I am a second-year Ph.D. student, under the direction of Iordanis KERENIDIS from the Algorithm and Complexity team. I am working on developing new algorithms for long term and short term quantum computers. My speciality is 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. Partial Differential Equations solvers for near term quantum circuits (NISQ).

These routines are at the core of new quantum algorithm 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.

email : landman@irif.fr
address : Sophie Germain, IRIF, room 4059

• Quantum Algorithms for Deep Convolutional Neural Networks
  ICLR 2020
• q-means: A quantum algorithm for unsupervised machine learning
  NeurIPS 2019, pp.4136-4146

• The Quantum Wave in Computing
  Simons Institute, UC Berkeley, California, February-March 2020
• Groupe de travail des thésards du LPSM
  Paris, 10 February 2020
• Thirty-fourth Conference on Neural Information Processing Systems
  Vancouver, Canada December 2019
• "Ordinateur quantique – quel impact pour les entreprises demain ?"
  Conférence Centrale Tech, Paris, 2 October 2019
• Quantum Innovators in computer science and mathematics
  Institute for Quantum Computing, University of Waterloo, Canada, October 2019

• Quantum Science and Technology, IOP Publishing.
  Topic : Training of quantum variational algorithms

• École Polytechnique, Palaiseau, France
  2013-2018 Master degree (“Diplôme Polytechnicien”) in Electronics 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

• Concours Général de Physique Mention, France 2011.