2 citations · 2 across the 8 of their papers we have counts for
8 papers
Classical simulation and model concentration in passive linear optics
Léo Monbroussou, Hugo Thomas, Hela Mhiri +2
Passive linear optics is a restricted model of quantum computation, with complexity-theoretic evidence of quantum advantage for sampling tasks and low losses that make it attractiv…
Private training in quantum machine learning
Tigran Sedrakyan, Frédéric Grosshans, Elham Kashefi
With the emergence of machine learning (ML) models trained on large datasets containing potentially sensitive data, a major question in AI safety is how to make learning private wi…
Authentication in Quantum Networks
Christopher Battarbee, Suchetana Goswami, Elham Kashefi +1
In this review, we survey the cryptographic task of authentication from the perspective of quantum communication. We review three main flavours of authentication that are often con…
Boson sampling beyond the dilute regime: second moments and anti-concentration
Hela Mhiri, Hugo Thomas, Léo Monbroussou +3
Boson sampling is a leading candidate for demonstrating quantum advantage in photonic systems. Despite significant experimental and theoretical progress, a characterization of its…
Selectively Blind Quantum Computation
Abbas Poshtvan, Oleksandra Lapiha, Mina Doosti +3
Known protocols for secure delegation of quantum computations from a client to a server in an information theoretic setting require quantum communication. In this work, we investig…
Hybrid Authentication Protocols for Advanced Quantum Networks
Suchetana Goswami, Mina Doosti, Elham Kashefi
Authentication is a fundamental building block of secure quantum networks, essential for quantum cryptographic protocols and often debated as a key limitation of quantum key distri…
Agnostic Process Tomography
Chirag Wadhwa, Laura Lewis, Elham Kashefi +1
Characterizing a quantum system by learning its state or evolution is a fundamental problem in quantum physics and learning theory with a myriad of applications. Recently, as a new…
Information-Theoretic Limits of Quantum Learning via Data Compression
Armando Angrisani, Brian Coyle, Elham Kashefi
Understanding the power of quantum data in machine learning is central to many proposed applications of quantum technologies. While access to quantum data can offer exponential adv…