Physics Theses and Dissertations

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    Increasing Helicity towards Dynamo Action with Rough Boundary Spherical Couette Flows
    (2022) Rojas, Ruben; Lathrop, Daniel P; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The dynamo action is the process through which a magnetic field is amplified and sustained by electrically conductive flows. Galaxies, stars and planets, all exhibit magnetic field amplification by their conductive constituents. For the Earth in particular, the magnetic field is generated due to flows of conductive material in its outer core. At the University of Maryland, our Three-meter diameter spherical Couette experiment uses liquid sodium between concentric spheres to mimic some of these dynamics, giving insight into these natural phenomena. Numerical studies of Finke and Tilgner (Phys. Rev. E, 86:016310, 2012) suggest a reduction in the threshold for dynamo action when a rough inner sphere was modeled by increasing the poloidal flows with respect to the zonal flows and hence increasing helicity. The baffles change the nature of the boundary layer from a shear dominated to a pressure dominated one, having effects on the angular momentum injection. We present results on a hydrodynamics model of 40-cm diameter spherical Couette flow filled with water, where torque and velocimetry measurements were performed to test the effects of different baffle configurations. The selected design was then installed in the 3-m experiment. In order to do that, the biggest liquid sodium draining operation in the history of the lab was executed. Twelve tons of liquid sodium were safely drained in a 2 hours operation. With the experiment assembled back and fully operational, we performed magnetic field amplification measurements as a function of the different experimental parameters including Reynolds and Rossby numbers. Thanks to recent studies in the hydrodynamic scale model, we can bring a better insight into these results. Torque limitations in the inner motor allowed us to inject only 4 times the available power; however, amplifications of more than 2 times the internal and external magnetic fields with respect to the no-baffle case was registered. These results, together with time-dependent analysis, suggest that a dynamo action is closer than before; showing the effect of the new baffles design in generating more efficient flows for magnetic field amplification. We are optimistic about new short-term measurement in new locations of the parameter space, and about the rich variety of unexplored dynamics that this novel experiment has the potential to reach. These setups constitute the first experimental explorations, in both hydrodynamics and magnetohydrodynamics, of rough boundary spherical Couette flows as laboratory candidates for successful Earth-like dynamo action.
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    (2022) Guo, Andrew; Gorshkov, Alexey V; Swingle, Brian; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Quantum many-body systems with long-range interactions—such as those that decay as a power-law in the distance between particles—are promising candidates for quantum information processors. Due to their high degree of connectivity, they are potentially capable of generating entanglement more quickly than systems limited to local interactions, which may lead to faster computational speeds. The questions of the nature of the speed-ups they can achieve—as well as how to program these long-range systems to achieve such speed-ups—are, therefore, of prime theoretical interest. To understand the nature of the speed-ups achievable, it is natural to consider the dual question, which is what are the fundamental speed limits in quantum many-body systems? Given that most systems relevant to quantum computation operate in the non-relativistic regime—where information typically propagates at velocities far below the threshold set by the speed of light—the absence of an absolute speed limit seems to allow for unbounded rates of information transfer. However, in 1972, Lieb and Robinson restored a notion of locality in systems with local interactions by proving a bound that led to light-cone-like regions outside which information propagation is exponentially suppressed. The question of whether similar bounds could be proven for long-range systems has remained open—until recently. In this thesis, we will describe results related to the now-fuller picture of the fundamental rates of information propagation in power-law-interacting systems. First, we consider the regime of ``strongly long-range'' interactions, for which velocities can grow unboundedly with system size. We will present Lieb-Robinson-type bounds for these systems and also outline a protocol that can transfer quantum states as fast as these bounds will allow. We will also discuss the implications of these bounds for quantum information scrambling. The second part of the thesis will study how protocols for transferring quantum states quickly can be used to perform multi-qubit gates. In particular, we will demonstrate how the power of long-range interactions allows one to implement the unbounded fanout gate asymptotically faster than systems with local interactions. This result also implies the hardness of simulating the dynamics of long-range systems evolving for superlogarithmic times, and demonstrates the potential for insights from quantum many-body physics to lead to a more powerful toolbox for quantum computation. Finally, we will address the question of fundamental speed limits in quantum systems that are open to the environment. A priori, it may seem surprising that such speed limits may exist, since non-unitary processes may break locality constraints. However, we show that under certain assumptions such as linearity and Markovianity of the bath, one can restore a notion of locality using Lieb-Robinson-type bounds. We use the resulting bounds to constrain the entanglement structure of the steady states of open long-range systems, a first step towards proving the area law for such systems.
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    (2022) Banerjee, Amitava; Ott, Edward; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Inference of the connectivity structure of a network from the observed dynamics of the states of its nodes is a key issue in science, with wide-ranging applications such as determination of the synapses in nervous systems, mapping of interactions between genes and proteins in biochemical networks, distinguishing ecological relationships between different species in their habitats etc. In this thesis, we show that certain machine learning models, trained for the forecasting of experimental and synthetic time-series data from complex systems, can automatically learn the causal networks underlying such complex systems. Based on this observation, we develop new machine learning techniques for inference of causal interaction network connectivity structures underlying large, networked, noisy, complex dynamical systems, solely from the time-series of their nodal states. In particular, our approach is to first train a type of machine learning architecture, known as the ‘reservoir computer’, to mimic the measured dynamics of an unknown network. We then use the trained reservoir computer system as an in silico computational model of the unknown network to estimate how small changes in nodal states propagate in time across that network. Since small perturbations of network nodal states are expected to spread along the links of the network, the estimated propagation of nodal state perturbations reveal the connections of the unknown network. Our technique is noninvasive, but is motivated by the widely used invasive network inference method, whereby the temporal propagation of active perturbations applied to the network nodes are observed and employed to infer the network links (e.g., tracing the effects of knocking down multiple genes, one at a time, can be used infer gene regulatory networks). We discuss how we can further apply this methodology to infer causal network structures underlying different time-series datasets and compare the inferred network with the ground truth whenever available. We shall demonstrate three practical applications of this network inference procedure in (1) inference of network link strengths from time-series data of coupled, noisy Lorenz oscillators, (2) inference of time-delayed feedback couplings in opto-electronic oscillator circuit networks designed the laboratory, and, (3) inference of the synaptic network from publicly-available calcium fluorescence time-series data of C. elegans neurons. In all examples, we also explain how experimental factors like noise level, sampling time, and measurement duration systematically affect causal inference from experimental data. The results show that synchronization and strong correlation among the dynamics of different nodal states are, in general, detrimental for causal network inference. Features that break synchrony among the nodal states, e.g., coupling strength, network topology, dynamical noise, and heterogeneity of the parameters of individual nodes, help the network inference. In fact, we show in this thesis that, for parameter regimes where the network nodal states are not synchronized, we can often achieve perfect causal network inference from simulated and experimental time-series data, using machine learning techniques, in a wide variety of physical systems. In cases where effects like observational noise, large sampling time, or small sampling duration hinder such perfect network inference, we show that it is possible to utilize specially-designed surrogate time-series data for assigning statistical confidence to individual inferred network links. Given the general applicability of our machine learning methodology in time-series prediction and network inference, we anticipate that such techniques can be used for better model-building, forecasting, and control of complex systems in nature and in the lab.
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    Non Traditional Solvent Effect On Protein Behavior
    (2022) Lee, Pei-Yin; Matysiak, Silvina; Chemical Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Protein preservation has been a long lasting research topic due to its importance in many bio-pharmaceutical applications. A ”cold chain” is a commonplace solution to protein preservation, which stores biochemical products at a refrigerated temperature. A big advantage of cold chain is that the storing process is straightforward, without many further processes before the use of stored bio-products. However, it can also experience malfunction of the cooling system and results in economic lost and health care crisis. Ionic liquids (ILs), as a type of non traditional solvents, consist only of ions and are reported to be a potential candidate to replace the use of cold chain. The advantages of ILs include low flammability, high conductivity and less toxicity compared to some organic solvents. The most interesting feature of ILs is their extremely large number of cation-anion combinations, that can be tailored for specific use according to different needs. This thesis aims to investigate specific mechanism behind how ILs modulate protein behavior, specifically, how ILs affect protein stability, activity, and aggregation. We approach the research questions through the lens of molecular dynamics (MD) simulations and complement with experimental findings. In the first part of the thesis we first investigate the effects of two imidazolium based ILs (1-ethyl-3-methylimidazolium ethylsulfate, [EMIM]+[EtSO4]− and 1-ethyl-3-methylimidazolium diethylphosphate, [EMIM]+[Et2PO4]−) on lysozyme stability and activity. We collaborate with an experiment group at the University of Massachusetts (Bermudez lab) to complement our simulation results. Both ILs are found to destabilize lysozyme stability. In addition, both the cation and anions lower the stability of lysozyme, but in a different fashion. [EMIM]+ interacts with an Arg-Trp-Arg bridge that is critical in lysozyme stability through π–π and cation–π interactions, leading to a local induced destabilization. On the other hand, both anions interact with the whole protein surface through short-range electrostatic interactions, with [Et2PO4]− having a stronger effect than [EtSO4]−. Lysozyme activity is also reduced by the presence of the two ILs, but can be recovered after rehydration. It is found that the protein-ligand complex is less stable in the presence of ILs. In addition, a dense cloud of [EMIM]+ is found in the vicinity of the lysozyme active site residues, possibly leading to a competition with the sugar ligand. A fast leaving of these [EMIM]+ is observed after rehydration, which explains the reappearance of the active site and the recover of lysozyme activity. Although classical all-atom MD simulations can provide us with a great deal of microscopic information, they are often limited by the temporal-spatial scale of the simulated systems. For example, systems with high viscosity solvents or systems involving large number of atoms will be difficult to reach convergence for all-atom MD. In this case, coarse grained (CG) MD can come into play to achieve the desired time- and length- scales. The faster sampling obtained from CG MD is achieved by reducing the degree of freedom of the system and by removing local energetic barriers. In CG MD, similar atoms are grouped to functional groups and thus the free energy landscape is smoothen. We develop a novel CG MD named ”Protein Model with Polarizability and Transferability (ProMPT)”. The novelty of this model is the inclusion of the charged dummies that can result in change of dipoles. These dipoles can reflect the change of environments and thus allow the model to respond to different environmental stimulus. We validate ProMPT with several benchmark proteins: Trp-cage, Trpzip4, villin, ww-domain, and β-α-β. ProMPT is able to simulate folding-unfolding and secondary structure transformation with minimal constraints, which is not feasible with previous CG models. In addition, ProMPT can also reproduce the experimental results for the dimerization of glycophorin A (GpA) with different point mutations. Here we demonstrate the ability of the model to capture the change of conformational space caused by point mutation. In the last part of this thesis, we combine ProMPT and an in-house CG IL model to study the effects of [TEA]+[Ms]− on amyloid beta 16-22 (Aβ16−22) aggregation. Aβ16−22 is the hydrophobic core region and is the smallest fragment of Aβ that can fibrilize. Aβ has been extensively linked to the pathogenesis of the Alzheimer’s disease. [TEA]+[Ms]− is reported to suppress the formation of β-sheets and induce helices at high concentration. From our results, both β-sheet content and the aggregate size decrease with the increase of IL concentration, which are in agreement with experiments. Aggregates can form in both water and IL, but with different morphologies. In water, a nice hydrophobic core involving Phe-Phe interactions can form as well as intact β-sheet contacts. In addition, a cross β-sandwich structure is also observed, as seen from previous literature. However, the same hydrophobic core can not persist in the presence of IL. Aggregate structures in IL are not stable over time due to the [TEA]+-Phe interaction. Helicity is also computed for Aβ16−22 in water and in IL at different concentrations and a positive correlation is found. The increase in helicity at high [TEA]+[Ms]− concentration can be explained by the reduction of the inter-peptide contacts, which then increases the opportunity for the peptides to form helical structures. Single peptide studies also reveal that [TEA]+[Ms]− increases the helicity, possibly through cation-induced dipole enhancement. In this thesis, a series of detailed investigations on the effects of ILs on protein behavior is performed. Specific interactions between IL functional groups and protein local/global structures are examined. The mechanisms we studied here will help constructing a holistic view for the design of IL-protein pair applications. The construction of the new CG protein/IL model provides another tool for the scientific community to study secondary structure transformation, folding- unfolding, and other biochemical processes that are sensitive to the environment with CG MD.
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    (2022) Cian, Ze-Pei; Hafezi, Mohammad; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    With the rapid development of programmable quantum simulators, the quantum states can be controlled with unprecedented precision. Thus, it opens a new opportunity to explore the strongly correlated phase of matter with new quantum technology platforms. In quantum simulators, one can engineer interactions between the microscopic degree of freedom and create exotic phases of matter that presumably are beyond the reach of natural materials. Moreover, quantum states can be directly measured instead of probing physical properties indirectly via optical and electrical responses of material as done in traditional condensed matter. Therefore, it is pressing to develop new approaches to efficiently prepare and characterize desired quantum states in the novel quantum technology platforms. In this thesis, we discuss the preparation and characterization of the topologically ordered state in nobel quantum technological platforms. First, we show that optically driven monolayer graphene in the quantum Hall regime creates an effective bilayer quantum Hall system. It provides a flexible platform for engineering quantum Hall phases. We use infinite density matrix renormalization group (iDMRG) techniques combined with exact diagonalization (ED) to show that the system exhibits a non-abelian bilayer Fibonacci phase at filling fraction $\nu = 2/3$. Moreover, at integer filling $\nu = 1$, the system exhibits quantum Hall ferromagnetism. Using Hartree-Fock theory and exact diagonalization, we show that excitations of the quantum Hall ferromagnet are topological textures known as skyrmions. Then we turn our attention to the characterization of the topological invariants from a ground state wave function of the topological order phase and the implementation in noisy intermediate quantum devices. Using topological field theory and tensor network simulations, we demonstrate how to extract the many-body Chern number (MBCN) given a bulk fractional quantum Hall wave function. We further propose an ancilla-free experimental scheme for measuring the MBCN without requiring any knowledge of the Hamiltonian. Specifically, we use the statistical correlations of randomized measurements to infer the MBCN of a wave function. Finally, we discuss an unbiased numerical optimization scheme to systematically find the Wilson loop operators given a ground state wave function of a gapped, translationally invariant Hamiltonian on a disk. We then show how these Wilson loop operators can be cut and glued through further optimization to give operators that can create, move, and annihilate anyon excitations. We then use these operators to determine the braiding statistics and topological twists of the anyons, yielding a way to fully characterize topological order from the bulk of a ground state wave function.