Thesis--University of Florida. / Description based on print version record. Typescript. Vita. Bibliography: leaves 118-120.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

In this thesis, details are presented of a numeric simulation of non-adiabatic rapid passage sweeps that were first realized experimentally in 1991. The sweeps are non-composite and generate controllable interference effects which can be used to create high accuracy quantum gates. The simulation is used to optimize the sweep parameters in order to obtain a reliable set of one-qubit quantum gates. A set of sweep parameters was found that approximate the Hadamard, a modified pi/8, a modified phase, and the not gates with an error probability of less than 10^-4. These gates are significant because they form a set that can approximate an arbitrary one qubit unitary operation. The 10^-4 is also significant because is used a rough estimate for the accuracy required to implement fault-tolerant quantum logic.

Secure function evaluation (SFE) on mobile devices, such as smartphones, allows for the creation of new privacy-preserving applications. Generating the circuits on smartphones which allow for executing customized functions, however, is infeasible for most problems due to memory constraints. In this thesis, we develop a new methodology for generating circuits that is memory-efficient. Using the standard SFDL language for describing secure functions as input, we design a new pseudo- assembly language (PAL) and a template-driven compiler, generating circuits which can be evaluated with the canonical Fairplay evaluation framework. We deploy this compiler and demonstrate larger circuits can now be generated on smartphones. We show our compiler's ability to interface with other execution systems and perform optimizations on that execution system. We show how runtime generation of circuits can be used in practice. Our results demonstrate the feasibility of generating garbled circuits on mobile devices. This thesis includes previously published co-authored material.

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Perception relies on the integration of numerous noisy inputs (cues). Cue combination has been relatively understudied in somatosensation, compared to vision and audition. Here, we investigated whether haptic cutaneous and hand configuration cues are combined optimally to discriminate between coin-sized discs of different sizes. When the hand is open such that the thumb and index fingers span the diameter of a disc to contact its perimeter, cutaneous cues occur from the indentation of the skin caused by the curvature of the disc (smaller discs cause greater indentation). Simultaneously, the hand configuration cue (relating to the perceived distance between fingers), provides an additional cue to size. These cues may vary in their reliability. In three experiments involving 34 participants, we measured these cues and considered three hypotheses for how humans may use them: humans rely solely on the least noisy cue (Winner-Take-All Model), humans combine cues based on a simple average (Average-Measurement Model), or humans combine cues via an optimal weighted average (Optimally-Weighted Model). Each experiment tested participants using a two-interval forced-choice (2IFC) paradigm with 3D printed disc stimuli. On each trial, under occluded vision, participants felt two discs sequentially and responded which felt larger. Participants were tested with each finger’s cutaneous cue alone, the hand configuration cue alone, and all cues together. In two experiments, the presented discs were both circular. In a third experiment, unknown to participants, some of the presented discs were oval-like cue conflict stimuli. Participant performance was compared to predictions of the cue combination models. We conclude that humans may combine haptic cutaneous and hand configuration cues optimally to judge the size of held objects. / Thesis / Doctor of Philosophy (PhD) / The sense of touch is understudied compared to the senses of sight and hearing. But simply reaching for a coin without looking involves complex calculations and decision-making. We studied how the brain may approach tasks like this. We were interested in how well the brain deals with multiple sources of information that do not always agree with each other. We investigated these questions in computer simulations and in experiments with undergraduate participants. Using carefully designed 3D-printed discs, we tested dozens of participants across 3 different experiments. Our results show that humans may use information in the best possible way and applications relevant to VR and robot-assisted surgery.

No / The marriage of computation, visual representation, and interactive thinking supports intensive analysis. The goal is not only to permit users to detect expected events, such as might be predicted by models, but also to help users discover the unexpected—the surprising anomalies, changes, patterns, and relationships that are then examined and assessed to develop new insight. The Guest Editors discuss the key issues and challenges associated with discovering the unexpected, as well as introduce the articles that make up this Special Issue.

This dissertation contains four projects: transforming electronic structure Hamiltonian to approximating Ising-type Hamiltonian to enable electronic structure calculations by quantum annealing, quantum-assisted restricted Boltzmann machine for electronic structure calculations, hybrid quantum classical neural network for calculating ground state energies of molecules and qubit coupled cluster single and double excitations variational quantum eigensolver for electronic structure. In chapter 1 we present a general introduction of quantum computer, including a brief introduction of two quantum computing model: gate model and quantum annealing model. We also give a general review about electronic structure calculations on quantum computer. In chapter 2, we show an approximating mapping between the electronic structure Hamiltonian and the Ising Hamiltonian. The whole mapping is enabled by first enlarging the qubits space to transform the electronic structure Hamiltonian to a diagonal Hamiltonian. Then introduce ancilla qubits to transform the diagonal Hamiltonian to an Ising-type Hamiltonian. We also design an algorithm to use the transformed Hamiltonian to obtain the approximating ground energy of the original Hamiltonian. The numerical simulation results of the transformed Hamiltonian for H₂, He₂, HeH⁺, and LiH molecules match the exact numerical calculations of the original Hamiltonian. This demonstrates that one can map the molecular Hamiltonian to an Ising-type Hamiltonian which could easily be implemented on currently available quantum hardware. In chapter 3, we report a hybrid quantum algorithm employing a restricted Boltzmann machine to obtain accurate molecular potential energy surfaces. By exploiting a quantum algorithm to help optimize the underlying objective function, we obtained an efficient procedure for the calculation of the electronic ground state energy for a small molecule system. Our approach achieves high accuracy for the ground state energy for H₂, LiH, H₂O at a specific location on its potential energy surface with a finite basis set. With the future availability of larger-scale quantum computers, quantum machine learning techniques are set to become powerful tools to obtain accurate values for electronic structures. In chapter 4, we present a hybrid quantum classical neural network that can be trained to perform electronic structure calculation and generate potential energy curves of simple molecules. The method is based on the combination of parameterized quantum circuit and measurements. With unsupervised training, the neural network can generate electronic potential energy curves based on training at certain bond lengths. To demonstrate the power of the proposed new method, we present results of using the quantum-classical hybrid neural network to calculate ground state potential energy curves of simple molecules such as H₂, LiH and BeH₂. The results are very accurate and the approach could potentially be used to generate complex molecular potential energy surfaces. In chapter 5, we introduce a new variational quantum eigensolver (VQE) ansatz based on the particle preserving exchange gate to achieve qubit excitations. The proposed VQE ansatz has gate complexity up-bounded to O(<i>n</i>⁴) where <i>n</i> is the number of qubits of the Hamiltonian. Numerical results of simple molecular systems such as BeH₂, H₂O, N₂, H₄ and H₆ using the proposed VQE ansatz gives very accurate results within errors about 10^-3 Hartree.

No description available.

This thesis reports a comprehensive study of the electrostatic solvation energy computation for macromolecules. In the molecular dynamics (MD) simulations it is important to be able to compute the free energy of the system accurately and efficiently. The solvation energy which is dominated by the electrostatics plays a significant role in the dynamics of macromolecules in solution. The standard way of computing the electrostatic solvation energy is to solve the Poisson-Boltzmann (PB) equations. However, due to the large size of the system, the computation cost of solving the PB equation becomes a bottleneck even for the continuum implicit solvent. The alternative method is the newly developed generalized Born (GB) method which gives a good approximation to the PB calculation if the Born radii are properly computed. The computation of the Born radii is the core computation in the GB method and is laborious. In this thesis we present a novel error-bounded fast surface GB approach which significantly improves the traditional surface GB approaches. An analytic algebraic spline model is built for the geometric model of the molecular surfaces which allows one to do the accurate computation on a coarse mesh. Based on the surface GB theory, we develop an algorithm that computes the Born radii by using the fast summation algorithm at a complexity nearly linear in terms of the number of atoms of the molecule and the number of elements on the mesh of the molecular surface. The algorithm is also extended to the electrostatic forces calculations. Finally we propose a hierarchical coarse grained (CG) model aiming at reducing the number of atoms in a macromolecule while still being able to reproduce the geometry as well as the electrostatic interactions of the atomic model. / text