“Hybrid Classical-Quantum Learning Applications for Noisy Intermediate-Scale Quantum Computing”
Research unit: UMR 7606 / Laboratoire d’informatique de Paris 6 / LIP 6
Director: Elham Kashefi (QI) & Co-supervisor, Vincent Cohen Addad (RO)
Keywords: Quantum machine learning, quantum advantage, privacy-preserving machine learning, generative models, statistical learning theory
My goal is to design effective quantum machine learning algorithms for Noisy Intermediate-Scale Quantum (NISQ) devices. This task poses challenging questions in quantum complexity and statistical learning theory, as we wish to prove a quantum advantage both in terms of time and sample complexity. Moreover, I am interested in secure delegation protocol for machine learning algorithms. NISQ devices will be remotely available to clients, thus privacy-preserving delegation protocols are crucial when the input contains personal (e.g. biometric) information. I am interested as well in classical machine learning problems, such as clustering and generative modelling.
“Semiconductor quantum dots for integrated quantum technologies”
Research unit: UMR 7588 / Institut des Nano-Sciences de Paris / Photons, magnons et technologies quantiques (PMTeQ)
Director: Valia Voliotis & Co-supervisor : Richard Hostein
Keywords: Quantum dots, Quantum optics, Quantum information, Confined quantum electrodynamics, Photonic cavities
My work during this thesis is based on the use of the spin of a hole trapped in an InAs/GaAs quantum dot as a qubit. My objective is to intricate the spins of the holes of two dots embedded in a photonic crystal via the photons the dots emit in order to create on-chip logical quantum gates. This project is part of the global ongoing effort towards the realization of the elementary component of quantum computers, the qubit. It takes advantage of the recent progress achieved in the engineering and understanding of semiconductor quantum dots.