Quantum Machine Learning

In past years, we have experienced the enormous expansion of Machine Learning, which now can be found in almost any technological application, from your mobile phone to the correct positioning of satellites. However, running those algorithms demands a huge computational cost, which was leveraged with the use of graphical processing units. Nevertheless, we are at a point in which only the biggest cluster can train state of the art networks.

Can quantum computing help solve this problem? The answer seems to be positive!

Quantum computing is a new paradigm in computing whose advent is currently happening. By using the fundamental concepts of Quantum Mechanics, physicists are developing these potentially powerful computers, namely Quantum Annealers and Circuits. Quantum computers can be used to run various approaches to Quantum Machine Learning (QML), in which the algorithms run in quantum processing units and are expected to give even exponential speed-ups.

In this session, we will review some of the last (and first) advances in QML, both for Quantum Annealers and Quantum Circuits. We will explain the methodologies behind them, how the actual computation is carried on and whether quantum advantage can be achieved or not. As a great finale, we will host a guest talk on the problem of optimizing variational algorithms.

Note: This meetup will require some familiarity with basic quantum computing concepts and Qiskit. But worry not! We have prepared a pre-event workshop to get you up to speed. Join here! https://www.meetup.com/meetup-group-quantumbarcelona/events/274126014/

The event will be held in English.

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Our speakers

Adrián
Adrián Pérez Salinas studied a Physics undergrad at UAM and a Master in Theoretical Physics at IFT, Madrid. He works currently at Barcelona Supercomputing Center developing Quantum Algorithms to be performed in near term devices and in future generations. This work is part of his PhD at UB, under the supervision of José Ignacio Latorre. His publications include quantum algorithms in several fields and a Open Source simulator for Quantum Circuits.

Gorka
Gorka Muñoz-Gil pursued his PhD in the Quantum Optics theory group at ICFO, under the supervision of Prof. M. Lewenstein. His research is devoted to the study of many body physics, both in quantum and classical systems. In these last years, he has focused on the connection between Machine Learning and Physics, in two directions: from one side, on the application of state-of-the-art Machine Learning to the study of complex physical systems; on the other side, on the development of new, more efficient training algorithms, based on Statistical Physics techniques.

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Event password: HJyCiwSK653

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Meeting picture from: Havlíček, V., Córcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., & Gambetta, J. M. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747), 209-212.