Birmingham, England, United Kingdom
👋🏽 Hi, my name is Aaron Thomas and I am from London. 🎓 Currently a PhD student at the University of Birmingham studying quantum generative machine learning in the Department of Computer Science. Graduate with an integrated Master of Science in Physics with Theoretical Physics (1st Class Honors) from Imperial College London. 📚 My main focus is in the field of quantum machine learning where I study quantum GANs and investigate their applications and weaknesses in the context of mode collapse and how manipulating latent manifolds impacts this. Earlier in my career, I have applied my machine learning (ML) expertise in my master’s research project where I used a neural predictor to guide the design of quantum ansatz in the context of the variational quantum eigensolver and the 1-D transverse field Ising model. Additionally, I have a strong background in agent-based modelling, which includes designing reinforcement and regret learning algorithms and have undergone a research internship in the DeepWok research lab at Imperial College where my work was centred around parameter-efficient fine-tuning of large language models to improve efficiency and performance on downstream tasks. 💻 I am determined to pursue my PhD and engage in further research endeavours aiming to collaborate with a diverse team to tackle complex problems within my field. I aspire to contribute to the advancement of knowledge in quantum machine learning and make meaningful impacts through interdisciplinary teamwork and cutting-edge research. 🏔 In my free time I enjoy landscape photography and longboarding, you can also find me boxing at my local training club.
As a Research Intern with QunaSys within the QuEnAIS consortium, I contribute to the development of quantum-enhanced AI pipelines for next-generation drug discovery. My work focuses on designing and evaluating hybrid quantum–classical generative models and scalable learning strategies that leverage near-term quantum hardware constraints. I collaborate with interdisciplinary teams across academia and industry to translate quantum machine learning advances into chemically meaningful workflows. Key contributions: • Developing hybrid quantum–classical generative models for molecular design and discovery • Investigating trainable quantum priors (e.g., QCBM-based) for structured latent representations • Implementing and benchmarking variational quantum algorithms under realistic NISQ constraints • Exploring scalable training and gradient-efficient optimisation strategies for QNNs • Collaborating within the QuEnAIS consortium to align quantum ML methods with drug discovery objectives • Contributing to research codebases, experimentation pipelines, and technical reporting
I mentor high school and middle school students working on AI research projects, guiding them through the coding process while teaching them how to think critically and solve complex research problems. • Help students build strong programming skills and deepen their understanding of AI concepts, algorithms, and data science principles. • Foster analytical thinking, encourage creativity and support students in designing, implementing, and presenting research projects.
PhD Candidate in the Department of Computer Science at the University of Birmingham. • Research goals: improve current state-of-the-art quantum generative models through application and theoretical research methods.
Private tutor for A-level Maths and Physics with Ucademy, providing personalized, in-depth support to students, helping them master challenging concepts and develop strong problem-solving skills. • Break down complex theories to make them accessible, while fostering analytical thinking and confidence, helping students excel in exams and develop a deeper appreciation for the subjects. • Tailor teaching style to each student's needs, providing personalized guidance and strategies to support their academic goals.
Tutored multiple students through GCSE and A-Level content to significantly improve their grade and designed resources for the personal education of a student. • Helping younger students with introductory content • Planned and delivered 1-hour science and maths lessons to an A/L class