Elham Kashefi
Personal Chair in Quantum Computing at the University of Edinburgh
CNRS Professor at Sorbonne University
Research Interests:
Security Analysis and Design of Quantum Protocols
Experimental Implementation of Quantum Protocols
Foundation of Quantum Mechanics
Quantum Parallel Computing
Quantum Complexity Theory, Interactive Proof Systems
New Models for Quantum Computing
Kashefi has pioneered a transdisciplinary research on the structure, behaviour, and interactions of quantum technology, from formal and foundational aspects all the way to actual industrial use-case delivery. Kashefi's research team innovates across a broad range of platforms (photonic, superconducting, ion trap) with an integrated software research programme (simulation, modelling and verification) delivering impact in quantum computing (machine learning, cryptanalysis) and quantum networks (quantum cryptography, quantum cloud computing) in a certifiable way (provable security, practical benchmarking, verification of computation). She is a recipient of an EPSRC Early Career and back-to-back Established Career Fellowship awards, a recipient of the Les Margaret Intrapraneur prize (France, 2021), and an elected member of the Young Academy of Scotland (Royal Society of Edinburgh). She has been awarded several UK, EU, and US grants for research in quantum computing, software, communication, and applications. As a global community leader, Kashefi champions the quantum application ecosystem: she is NQCC Chief scientists; co-author of the EU Quantum Software Manifesto; Senior science team member for the UK QCS hub. She has served on programme and steering committees of flagship conferences of the field (e.g. QCrypt, TQC, QIP), published 130+ articles across high impact venues and delivered 300+ invited talks in major workshops, conferences, symposiums, and industry events.
Featured Publications:
Verifiable blind quantum computing with trapped ions and single photons
Asymmetric Quantum Secure Multi-Party Computation With Weak Clients Against Dishonest Majority
Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms