I am a third-year Ph.D. student at the University of Paris, under the direction of Iordanis KERENIDIS from the Algorithm and Complexity team. I am working on developing new algorithms for long and short term quantum computers. My expertise 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. 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)

Publications and preprints

Talks and Seminars


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

Blog Post


  • 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



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