Physics
Permanent URI for this communityhttp://hdl.handle.net/1903/2269
Browse
9 results
Search Results
Item BUILDING KINETIC MODELS FOR COMPLEX SYSTEMS WITH ARBITRARY MEMORIES(2022) Tsai, Sun-Ting; Tiwary, Pratyush; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Analyzing time series from complex dynamical systems in nature is a common yet challenging task in scientific computation since these time series are usually high-dimensional. To apply our physics intuitions to these dynamical systems often requires projecting these time series to certain low-dimensional degrees of freedom, which often introduces complicated memory effect. A simplest and classic example can be a 2-dimensional coupled differential equation. When one only looks at one of the Cartesian coordinates, one loses the predictability to predict what will happen next given the current 1-dimensional coordinate. The well-known solution is to describe the solution using the eigenvector, and the coupled equation is decoupled into a constant and a 1-dimensional memoryless equation. However, it can be imagined in a more complicated system we may have to look back to more time steps in the past, and it can be impossible to obtain a simple 1-dimensional eigenvector. In this work, we examine such memory effect within time series generated from Langevin dynamics, Molecular Dynamics (MD) simulations, and some experimental time series. We also develop computational methods to minimize and model such memory effects using statistical mechanics and machine learning. In recent years, MD simulation has become a powerful tool to model complex molecular dynamics in physics, chemistry, material science, biology, and many other fields. However, rare events such as droplet formation, nucleation, and protein conformational changes are hard to sample using MD simulations since they happen on the timescales far away from what all-atom MD simulation can reach. This makes MD simulation less useful for studying the mechanism of rare event kinetics. Therefore, it is a common practice to perform enhanced sampling techniques to help sample rare events, which requires performing dimensionality reduction from atomic coordinates to a low-dimensional representation that has a minimal memory effect. In the first part of this study, we focus on reducing the memory effect by capturing slow degrees of freedom using a set of low-dimensional reaction coordinates (RCs). The RCs are a low-dimensional surrogate of the eigenvector in the example of coupled equations. When describing the system using RCs, other dimensions become constant except fast randomly fluctuating noise. These RCs can then be used to help reproducing correct kinetic connectivity between metastable states using enhanced sampling methods such as metadynamics. We demonstrate the utility of our method by applying them to the droplet formation from the gaseous phase of Lennard-Jones particles and the conformational changes of a small peptide Ace-Ala3-Nme. The second part of the study aims at modeling another type of memory coming from intrinsic long-term dependency induced by ignored fast degrees of freedom wherein we utilize one of the fundamental machine learning techniques called the recurrent neural network to model non-Markovianity within time-series generated from MD simulations. This method has been shown to work not only on the molecular model of alanine dipeptide but also on experimental time series taken from single-molecule force spectroscopy. At the end of this second part, we also improve this method to extrapolate physics that the neural network had never seen in the training dataset by incorporating static or dynamical constraints on the path ensemble it generates.Item Simulating many-body quantum spin models with trapped ions(2021) Kyprianidis, Antonis; Monroe, Christopher R; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Richard Feynman in 1981 suggested using a quantum machine to simulate quantum mechanics.Peter Shor in 1994 showed that a quantum computer could factor numbers much more efficiently than a conventional one. Since then, the explosion of the quantum information field is attesting to how motivation and funding work miracles. Research labs in the field are multiplying, commercial companies manufacturing prototypes are proliferating, undergraduate Physics curricula incorporate more than one courses in aspects of quantum information, quantum advantage over classical computers has been claimed, and the United States and European Union will be spending more than \$$10^9$ each in quantum information over the next few years. Naturally, this expansion has led to diversification of the devices being developed. The quantum information systems that cannot simulate an arbitrary evolution, but are specialized in a specific set of Hamiltonians, are called quantum \emph{simulators}. They enjoy the luxury of being able to surpass computational abilities of classical computers \emph{right now}, at the expense of only doing so for a narrow type of problem. Among those systems, ions trapped in vacuum by electric fields and manipulated with light have proved to be a leading platform in emulating quantum magnetism models. In this thesis I present trapped-ion experiments realizing a prethermal discrete time crystal. This exotic phase occurs in non-equilibrium matter subject to an external periodic drive. Normally, the ensuing Floquet heating maximizes the system entropy, leaving us with a trivial, infinite-temperature state. However, we are able to parametrically slow down this heating by tuning the drive frequency. During the time window of slow thermalization, we define an order parameter and observe two different regimes, based on whether it spontaneously breaks the discrete time translation symmetry of the drive or it preserves it. Furthermore, I demonstrate a simple model of electric field noise classically heating an ion in an anharmonic confining potential. As ion traps shrink, this kind of noise may become more significant. And finally, I discuss a handful of error sources. As quantum simulation experiments progress to more qubits and complicated sequences, accounting for system imperfections is becoming an integral part of the process.Item Scaling Quantum Computers with Long Chains of Trapped Ions(2021) Egan, Laird Nicholas; Monroe, Christopher; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Quantum computers promise to solve models of important physical processes, optimize complex cost functions, and challenge cryptography in ways that are intractable using current computers. In order to achieve these promises, quantum computers must both increase in size and decrease error rates. To increase the system size, we report on the design, construction, and operation of an integrated trapped ion quantum computer consisting of a chain of 15 171Yb+ ions with all-to-all connectivity and high-fidelity gate operations. In the process, we identify a physical mechanism that adversely affects gate fidelity in long ion chains. Residual heating of the ions from noisy electric fields creates decoherence due to the weak confinement of the ions transverse to a focused addressing laser. We demonstrate this effect in chains of up to 25 ions and present a model that accurately describes the observed decoherence. To mitigate this noise source, we first propose a new sympathetic cooling scheme to periodically re-cool the ions throughout a quantum circuit, and then demonstrate its capability in a proof-of-concept experiment. One path to suppress error rates in quantum computers is through quantum error correction schemes that combine multiple physical qubits into logical qubits that robustly store information within an entangled state. These extra degrees of freedom enable the detection and correction of errors. Fault-tolerant circuits contain the spread of errors while operating the logical qubit and are essential for realizing error suppression in practice. We demonstrate fault-tolerant preparation, measurement, rotation, and stabilizer measurement of a distance-3 Bacon-Shor logical qubit in our quantum computer. The result is an encoded logical qubit with error rates lower than the error of the entangling operations required to operate it.Item Study and Mitigation of Transverse Resonances with Space Charge Effects at the University of Maryland Electron Ring(2020) Dovlatyan, Levon; Antonsen, Thomas M; Beaudoin, Brian L; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Research at the intensity frontier of particle physics has led to the consideration of accelerators that push the limits on achievable beam intensities. At high beam intensities Coulomb interactions between charged particles generate a space charge force that complicates beam dynamics. The space charge force can lead to a range of nonlinear, intensity- limiting phenomena that result in degraded beam quality and current loss. This is the central issue faced by the next generation of high-intensity particle accelerators. An improved understanding of the interaction of the space charge forces and transverse particle motion will help researchers better design around these limiting issues. Furthermore, any scheme able to mitigate the impacts of such destructive interactions for space charge dominated beams would help alleviate a significant limitation in reaching higher beam intensities. Experimental work addressing these issues is presented using the University of Maryland Electron Ring (UMER). This dissertation presents experimental studies of space charge dominated beams, and in particular the resonant interaction between the transverse motion of the beam and the periodic perturbations that occur due to the focusing elements in a circular ring. These interactions are characterized in terms of the tune shifts, Qx and Qy, that are the number of transverse oscillations (in and out of the plane of the ring) per trip around the ring. Resonances occur for both integer and half-integer values of tune shift. Particle tune measurement tools and resonance detection techniques are developed for use in the experiment. Results show no shift for either the integer (Qx = 7.0, Qy = 7.0) or half-integer (Qx = 6.5, Qy = 6.5) resonance bands as a function of space charge. Accepted theory predicts only a shift in the half-integer resonance case. A second experiment testing the potential mitigation of transverse resonances through nonlinear detuning of particle orbits from resonance driving terms is also presented. The study included the design, simulation, and experimental test of a quasi-integrable accelerator lattice based on a single nonlinear octupole channel insert. Experiments measured a nonlinear amplitude dependent tune shift within the beam on the order of ∆Qx ≈ 0.02 and ∆Qy ≈ 0.03. The limited tolerances on accelerator steering prevented measuring any larger tune shifts.Item UNCOVERING PATTERNS IN COMPLEX DATA WITH RESERVOIR COMPUTING AND NETWORK ANALYTICS: A DYNAMICAL SYSTEMS APPROACH(2020) Krishnagopal, Sanjukta; Girvan, Michelle; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this thesis, we explore methods of uncovering underlying patterns in complex data, and making predictions, through machine learning and network science. With the availability of more data, machine learning for data analysis has advanced rapidly. However, there is a general lack of approaches that might allow us to 'open the black box'. In the machine learning part of this thesis, we primarily use an architecture called Reservoir Computing for time-series prediction and image classification, while exploring how information is encoded in the reservoir dynamics. First, we investigate the ways in which a Reservoir Computer (RC) learns concepts such as 'similar' and 'different', and relationships such as 'blurring', 'rotation' etc. between image pairs, and generalizes these concepts to different classes unseen during training. We observe that the high dimensional reservoir dynamics display different patterns for different relationships. This clustering allows RCs to perform significantly better in generalization with limited training compared with state-of-the-art pair-based convolutional/deep Siamese Neural Networks. Second, we demonstrate the utility of an RC in the separation of superimposed chaotic signals. We assume no knowledge of the dynamical equations that produce the signals, and require only that the training data consist of finite time samples of the component signals. We find that our method significantly outperforms the optimal linear solution to the separation problem, the Wiener filter. To understand how representations of signals are encoded in an RC during learning, we study its dynamical properties when trained to predict chaotic Lorenz signals. We do so by using a novel, mathematical fixed-point-finding technique called directional fibers. We find that, after training, the high dimensional RC dynamics includes fixed points that map to the known Lorenz fixed points, but the RC also has spurious fixed points, which are relevant to how its predictions break down. While machine learning is a useful data processing tool, its success often relies on a useful representation of the system's information. In contrast, systems with a large numbers of interacting components may be better analyzed by modeling them as networks. While numerous advances in network science have helped us analyze such systems, tools that identify properties on networks modeling multi-variate time-evolving data (such as disease data) are limited. We close this gap by introducing a novel data-driven, network-based Trajectory Profile Clustering (TPC) algorithm for 1) identification of disease subtypes and 2) early prediction of subtype/disease progression patterns. TPC identifies subtypes by clustering patients with similar disease trajectory profiles derived from bipartite patient-variable networks. Applying TPC to a Parkinson’s dataset, we identify 3 distinct subtypes. Additionally, we show that TPC predicts disease subtype 4 years in advance with 74% accuracy.Item RECEPTOR MOBILITY AND CYTOSKELETAL DYNAMICS AT THE IMMUNE SYNAPSE: THE ROLE OF ACTIN REGULATORY PROTEINS(2020) Rey Suarez, Ivan Adolfo; Upadhyaya, Arpita; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Spatial and temporal regulation of actin and microtubule dynamics is of utmost importance for many cellular processes at different sub-cellular length scales. This is particularly relevant for cells of the immune system, which must respond rapidly and accurately to protect the host, where B cells and T cells are the main players during the adaptive immune response. An understanding of the biophysical principles underlying cytoskeletal dynamics and regulation of signaling will help elucidate the fundamental mechanisms driving B and T cell immune response. B cell receptor (BCR) diffusivity is modulated by signaling activation, however the factors linking mobility and signaling state are not completely understood. I used single molecule imaging to examine BCR mobility during signaling activation and a novel machine learning based method to classify BCR trajectories into distinct diffusive states. Inhibition of actin dynamics downstream of the actin nucleating factors Arp2/3 and formins resulted decreased BCR mobility. Loss of the Arp2/3 regulator, N-WASP, which is associated with enhanced signaling, leads to a predominance of BCR trajectories with lower diffusivity. Furthermore, loss of N-WASP reduces diffusivity of the stimulatory co-receptor CD19, but not that of unstimulated FcγRIIB, an inhibitory co-receptor. Our results implicate the dynamic actin network in fine-tuning receptor mobility and receptor-ligand interactions, thereby modulating B cell signaling. Activation of T cells leads to the formation of the immunological synapse (IS) with an antigen presenting cell (APC). This requires T cell polarization and coordination between the actomyosin and microtubule cytoskeleton. The interactions between the different cytoskeletal components during T cell activation are not well understood. I use high-resolution fluorescence microscopy to study actin-microtubule crosstalk during IS formation. Microtubules in actin rich zones display more deformed shapes and higher dynamics compared to MTs at the actin-depleted region. Chemical inhibition of formin and myosin activation reduced MT deformations, suggesting that actomyosin contractility plays an important role in defining MT shapes. Interestingly MT growth was slowed by formin inhibition and resulting enrichment of Arp2/3 nucleated actin networks. These observations indicate an important mechanical coupling between the actomyosin and microtubule systems where different actin structures influence microtubule dynamics in distinct ways.Item Many-Body Dephasing in a Cryogenic Trapped Ion Quantum Simulator(2019) Kaplan, Harvey B.; Monroe, Christopher R; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)While realizing a fully functional quantum computer presents a long term technical goal, in the present, there are small to mid-sized quantum simulators (up to $\sim 100$ qubits), that are capable of approaching specialized problems. The quantum simulator discussed here uses trapped ions to act as qubits and is housed in a cryogenically cooled vacuum chamber in order to reduce the background pressure, thereby increasing ion chain length and life-time. The details of performance and characterization of this cryogenic apparatus are discussed, and this system is used to study many-body dephasing in a finite-sized quantum spin system. How a closed quantum many-body system relaxes and dephases as a function of time is important to understand when dealing with many-body spin systems. In this work, the first experimental observation of persistent temporal fluctuations after a quantum quench is presented with a tunable long-range interacting transverse-field Ising Hamiltonian. The fluctuations in the average magnetization of a finite-size system of spin-$1/2$ particles are measured presenting a direct measurement of relaxation dynamics in a non-integrable system. This experiment is in the regime where the properties of the system are closely related to the integrable Hamiltonian with global coupling. The system size is varied in order to investigate the dependence on finite-size scaling, and the system size scaling exponent extracted from the measured fluctuations is consistent with theoretical prediction.Item NANOSCALE THERMODYNAMICS OF MECHANOSENSITIVE ION CHANNELS AND THEIR ROLE IN THE MECHANISM OF OSMOTIC FITNESS OF MICROBES(2018) Cetiner, Ugur; Sukharev, Sergei; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bacterial mechanosensitive channels are major players in cells’ ability to cope with hypo-osmotic stress. Excess turgor pressure due to fast water influx is reduced as the channels, triggered by membrane tension, open and release osmolytes. In bacteria, the bulk release of ions and other osmolytes is mainly mediated by two families of mechanosensitive channels: MscS and MscL. The MscL family channels form large non-selective pathways in the membrane and gate near the lytic tension. In this way, they act as a final back up mechanism against osmotic downshock. MscS family channels require less tension to open and display great diversity in structure and functionality. Chapter 2 describes the first multifaceted phenomenological study of the emergency osmolyte release system in wild type Pseudomonas aeruginosa in comparison with E.coli. We recorded the kinetics of cell equilibration reported by light scattering responses to osmotic up- and down-shocks using the stopped-flow technique. We also performed the first electrophysiological characterization of the mechanosensitive ion channels in Pseudomonas aeruginosa. We presented a quantitative biophysical description of “osmotic fitness” which would be of interest to microbiologists, epidemiologists, ecologists and general environmental scientists. Chapter 3 presents the combined theoretical and experimental analysis of the full functional cycle of the bacterial channel MscS, which plays a major role in osmotic adjustments and environmental stability of most bacteria. We modeled MscS gating as a finite state continuous-time Markov chain and obtained analytical expressions for the steady state solution and the inactivated state area (which is experimentally hard to determine). In Chapter 4, we derived a general formalism to extract the free energy difference between the closed and open states of mechanosensitive ion channels (ΔF) from non-equilibrium work distributions associated with the channels’ gating. Our new approach bridges the gap between recent developments in non-equilibrium thermodynamics of small systems and ion channel biophysics. Our study also serves as an experimental verification of non-equilibrium work relations in a biological system. Therefore, the results in this thesis are sufficiently general and would be of interest to a broad community.Item UNCOVERING FUNDAMENTAL MECHANISMS OF ACTOMYOSIN CONTRACTILITY USING ANALYTICAL THEORY AND COMPUTER SIMULATION(2018) Komianos, James Eric; Papoian, Garegin A; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Actomyosin contractility is a ubiquitous force-generating function of almost all eukaryotic organisms. While more understanding of its dynamic non-equilibrium be- havior has been uncovered in recent years, little is known regarding its self-emergent structures and phase transitions that are observed in vivo. With this in mind, this thesis aims to develop a state-of-the-art computational model for the simulation of actomyosin assemblies, containing detailed cytosolic reaction-diffusion processes such as actin filament treadmilling, cross-linker (un)binding, and molecular motor walking. This is explicitly coupled with novel mechanical potentials for semi-flexible actin filaments. Then, using this simulation framework combined with other ana- lytical approaches, we propose a novel mechanism of contractility in a fundamental actomyosin structural element, derived from a thermodynamic free energy gradi- ent favoring overlapped actin filament states when passive cross-linkers are present. With this spontaneous cross-linking, transient motors such as non-muscle myosin II can generate robust network contractility in a collective myosin II-cross-linker ratcheting mechanism. Finally, we map the phases of contractile behavior of disor- dered actomyosin using this theory, showing explicitly the cross-linking, motor and boundary conditions required for geometric collapse or tension generation in a net- work comprised of those elements. In this theory, we move away from the sarcomeric contractility mechanism typically reconciled in disordered non-muscle structures. It is our hope that this study adds theoretical knowledge as well as computational tools to study the diverse contractile assemblies found in non-muscle actomyosin networks.