1 citations · 1 across the 2 of their papers we have counts for
9 papers
Efficient training of photonic quantum generative models
Felix Gottlieb, Chayma Faraji, Rawad Mezher +3
The topic of generative learning has gained traction within the field of quantum machine learning, in particular with the advent of train-on-classical, deploy-on-quantum methods. T…
Mitigating photon loss in linear optical quantum circuits
James Mills, Rawad Mezher
Photon loss rates set an effective upper limit on the size of computations that can be run on current linear optical quantum devices. We present a family of techniques designed to…
Exponential improvement in benchmarking multiphoton interference
Rodrigo M. Sanz, Emilio Annoni, Stephen C. Wein +4
Several photonic quantum technologies rely on the ability to generate multiple indistinguishable photons. Benchmarking the level of indistinguishability of these photons is essenti…
Establishing Baselines for Photonic Quantum Machine Learning: Insights from an Open, Collaborative Initiative
Cassandre Notton, Vassilis Apostolou, Agathe Senellart +28
The Perceval Challenge is an open, reproducible benchmark designed to assess the potential of photonic quantum computing for machine learning. Focusing on a reduced and hardware-fe…
On the role of coherence for quantum computational advantage
Hugo Thomas, Pierre-Emmanuel Emeriau, Rawad Mezher +3
Quantifying the resources available to a quantum computer appears to be necessary to separate quantum from classical computation. Among them, entanglement, nonstabilizerness and co…
New designs of linear optical interferometers with minimal depth and component count
Timothée Goubault de Brugière, Rawad Mezher, Sebastian Currie +1
We adapt an algorithm for CNOT circuits synthesis based on the Bruhat decomposition to the design of linear optical circuits with Mach-Zehnder interferometers (MZI). The synthesis…