20 citations · 56 across the 12 of their papers we have counts for
15 papers
Learnt Attacks on Quantum Key Distribution under Channel Noise and Device Drift
Marcel Mordarski, Benjamin Gras, Abdelrahman Shehata +2
Quantum key distribution (QKD) links are provisioned from security analyses of stationary channels, whereas the devices that determine the channel drift between recalibrations. Whe…
Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networks
Marcel Mordarski, Nathan Mani, Arshad Patel +2
Encrypted training relies on keeping server-side updates low-degree. This constraint traditionally excludes models whose weights inhabit a compact Lie group (notably variational qu…
Discrete Feynman-Kac Correctors
Mohsin Hasan, Viktor Ohanesian, Artem Gazizov +5
Discrete diffusion models have recently emerged as a promising alternative to the autoregressive approach for generating discrete sequences. Sample generation via gradual denoising…
Assessing Quantum Advantage for Gaussian Process Regression
Dominic Lowe, M. S. Kim, Roberto Bondesan
Gaussian Process Regression is a well-known machine learning technique for which several quantum algorithms have been proposed. We show here that in a wide range of scenarios these…
Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts
Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian +6
While score-based generative models are the model of choice across diverse domains, there are limited tools available for controlling inference-time behavior in a principled manner…
Accurate Learning of Equivariant Quantum Systems from a Single Ground State
Štěpán Šmíd, Roberto Bondesan
Predicting properties across system parameters is an important task in quantum physics, with applications ranging from molecular dynamics to variational quantum algorithms. Recentl…