Daniel Quinn (He/Him)

Current Research:

Quantifying Energetic Resources in Quantum Machine Learning: A Comprehensive Analysis

Quantum technologies are revolutionising computation and machine learning, unlocking new possibilities beyond classical limits. Quantum Machine Learning (QML) stands at the forefront of this transformation, using quantum computing to enhance classical machine learning techniques.

In my PhD, I aim to develop QML models that leverage quantum algorithms while addressing key challenges such as scalability and noise. A core aspect of my research involves analysing existing models in terms of their energy usage and optimising resource efficiency.

My work focuses on understanding how resource constraints impact learning processes and developing strategies to optimise QML models for these limitations. This research contributes to making QML both practical and efficient for solving real-world problems.

Biography:

I am a PhD student within the Quantum Technologies at Queens (QTeQ) group having started in September 2024 after graduating with an MSci joint honours degree in Mathematics and Computer Science at Queen's. My Masters project focused on investigating the use of machine learning algorithms to predict some governing parameters of a quantum system and it is what sparked my interest in using machine learning in quantum physics.

Research Interests:

• Quantum Computing
• Quantum Machine Learning

Supervisors:

Dr Gabriele De Chiara and Dr Myrta Gruening