Overview

I am a technology enthusiast, and my particular areas of research include quantum computation, computational chemistry, numerical linear algebra, and machine learning. More recently I have applied these methods to drug discovery through Rahko, but at heart I am a more theoretical scientist.

My PhD is on quantum machine learning, which is the unique intersection of machine learning with quantum physics in the broader sense. In my PhD I focused on classical and quantum numerical linear algebra, machine learning methods, and on statistical learning theory. If you want to know more about quantum machine learning I can recommend our Royal Society Proc. A review on it.

In the past I have 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 as well as classical, and quantum algorithms. All of my research articles can be found here, and selected publications are below. For a general introduction or more information about quantum computation and the near term applications, I refer anyone to this brilliant article by John Preskill, or similarly the one by Aram Harrow and Ashley Montanaro.

Our team at Rahko has been developing a variety of new algorithms at the intersection of classical and quantum neural networks and we are currently working on a library which enables a non-expert to smoothly run experiments via the Rahko platform Hyrax (similar to Keras & Tensorflow).

Recently, much of my work focuses on applying machine learning to quantum systems, which is one of the core areas of Rahko.

Papers

Below you can find a list of selected publications:

• • • • Ciliberto, C., Herbster, M., Ialongo, A. D., Pontil, M., Rocchetto, A., Severini, S., & Wossnig, L. (2018). Quantum machine learning: a classical perspective. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 474(2209), 20170551.

      • Wossnig, L., Zhao, Z., & Prakash, A. (2018). Quantum linear system algorithm for dense matrices. Physical review letters, 120(5), 050502.

      • Rebentrost, P., Schuld, M., Wossnig, L., Petruccione, F., & Lloyd, S. (2019). Quantum gradient descent and Newton’s method for constrained polynomial optimization. New Journal of Physics, 21(7), 073023.

      • Benedetti, M., Grant, E., Wossnig, L., & Severini, S. (2019). Adversarial quantum circuit learning for pure state approximation. New Journal of Physics, 21(4), 043023.

      • Wiebe, N., & Wossnig, L. (2019). Generative training of quantum Boltzmann machines with hidden units. arXiv preprint arXiv:1905.09902.

      • Grant, E., Wossnig, L., Ostaszewski, M., & Benedetti, M. (2019). An initialization strategy for addressing barren plateaus in parametrized quantum circuits. Quantum, 3, 214. Chicago

      • Rudi, A., Wossnig, L., Ciliberto, C., Rocchetto, A., Pontil, M., & Severini, S. (2020). Approximating Hamiltonian dynamics with the Nyström method. Quantum, 4, 234.

      • Ciliberto, C., Rocchetto, A., Rudi, A., & Wossnig, L. (2020). Fast quantum learning with statistical guarantees. arXiv preprint arXiv:2001.10477.

Talks, Teaching, And Collaborations

In the past I had the pleasure to work at ETH Zurich with Matthias Troyer, Oxford University with Simon Benjamin, and NUS in Singapore with Joe Fitzsimons, and, due to fortunate circumstances, Patrick Rebentrost. Additionally, I was working as a Research Intern at Microsoft Research with Nathan Wiebe and Guang Hao Low, as well as IBM Research with Ali Javadi and Kristan Temme.

Independently I have been working on projects with Google Research (Masoud Mohseni) and my colleague Hongxiang Chen on circuit optimisation and so called circuit learning; I have also enjoyed considerable academic and friendly exchanges with Maria Schuld.

I am/have been a reviewer for the New Journal of Physics, Science Advances, PRL, SODA, the Royal Society, Quantum Journal, Quantum Information Processing, and Nature Science among other journals.

I have further given (invited) talks on the following selected occasions:

[UPDATE: Due to time constraints I have not been able to give academic talks since 2020]

• • • Quantum algorithms in a nutshell and beyond - an overview talk about the current state of the art in quantum algorithms. April 24, 2019, National Physical Laboratories, Teddington, UK

    • 'Quantum Machine Learning', Invited talk at the APS March Meeting 2019, March 4-8, 2019 in Boston, Massachusetts, U.S.A.

    • Randomized NLA and quantum algorithms - Algebraic Graph Theory and Quantum Walks, April 23 - 27, 2018 at the University of Waterloo

    • Approximating Hamiltonian dynamics with the Nystrom method - University of Maryland, 2018

    • Sketching as a tool for faster quantum simulation - IRIF, Paris Diderot, June 2018

    • Recap of recent developments in quantum algorithms - Nano Innovation, CEA Paris, 2018

    • Quantum linear system algorithms - CS Quantum (before joining) UCL 2017

    • Introduction to Deep Learning with Edward Grant - MedTech Soc. at UCL(2017/18)

Besides this I have been a TA for Deep Learning, Statistical Learning Theory, Mathematical methods in machine learning and other classes which were or are taught at UCL.