Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Improving The Scalability Of Neural Network Surface Code Decoders, Kevin Wu
Improving The Scalability Of Neural Network Surface Code Decoders, Kevin Wu
Undergraduate Honors Theses
Quantum computers have recently gained significant recognition due to their ability to solve problems intractable to classical computers. However, due to difficulties in building actual quantum computers, they have large error rates. Thus, advancements in quantum error correction are urgently needed to improve both their reliability and scalability. Here, we first present a type of topological quantum error correction code called the surface code, and we discuss recent developments and challenges of creating neural network decoders for surface codes. In particular, the amount of training data needed to reach the performance of algorithmic decoders grows exponentially with the size of …
Identifying Social Media Users That Are Susceptible To Phishing Attacks, Zoe Metzger
Identifying Social Media Users That Are Susceptible To Phishing Attacks, Zoe Metzger
Undergraduate Honors Theses
Phishing scams are a billion-dollar problem. According to Threatpost, in 2020, business email compromise phishing attacks cost the US economy $ 1.8 billion. Social media phishing scams are also on the rise with 74% of companies experiencing social media attacks in 2021 according to Proofpoint. Educating users about phishing scams is an effective strategy for reducing phishing attacks. Despite efforts to combat phishing, the number of attacks continues to rise, likely indicative of a reticence of users to change online behaviors. Existing research into predicting vulnerable social media users that are susceptible to phishing mostly focuses on content analysis of …
Quantum Federated Learning: Training Hybrid Neural Networks Collaboratively, Anneliese Brei
Quantum Federated Learning: Training Hybrid Neural Networks Collaboratively, Anneliese Brei
Undergraduate Honors Theses
This thesis explores basic concepts of machine learning, neural networks, federated learning, and quantum computing in an effort to better understand Quantum Machine Learning, an emerging field of research. We propose Quantum Federated Learning (QFL), a schema for collaborative distributed learning that maintains privacy and low communication costs. We demonstrate the QFL framework and local and global update algorithms with implementations that utilize TensorFlow Quantum libraries. Our experiments test the effectiveness of frameworks of different sizes. We also test the effect of changing the number of training cycles and changing distribution of training data. This thesis serves as a synoptic …
A Pain Free Nociceptor: Predicting Football Injuries With Machine Learning, Andrew Lyubovsky
A Pain Free Nociceptor: Predicting Football Injuries With Machine Learning, Andrew Lyubovsky
Undergraduate Honors Theses
Injuries are a significant aspect of every sport, with the ability to impact a player’s career and the success of a team in their season. As sensor data is able to pick up on a player’s physical state, recently it has been analyzed for its ability to predict player injuries. We inspect the predictive power of player stats, subjective player responses, GPS data, and training load data in forecasting game injuries from an NCAA American football team during the 2019 season. Data processing techniques are used to remove noise and decrease correlated data, and as large portions of the data …
Molecular Cluster Fragment Machine Learning Training Techniques To Predict Energetics Of Brown Carbon Aerosol Clusters, Emily E. Chappie
Molecular Cluster Fragment Machine Learning Training Techniques To Predict Energetics Of Brown Carbon Aerosol Clusters, Emily E. Chappie
Undergraduate Honors Theses
Density functional theory (DFT) has become a popular method for computational work involving larger molecular systems as it provides accuracy that rivals ab initio methods while lowering computational cost. Nevertheless, computational cost is still high for systems greater than ten atoms in size, preventing their application in modeling realistic atmospheric systems at the molecular level. Machine learning techniques, however, show promise as cost-effective tools in predicting chemical properties when properly trained. In the interest of furthering chemical machine learning in the field of atmospheric science, I have developed a training method for predicting cluster energetics of newly characterized nitrogen-based brown …