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.
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.

Talks and Seminars

Reviews

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

Education

• 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

Contact

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