Hi, I'm Samuel, a quantum machine learning researcher.

 Quantum + AI

Since embarking on my doctoral journey, I've been dedicated to exploring the intricate relationship between classical and quantum computing in the context of AI. My research primarily focuses on creating AI agents that excel in solving diverse challenges while prioritizing information security in their implementation.

One of my major milestones was achieving a groundbreaking integration of reinforcement learning into the quantum realm through a novel agent featuring trainable quantum circuits. Additionally, I introduced the quantum long short-term memory (QLSTM) model, demonstrating its superiority over classical LSTM models in specific conditions related to time-series modeling and reinforcement learning.

Beyond theoretical exploration, I've applied these concepts practically across various domains. From developing a quantum convolutional neural network for high-energy physics event classification to leveraging graph convolutional neural networks for chemical and biomedical analysis, my work spans diverse fields.

My investigations also encompass the augmentation of privacy features in speech recognition, natural language processing, and the development of AI models tailored for advanced investment advice.

My passion lies in the convergence of AI and quantum information science. I'm committed to advancing their applications in physics, chemistry, biomedicine, and beyond, contributing to the ongoing evolution of these groundbreaking technologies. 

 Research Fields

Selected work