with quantum 
machine learning





Rahko is solving chemistry with quantum machine learning

Rahko is one of the world’s most advanced teams in quantum machine learning.

With the Rahko quantum machine learning platform and a team comprising experts in quantum machine learning, quantum software engineering, and quantum chemistry, Rahko is constantly breaking ground in quantum machine learning for quantum chemistry.

Rahko has a strong focus on solving real-world, commercially valuable problems, in partnership with several of the world’s largest quantum hardware manufacturers and with some of the world’s largest companies whose future capacity to compete depends on their capabilities in chemical simulation.

Our Team

Leonard Wossnig

Chief Executive Officer and Co-founder

Edward Grant

Chief Science Officer and Co-founder

Miriam Cha

Chief Operating Officer and Co-founder

Ian Horobin

Executive Chairman and Co-founder

Shuxiang Cao

Quantum Software Engineer

Hongxiang Chen

Quantum Software Engineer

Jules Tilly

Research Scientist

Dr Thomas Rogers

Quantum Machine Learning Scientist

Dr David McMahon

Computational Quantum Chemist

Dr Pal Mezei

Computational Quantum Chemist

Prof Alexandre Tkatchenko

Professor of Theoretical Chemical Physics, University of Luxembourg

Prof Gábor Csányi

Professor of Molecular Modelling, University of Cambridge

Dr Cedric Weber

Snr Lecturer/Assoc Prof, King's College London

Dr George Booth

Reader in Theoretical Physics, King's College London

Prof Jonathan Tennyson

Professor of Physics, UCL / Chief Scientist, Quantemol / Founder, ExoMol

Prof Mike Payne

Chair of Computational Physics, Univ. of Cambridge

Peter Mountney

CEO, Odin Vision / Royal Society and UCL Entrepreneur in Residence

John Spindler

CEO, Capital Enterprise / CEO, AI Seed

Andy Martyn

Former CTO, Omnicision


  • arXiv

    Computation of molecular excited states on IBMQ using a Discriminative Variational Quantum Eigensolver

    View Publication
  • arXiv

    Machine learning logical gates for quantum error correction

    View Publication
  • New Journal of Physics

    Modelling non-markovian quantum processes with recurrent neural networks

    View Publication
  • New Journal of Physics

    Adversarial quantum circuit learning for pure state approximation

    View Publication

Blog & News


Join our growing, multidisciplinary team. 


Join our growing, multidisciplinary team. 

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