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Comparison of Different Circuit Ansatz to Optimize Quantum Machine Learning Performance

Description

The field of quantum computing is an exciting area of research that allows quantum mechanics such as superposition, interference, and entanglement to be utilized in solving complex computing problems. One real world application of quantum computing involves applying it to machine learning problems. In this thesis, I explore the effects…

The field of quantum computing is an exciting area of research that allows quantum mechanics such as superposition, interference, and entanglement to be utilized in solving complex computing problems. One real world application of quantum computing involves applying it to machine learning problems. In this thesis, I explore the effects of choosing different circuit ansatz and optimizers on the performance of a variational quantum classifier tasked with binary classification.
ContributorsHsu, Brightan (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12

FuseApply: Reducing Friction in Online Job Applications

Description
The growth in online job boards has made it easier than ever to find and apply for roles online. Unfortunately, since said job boards are, mainly, designed for hiring companies and not job applicants, the applicant interface is high friction and frustrating. With each company (and often each…

The growth in online job boards has made it easier than ever to find and apply for roles online. Unfortunately, since said job boards are, mainly, designed for hiring companies and not job applicants, the applicant interface is high friction and frustrating. With each company (and often each job) that a job-seeker applies for, they need to fill out an application form asking for the same information they have already provided countless times. This thesis explores the effectiveness of FuseApply, a web application and accompanying Chrome extension that reduces the friction involved in filling out these forms by automatically filling out a portion of job applications for users. Results from user experience testing with eleven Arizona State University (ASU) School of Computing and Augmented Intelligence students on real-world job applications demonstrated significant time savings and thus added value for users. On average, FuseApply saved users 33.09 seconds in time completing online job application forms, compared with manually filling them out. A one-tail T-test confirmed that this difference is statistically significant. Users also showed noticeable reduction in frustration with FuseApply. 72.7% of applicants said that they would use FuseApply in the future when applying for jobs, and comments were also positive. Business viability is less clear, as 63.6% of applicants said they would not pay for the software. Results demonstrate that FuseApply is useful and valuable software, but cast doubt on monetization plans.
ContributorsO'Scannlain-Miller, Henry (Author) / Elena Chavez-Echeagaray, Maria (Thesis director) / Benjamin, Victor (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12

PyAntiPhish: A Python-Based Machine Learning Detector of Phishing Websites and An Examination of Relevant URL-Based Features

Description
Phishing is one of most common and effective attack vectors in modern cybercrime. Rather than targeting a technical vulnerability in a computer system, phishing attacks target human behavioral or emotional tendencies through manipulative emails, text messages, or phone calls. Through PyAntiPhish, I attempt to create my own version of an…

Phishing is one of most common and effective attack vectors in modern cybercrime. Rather than targeting a technical vulnerability in a computer system, phishing attacks target human behavioral or emotional tendencies through manipulative emails, text messages, or phone calls. Through PyAntiPhish, I attempt to create my own version of an anti-phishing solution, through a series of experiments testing different machine learning classifiers and URL features. With an end-goal implementation as a Chromium browser extension utilizing Python-based machine learning classifiers (those available via the scikit-learn library), my project uses a combination of Python, TypeScript, Node.js, as well as AWS Lambda and API Gateway to act as a solution capable of blocking phishing attacks from the web browser.
ContributorsYang, Branden (Author) / Osburn, Steven (Thesis director) / Malpe, Adwith (Committee member) / Ahn, Gail-Joon (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

ptr_linked_list_cpp.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

ptr_linked_list_h.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

helper_functions_h.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

string_mapping_h.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

truth_table_cpp.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

linked_list_cpp.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

pgm_error_cpp.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05