Daniel Quinn

Daniel Quinn

2nd Year Phd Student

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About Me

Hi, I’m Daniel! I’m a Physics PhD student at Queen’s University Belfast (QUB), supervised by Prof. Gabriele De Chiara. My research focuses on Quantum Machine Learning (QML) models, specifically the project is on quantifying the energetic resources required for such models. I’ve been actively working with quantum algorithms, utilising tools like Qiskit and QIBO to implement these models.

I’m passionate about exploring anything quantum and anything machine learning. If you’re interested in collaborating on an exciting QML project or have ideas to share, feel free to reach out to me at dquinn53@qub.ac.uk.

Education

Queen’s University Belfast
PhD in Physics
Sep 2024 - Present
My current research involves generative QML models, particularly denoising diffusion probabilistic models, and extending them in innovative directions.

Queen’s University Belfast
MSci in Mathematics and Computer Science
1st Class Honours
Sep 2020 - Jun 2024
Relevant modules include Analysis of Algorithms, Deep Learning, Advanced Quantum Theory, and Quantum Information Processing.

Publications

Conferences and Talks

LMS Research School on Quantum Machine Learning and Hamiltonian Simulation

Jun 2025, Sabhal Mor Ostaig, (Gaelic College), Sleat, Isle of Skye

  • Gave a short talk on Conditioned Quantum Denoising Diffusion Models
  • See school website

Teaching Experience

Marker, QUB Sep 2025 - Present

  • Graded homework assignments for a core first-year module covering Multivariable Calculus, Differential Equations, Linear Algebra, and Complex Numbers.
  • Provided constructive feedback to students while maintaining fairness and consistency in assessments.

Tutor, QUB Sep 2024 - Apr 2025

  • Delivered concise weekly summaries for a first-year module on Real Analysis and Linear Algebra to reinforce students’ understanding.
  • Supported students with weekly homework assignments, ensuring they stayed on track academically.

Marker, QUB Jan 2024 - Apr 2024

  • Graded homework assignments for a second-year module on Complex Analysis and Partial Differential Equations.

Professional Development

  • Completed a course on university teaching methods, covering:
    Small Group Teaching and Lab Demonstrations, Learning and Facilitation Approaches, Building a Positive Learning Environment, Inclusive Teaching, Accessibility, Learning Design, Assessment and Feedback (2023).

Skills

  • Quantum Machine Learning Tools: Experienced in Qiskit and currently TensorCircuit for fast quantum circuit evaluation.
  • Mathematics & Computational Proficiency: Exceptional skills in advanced mathematics and algorithmic design.
  • HPC & Shell Scripting: Experienced in using Bash and shell scripting to manage and execute jobs on high-performance computing (HPC) clusters.
  • Collaboration & Adaptability: Experienced in mentoring peers, leading projects, and adapting to complex challenges in high-pressure environments.