Warsaw Quantum Computing Group

Episode XXXIII of the Warsaw Quantum Computing Group meetings!

20.12.2021, 18:00 CET

Justyna Zawalska

“Hybrid quantum-classical machine learning with TensorFlow Quantum”

Recording: https://www.youtube.com/watch?v=NfSSqL4Sx0c

Slides: https://drive.google.com/file/d/1yvrNl191N9Gi1i8pn7xfQC9t8_AUNKvl/view?usp=sharing

Github: https://github.com/jzawalska/tensorflow-quantum

Abstract from Justyna: In this talk, I will provide an assessment of TensorFlow Quantum – a library for designing hybrid quantum-classical machine learning models. Also, I will present two experiments showing how to use this library to solve the Traveling Salesman Problem (TSP) with the Quantum Approximate Optimization Algorithm. The first experiment will examine the possibility of minimizing the expectation value for a single TSP instance. The second experiment will investigate the generalization abilities of a hybrid quantum-classical neural network trained with many instances of the TSP. This presentation will be based on:

J. Zawalska. Assessment of TensorFlow Quantum. Master’s thesis supervised by Katarzyna Rycerz, Ph.D. Institute of Computer Science, AGH University of Science and Technology, Krakow, 2021.

URL: http://dice.cyfronet.pl/publications/source/MSc_theses/Justyna_Zawalska_msc.pdf

BIO:

Justyna Zawalska works at the Quantum Computing Laboratory at Academic Computer Center Cyfronet AGH. In 2021 she graduated from the AGH University of Science and Technology in Krakow with a master’s degree in computer science. Her research interests include quantum computing and solving combinatorial optimization problems.

This meeting is organized by the Quantum AI Foundation.

Strategic Partners: Snarto, Cogit

Honorary Patrons: ICM, Students’ Association for Computer Science, Machine Learning Society at MIM UW, QPoland, ML in PL Association, OM PTI, WDI, AleQCG, Quantumz.io, Digital Poland Foundation, Candela, Sano