From the 1 of 10 linked papers with an AI index.
10 papers
Parameter-Shift Rules for Gradients in Boson Sampling Experiments
Marius Trudeau, Pierre-Emmanuel Emeriau, Nicolás Quesada
The paper derives n‑th order parameter‑shift rules for computing gradients of Fock boson‑sampling probabilities in lossy interferometers, shows that such rules generally cannot be…
Shedding light on classical shadows: learning photonic quantum states
Hugo Thomas, Ulysse Chabaud, Pierre-Emmanuel Emeriau
Learning quantum state properties is both a fundamental and practical problem in quantum information theory. Classical shadows have emerged as an efficient method for estimating pr…
Quantum Energetic Advantage before Computational Advantage in Boson Sampling
Ariane Soret, Nessim Dridi, Stephen C. Wein +3
Understanding the energetic efficiency of quantum computers is essential for assessing their scalability and for determining whether quantum technologies can outperform classical c…
A unified framework for Bell inequalities from continuous-variable contextuality
Carlos Ernesto Lopetegui-González, Gaël Massé, Enky Oudot +6
Although the original EPR paradox was formulated in terms of position and momentum, most studies of these phenomena have focused on measurement scenarios with only a discrete numbe…
Towards practical secure delegated quantum computing with semi-classical light
Boris Bourdoncle, Pierre-Emmanuel Emeriau, Paul Hilaire +3
Secure Delegated Quantum Computation (SDQC) protocols are a vital piece of the future quantum information processing global architecture since they allow end-users to perform their…
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…