I'm Chukwudubem Umeano*.
Chukwudubem Umeano*.
Quantum Computing and Quantum Machine Learning Researcher.
Personal Profile
I am currently a 4th Year Physics PhD student at the University of Exeter, working within the QuDOS group. My interests are mainly in applications of quantum computing in the near term. I study theory and applications related to quantum machine learning, quantum simulation and quantum algorithms.
This PhD follows on from the successful completion of my BSc in Physics at the University of Warwick and my MSc in Quantum Fields and Fundamental Forces at Imperial College, London. Within the latter, I also scored 90% on my final dissertation, where I wrote a detailed review of Quantum Algorithms.
You can find links to all my publications on my Google Scholar, and my full profile can be found on my CV. If you would like to get in touch, you can drop me an email or contact me on Linkedin or X.
When I'm not cracking my brain over physics, I spend my time solving word puzzles, playing football or basketball (casually), or watching football or basketball (less casually). My numerous ramblings on the beautiful game can also be found on X.
Academic summary
- 2021-Present | PhD, Physics, University of Exeter
- Research interests: Quantum Machine Learning, Quantum Algorithms, Quantum Simulation.
- 2020-2021 | MSc, Quantum Fields and Fundamental Forces, Imperial College London
- Grade: Merit (69.6%)
- Selected Modules: Quantum Information, Particle Symmetries, Quantum Electrodynamics, Quantum Field Theory, Unification
- Dissertation: Quantum Algorithms: A Review (90%).
- 2017-2020 | BSc, Physics, University of Warwick
- Grade: First Class (80%). I was awarded the Seymour Prize, given to the highest achieving student in the class
- Selected Modules: Quantum Mechanics, Electrodynamics, Condensed Matter Physics, Mathematical Methods for Physicists, Statistical Physics, Computational Physics
- Final Project: Computational recognition of crystalline environments (75%).
Papers
Publications
- Quantum subspace expansion approach for simulating dynamical response functions of Kitaev spin liquids
C. Umeano, F. Jamet, L. P. Lindoy, I. Rungger and O. Kyriienko
Phys. Rev. Materials 9, 034401 (2025). - What can we learn from Quantum Convolutional Neural Networks?
C. Umeano, A. E. Paine, V. E. Elfving and O. Kyriienko
Adv Quantum Technol. 2400325 (2024). - The topology of data hides in quantum thermal states
S. Scali, C. Umeano and O. Kyriienko
APL Quantum 1, 036106 (2024). - Quantum topological data analysis via the estimation of the density of states
S. Scali, C. Umeano and O. Kyriienko
Phys. Rev. A 110, 042616 (2024).
Preprints
- Quantum community detection via deterministic elimination
C. Umeano, S. Scali and O. Kyriienko
arXiv:2412.13160 (2024).
- Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification?
C. Umeano, S. Scali and O. Kyriienko
arXiv:2409.01496 (2024).
- Ground state-based quantum feature maps
C. Umeano and O. Kyriienko
arXiv:2404.07174 (2024).
- Geometric quantum machine learning of BQPA protocols and latent graph classifiers
C. Umeano, V. E. Elfving and O. Kyriienko
arXiv:2402.03871 (2024).
C. Umeano, S. Scali and O. Kyriienko
arXiv:2412.13160 (2024).
C. Umeano, S. Scali and O. Kyriienko
arXiv:2409.01496 (2024).
C. Umeano and O. Kyriienko
arXiv:2404.07174 (2024).
C. Umeano, V. E. Elfving and O. Kyriienko
arXiv:2402.03871 (2024).
Conferences and workshops
- APS Global Physics Summit 2025
Anaheim, California, USA
Talk: Quantum subspace expansion approach for simulating dynamical response functions of Kitaev spin liquids - Quantum Simulators and Quantum Digital Twins: The Masterclass 2025
London, UK
Poster: Quantum simulation of quantum spin liquids - Quantum Techniques in Machine Learning 2024
Melbourne, Australia
Talk: Quantum topological data analysis: learning graph properties from thermal states - Quantum Simulation with Tensor Networks for Digital Twins 2024
Exeter, UK
Talk: Quantum subspace expansion approach for simulating dynamical response functions of Kitaev spin liquids - Workshop on Quantum Machine Learning 2024
London, UK
Talk: Geometric quantum machine learning of BQPA protocols - Quantum Computing Theory in Practice 2024
Edinburgh, UK
Poster: What can we learn from quantum convolutional neural networks? - Q-Sci-ML Research meeting 2023
Amsterdam, the Netherlands
Talk: What can we learn from quantum convolutional neural networks? - Quantum Techniques in Machine Learning 2023
CERN, Geneva, Switzerland
Poster: Quantum subspace expansion for the Kitaev model - International Summer School in Quantum Technologies 2023
Birmingham, UK
Poster: Quantum simulation of quantum spin liquids - Quantum Computing Theory in Practice 2023
Cambridge, UK
Poster: Quantum simulation of quantum spin liquids - Mini workshop on quantum computing 2022
Bristol, UK
Talk: Quantum simulation of quantum spin liquids
