Currently Third year Postodoctoral Researcher at The University of Edinburgh, Scotland, leading the Quantum Machine Learning team under Elham's Kashefi supervision. Our research goals concerns the provability of quantum advantage for near term quantum machine learning proposals such as variational quantum circuits and hamming weight preserving quantum circuits.

• Quantum Fourier Networks for Solving Parametric PDEs
  Arxiv preprint
• Quantum Methods for Neural Networks and Application to Medical Image Classification
  Arxiv preprint
• Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
  ICLR 2023
• Quantum Vision Transformers
  Arxiv preprint
• Medical image classification via quantum neural networks
  Arxiv preprint
• Classical and Quantum Algorithms for Orthogonal Neural Networks
  Arxiv preprint
• Quantum Bayesian Neural Networks
  Arxiv preprint
• Quantum Spectral Clustering
  Physical Review A - 2021 APS
• Quantum Algorithms for Deep Convolutional Neural Networks
  Proceedings of the 8th International Conference on Learning Representation (ICLR) - 2020
• q-means: A quantum algorithm for unsupervised machine learning
  Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS) - 2019, pp.4136-4146

Previously, I was a Ph.D. student at the University of Paris, under the direction of Iordanis KERENIDIS from the IRIF Algorithm and Complexity team. I worked on developing new algorithms for long and short term quantum computers, in particular quantum algorithms for machine learning. My main research consists in identifying classical machine learning algorithms that could potentially be adapted to the quantum computing framework with provable speedup.

We develop fundamental quantum circuits to process data, defining routines for linear algebra, graph, analytic computations. In my recent works I have been focused on developing:

1. Quantum distance calculation between vectors in superposition with logarithmic dependence.
2. Quantum convolution product between two 3D tensors.
3. Neural Network Quantum backpropagation for convolution and pooling layers.
4. Faster quantum tomography with $\ell_{\infty}$ norm guarantee.
5. Quantum access to Adjency graph, Incidence graph, and Laplacian graph with projection on its eigenspace.
6. Neural networks implementation on near term quantum circuits (NISQ).

These routines are at the core of new quantum algorithms for unsupervised machine learning such as k-means clustering, Gaussian mixture models, spectral clustering, as well as fully connected and convolutional neural networks, and many others. Link to PhD Thesis "Quantum Algorithms for Unsupervised Machine Learning and Neural Networks" (2021)

• Quantum Techniques in Machine Learning (QTML 2023) CERN, Switzerland, 2023
  1 long Talk “Quantum Fourier Networks for Solving Parametric PDEs” + 3 Posters
• Quantum Information Processing (QIP 2023) Gent, Belgium 2023
  2 Posters
• Quantum Techniques in Machine Learning (QTML 2022) Naples, Italy, 2022
  2 Posters
• EPRI AI 2022 Rome, Italy, 2022
  Talk “Quantum Machine Learning”
• Collège de France "Recent advances on Quantum Algorithms" Paris, France 2021
  Talk: “Recent Quantum Algorithms for Machine Learning and Neural Networks”
• 8th International Conference on Learning Representations (ICLR 2020) Addis-Abeba, Ethiopia 2020
  Poster: “Quantum algorithms for Deep Convolutional Neural Networks”
• Quantum Computing Theory In Practice (QCTIP 2020) Riverlane, Cambridge, UK, 6-8 April 2020
  Poster: “Quantum algorithms for Deep Convolutional Neural Networks”
• The Quantum Wave in Computing Simons Institute, UC Berkeley, California, February-March 2020
  Talk: “Quantum algorithms for unsupervised learning”
• 34th Conference on Neural Information Processing Systems (NeurIPS 2019) Vancouver, Canada December 2019
  Poster: “q-means: A quantum algorithm for unsupervised machine learning”
• "Ordinateur quantique – quel impact pour les entreprises demain ?" Conférence Centrale Tech, Paris, 2 October 2019
  Talk: “Quantum computing and applications to machine learning”
• Quantum Innovators in computer science and mathematics Institute for Quantum Computing, University of Waterloo, Canada, October 2019
  Talk: “Quantum algorithms for Deep Convolutional Neural Networks”

Topics : Quantum algorithms for ML / Training of variational quantum circuits for ML :

• Machine Learning: Science and Technology, Nature Quantum Information, NeurIPS 2020,2021, Quantum Science and Technology, Physics Letters A , ICLR 2021, ICML 2021, etc.

• "How and when quantum computers will improve machine learning?", Le Lab Quantique (Medium). 2019
• "Quantum Machine Learning: a faster clustering algorithm on a quantum computer", Towards Data Science (Medium). 2019
• "Deep Convolutional Neural Networks for Quantum Computers", Towards Data Science (Medium). 2020

• École Polytechnique, Palaiseau, France
  2013-2018 MSc (“Diplôme Polytechnicien”) in Electrical Engineering and Machine learning
• UC Berkeley, California, USA
  Fall Semester 2017 : Visiting Scholar, Data Science and Entrepreneurship
• Lycée Henri IV, Paris, France
  2011-2013 Classe Préparatoire

• Prix solennel de thèse de la chancellerie des Universités de Paris, France 2022.
• Concours Général de Physique Mention, France 2011.

email : jonas ldmn [@] gmail .com