Physical Sciences and Mathematics Commons^™

Towards Scalable Characterization Of Noisy, Intermediate-Scale Quantum Information Processors, Travis Luke Scholten

Physics & Astronomy ETDs

In recent years, quantum information processors (QIPs) have grown from one or two qubits to tens of qubits. As a result, characterizing QIPs – measuring how well they work, and how they fail – has become much more challenging. The obstacles to characterizing today’s QIPs will grow even more difficult as QIPs grow from tens of qubits to hundreds, and enter what has been called the “noisy, intermediate-scale quantum” (NISQ) era. This thesis develops methods based on advanced statistics and machine learning algorithms to address the difficulties of “quantum character- ization, validation, and verification” (QCVV) of NISQ processors. In the …

Beam-Target Helicity Asymmetry E In K0Λ And K0Σ0 Photoproduction On The Neutron, D. H. Ho, R. A. Schumacher, A. D’Angelo, A. Deur, J. Fleming, C. Hanretty, T. Kageya, F. J. Klein, E. Klempt, M. M. Lowry, H. Lu, V. A. Nikonov, P. Peng, A. M. Sandorfi, A. V. Sarantsev, I. I. Strakovsky, N. K. Walford, X. Wei, R. L. Workman, K. P. Adhikari, S. Adhikari, D. Adikaram, Z. Akbar, J. Ball, L. Barion, M. Bashkanov, C. D. Bass, M. Battaglieri, I. Bedlinskiy, A. S. Biselli, Wesley P. Gohn

Physics and Astronomy Faculty Publications

We report the first measurements of the E beam-target helicity asymmetry for the γ^→ n^→ → K⁰Λ and K⁰Σ⁰ channels in the energy range 1.70 ≤ W ≤ 2.34 GeV. The CLAS system at Jefferson Lab uses a circularly polarized photon beam and a target consisting of longitudinally polarized solid molecular hydrogen deuteride with low background contamination for the measurements. The multivariate analysis method boosted decision trees is used to isolate the reactions of interest. Comparisons with predictions from the KaonMAID, SAID, and Bonn-Gatchina models are presented. These results will help separate the …

A Machine Learning Algorithm For Identifying And Tracking Bacteria In Three Dimensions Using Digital Holographic Microscopy, Manuel Bedrossian, Marwan El-Kholy, Daniel Neamati, Jay Nadeau

Physics Faculty Publications and Presentations

Digital Holographic Microscopy (DHM) is an emerging technique for three-dimensional imaging of microorganisms due to its high throughput and large depth of field relative to traditional microscopy techniques. While it has shown substantial success for use with eukaryotes, it has proven challenging for bacterial imaging because of low contrast and sources of noise intrinsic to the method (e.g. laser speckle). This paper describes a custom written MATLAB routine using machine-learning algorithms to obtain three-dimensional trajectories of live, lab-grown bacteria as they move within an essentially unrestrained environment with more than 90% precision. A fully annotated version of the software used …

Motion-Induced Artifact Mitigation And Image Enhancement Strategies For Four-Dimensional Fan-Beam And Cone-Beam Computed Tomography, Matthew J. Riblett

Theses and Dissertations

Four dimensional imaging has become part of the standard of care for diagnosing and treating non-small cell lung cancer. In radiotherapy applications 4D fan-beam computed tomography (4D-CT) and 4D cone-beam computed tomography (4D-CBCT) are two advanced imaging modalities that afford clinical practitioners knowledge of the underlying kinematics and structural dynamics of diseased tissues and provide insight into the effects of regular organ motion and the nature of tissue deformation over time. While these imaging techniques can facilitate the use of more targeted radiotherapies, issues surrounding image quality and accuracy currently limit the utility of these images clinically.

The purpose of …

Unsupervised Machine Learning Account Of Magnetic Transitions In The Hubbard Model, Kelvin Ch'ng, Nick Vazquez, Ehsan Khatami

Faculty Publications

We employ several unsupervised machine learning techniques, including autoencoders, random trees embedding, and t-distributed stochastic neighboring ensemble (t-SNE), to reduce the dimensionality of, and therefore classify, raw (auxiliary) spin configurations generated, through Monte Carlo simulations of small clusters, for the Ising and Fermi-Hubbard models at finite temperatures. Results from a convolutional autoencoder for the three-dimensional Ising model can be shown to produce the magnetization and the susceptibility as a function of temperature with a high degree of accuracy. Quantum fluctuations distort this picture and prevent us from making such connections between the output of the autoencoder and …