain drive laser intensity over much longer distances than the characteristic diffraction length of the pulse. In this dissertation, I will present the first successful implementations of optically generated plasma waveguides in multi-GeV laser wakefield acceleration. I will focus on three primary topics: (1) experimental considerations for generating and diagnosing meter-scale plasma waveguides and the wakefield acceleration process, (2) the experimental demonstration of electron bunches accelerated up to 5 GeV in an all-optical LWFA, and (3) development of a model of drive pulse evolution and electron injection in agreement with a broad range of our experimental results, including the demonstration of localized electron injection through modification of the waveguide properties.

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