Eric Gyabeng Fuakye

Quantum Computing · Machine Learning · Particle Physics

Portrait of Eric Gyabeng Fuakye

Quantum Computing for Machine Learning

Graduate researcher with a strong background in particle physics, data analysis, and machine learning, now focusing on quantum computing for machine learning under the supervision of Prof. F.X. Wu.

Resarch Focus: exploring how quantum algorithms can accelerate and enrich modern ML methods.

View Research Focus Windows CMD Guide
Martensville, Saskatchewan, Canada University of Saskatchewan Physics · Biomedical Engineering · AI

Research overview

Eric is a physics graduate with over five years of research and teaching experience across Canada and Ghana, including work at Concordia University, the University of Regina, and CERN. His expertise spans data analysis, scientific programming, and machine learning applied to high‑energy physics and biomedical contexts.[file:43]

Building on this foundation, Eric is now transitioning into quantum computing for machine learning, aiming to design and analyze quantum‑enhanced algorithms for data‑intensive problems in science and health.

Current focus

  • Quantum algorithms for supervised and unsupervised learning.
  • Bridging particle physics style data analysis with quantum ML workflows.
  • Applications of quantum‑inspired methods to biomedical and high‑energy physics datasets.

Quantum circuits & data encoding

Visualizing how classical data can be embedded into quantum states, and how circuit depth and noise impact learnability.

Bloch spheres Parameterised circuits Feature maps

Hybrid quantum–classical pipelines

Designing workflows where quantum models act as modules inside larger ML systems, evaluated with familiar Python tools.

PyTorch QML libraries Optimization

From collider data to qubits

Leveraging experience from CERN and Higgs phenomenology to build realistic, physics‑motivated benchmarks for quantum ML.

ROOT Higgs 2HDM Event classification

Quantum computing for machine learning – reading list

After meeting with Prof. F.X. Wu on December 17, 2025, the following core texts were selected as the starting point for Eric’s research in quantum computing for machine learning.

Foundations of quantum computing

  • Introduction to Quantum Algorithms via Linear Algebra – conceptual bridge between linear algebra and quantum algorithms, ideal for a physics and ML background.
  • Quantum Computing for Computer Scientists – structured view of quantum computation for algorithm design and complexity.

Quantum machine learning & applications

  • Quantum Machine Learning: What Quantum Computing Means to Data Mining (Wittek) – early, influential view on quantum‑enhanced data mining.
  • Quantum Computing and Quantum Machine Learning Classification – A Survey – overview of classification‑oriented QML methods and challenges.
  • Quantum Computing for Drug Discovery (2023) – modern application‑oriented perspective linking quantum algorithms with real‑world problems.

Concept art: quantum waves

Abstract visualization of quantum interference patterns, representing superposition and entanglement as resources for learning.

Neural networks on qubits

Imagery combining neural network diagrams with quantum circuit motifs to capture Eric’s hybrid classical–quantum focus.

From textbooks to experiments

Collage‑style representation of the five recommended texts transitioning into simulated and (eventually) hardware‑level experiments.

Academic & research experience

Teaching and Research Assistant – University of Regina

Department of Physics, Regina, Saskatchewan · 2020 – 2025

  • Conducted advanced data analysis using C++, Python, MATLAB, and ROOT for physics research projects.[file:43]
  • Developed machine learning models for predictive data analysis and supported experimental workflows.[file:43]
  • Led laboratory instruction and mentoring for undergraduate students in physics courses.[file:43]

Teaching Assistant – Concordia University

Department of Physics, Montréal, Quebec · 2018 – 2020

  • Taught labs and tutorials in electricity and magnetism and graded assignments and exams.[file:43]
  • Supported student learning through review sessions and office hours.[file:43]

Summer Student Researcher – CERN

European Centre for Nuclear Research (CERN), Geneva, Switzerland · Summer 2017

  • Contributed to the ROOT data analysis framework, improving visualization for international particle physics experiments.[file:43]
  • Collaborated with technical teams on experimental data analysis and user support.[file:43]

Teaching Assistant – KNUST

Kwame Nkrumah University of Science & Technology, Kumasi, Ghana · 2016 – 2017

  • Assisted in teaching core physics courses and supported labs in classical mechanics, statistical mechanics, and electromagnetism.[file:43]

Selected publication

  • M. Frank, E. G. Fuakye, and M. Toharia, “Restricting the parameter space of type-II two-Higgs-doublet models with CP violation,” Physical Review D 106, 035010 (2022). DOI: 10.1103/PhysRevD.106.035010 .[file:43]

This work on Higgs boson phenomenology and beyond‑the‑Standard‑Model physics provides a strong theoretical and computational base for Eric’s transition into quantum computing and machine learning.

Contact

For collaboration, supervision, or questions about quantum computing, machine learning, or particle physics, please reach out by email.