QH Podcast 11 :

Quantum Hermit spoke to Leonard Wossnig on Quantum Machine Learning. He is a PhD candidate in theoretical computer science at UCL with main supervisor Simone Severini (UCL, now AWS) and co-supervisor Aram Harrow (MIT), where I focus on classical and quantum algorithms for (randomised) numerical linear algebra, optimisation and machine learning.

His PhD has been funded by a Royal Society Fellowship with Simone Severini, and he received the 2019 Google PhD Fellowship in Quantum computing which will begin in the end of 2019. He has developed quantum algorithms for solving linear systems and Hamiltonian simulation as well as faster randomised classical algorithms for simulating quantum systems, i.e. to perform Hamiltonian simulation, based on recent sketching methods among other classical and quantum algorithms. Recently he has been developing within our team at UCL a variety of new algorithms at the intersection of classical and quantum neural networks.

He is also leading together with Ed Grant, Ian Horobin and Miriam Cha, a Startup called Rahko which develops quantum machine learning software with applications to quantum chemistry based on (machine learning) Heuristics.

In the past he had the pleasure to have worked at ETH Zurich (with Matthias Troyer), Oxford University (Simon Benjamin), NUS in Singapore and Microsoft (where he had the pleasure to have Nathan Wiebe as Mentor) and IBM Research (Ali Javadi and Kristan Temme as well as Shashanka Ubaru and Lior Horesh).

Independently he has been working on prior projects with Google Research on Circuit Optimisation and so called Circuit Learning.


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