Hello, world!

I’m a research scientist and software developer at Quantinuum.

I’m also a PhD student in the Oxford Quantum Group, supervised by Aleks Kissinger and Bob Coecke.

My research focuses on the compilation and simulation of quantum computation, often with the help of diagrammatic calculii and machine learning techniques to get better results.

Software

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I contribute to several open-source projects related to quantum computing, diagrammatic reasoning, and quantum natural language processing. Click on the images below to see the project descriptions!

• DisCoPy [GitHub]
  
  
  
• λambeq [GitHub]
  
  
  

• Cryptomite [GitHub]
  
  A versatile and user-friendly Python library of randomness extractors with a C++ backend. Cryptomite offers efficient implementations of two-source, seeded, and deterministic randomness extractors for quantum cryptography applications.

• ZXLive [GitHub]
  
  
• pauliopt [Github] and syn [GitHub]
  
  
• ap-form [GitHub]
  
  

Publications

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Theses

• M.Sc. University of Oxford (2020)
  Diagrammatic Design and Study of Ans"{a} tze for Quantum Machine Learning [pdf]
  • Supervisor: Stefano Gogioso
• B.Sc. University of Cambridge (2019)
  Constructing hard examples for the graph isomorphism problem [pdf]
  • Supervisor: Anuj Dawar

Journal articles

• J. van de Wetering, R. Yeung, T. Laakkonen, A. Kissinger (2025)
  Optimal compilation of parametrised quantum circuits [arXiv]
  Quantum 9, 1828

• A. Pappalardo, PE. Emeriau, G. de Felice, B. Ventura, H. Jaunin, R. Yeung, B. Coecke, S. Mansfield (2025)
  Photonic parameter-shift rule: Enabling gradient computation for photonic quantum computers [arXiv]
  Physical Review A 111 (3), 032429

• C. Foreman, R. Yeung, A. Edgington, FJ. Curchod (2025)
  Cryptomite: A versatile and user-friendly library of randomness extractors [arXiv]
  Quantum 9, 1584

• C. Foreman, R. Yeung, FJ. Curchod (2024)
  Statistical testing of random number generators and their improvement using randomness extraction [arXiv]
  Entropy 26 (12), 1053

• Q. Wang, R. Yeung, M. Koch (2024)
  Differentiating and integrating ZX diagrams with applications to quantum machine learning [arXiv]
  Quantum 8, 1491

• M. Koch, R. Yeung, Q. Wang (2024)
  Contraction of ZX diagrams with triangles via stabiliser decompositions [arXiv]
  Physica Scripta 99 (10), 105122

Book chapters

• E. Miranda, R. Yeung, A. Pearson, K. Meichanetzidis, B. Coecke (2022)
  A quantum natural language processing approach to musical intelligence [arXiv]
  Quantum Computer Music: Foundations, Methods and Advanced Concepts, pages 313-356

Conference proceedings

• Q. Huang, D. Winderl, A. Meijer-Van De Griend, R. Yeung (2024)
  Redefining Lexicographical Ordering: Optimizing Pauli String Decompositions for Quantum Compiling [arXiv]
  2024 IEEE International Conference on Quantum Computing and Engineering (QCE)

• B. Poór, Q. Wang, R. Shaikh, L. Yeh, R. Yeung, B. Coecke (2023)
  Completeness for arbitrary finite dimensions of ZXW-calculus, a unifying calculus [arXiv]
  LICS 2023

• R. Shaikh, Q. Wang, R. Yeung (2021)
  How to sum and exponentiate Hamiltonians in ZXW calculus [arXiv]
  QPL 2022

• S. Gogioso, R. Yeung (2022)
  Annealing optimisation of Mixed ZX Phase Circuits [arXiv]
  QPL 2022

• R. Yeung, D. Kartsaklis (2021)
  A CCG-Based Version of the DisCoCat Framework [arXiv]
  SemSpace 2021

• A. Toumi, R. Yeung and G. de Felice (2021)
  Diagrammatic Differentiation for Quantum Machine Learning [arXiv]
  QPL 2021

Conference abstracts

• A. Koziell-Pipe, R. Yeung, M. Sutcliffe (2024)
  Towards Faster Quantum Circuit Simulation Using Graph Decompositions, GNNs and Reinforcement Learning [link]
  The 4th Workshop on Mathematical Reasoning and AI at NeurIPS’24

• F. Charton, A. Krajenbrink, K. Meichanetzidis, R. Yeung (2023)
  Teaching small transformers to rewrite ZX diagrams [link]
  The 3rd Workshop on Mathematical Reasoning and AI at NeurIPS’23

• A. Toumi, G. de Felice and R. Yeung
  DisCoPy for the quantum computer scientist [arXiv]
  QPL 2022

• D. Kartsaklis, I. Fan, R. Yeung, T. Hoffmann, V. Kocijan, C. London, A. Pearson, R. Lorenz, A. Toumi, G. de Felice, K. Meichanetzidis, S. Clark and B. Coecke
  Quantum NLP with lambeq [pdf]
  ACT 2022

Technical reports

• D. Kartsaklis, I. Fan, R. Yeung, A. Pearson, R. Lorenz, A. Toumi, G. de Felice, K. Meichanetzidis, S. Clark and B. Coecke (2021)
  lambeq: An Efficient High-Level Python Library for Quantum NLP [arXiv]