Assessment of TensorFlow Quantum

Abstract

With the advent of quantum machine learning [1], the need for specialized software that can transfer theoretical considerations into practical solutions arose. Although the existing machine learning libraries excel at applications that require classical components, they alone cannot interact with quantum components. For this purpose, TensorFlow Quantum [2] – a library that enables designing hybrid quantum-classical models – was created. In this presentation, we will provide an assessment of this library’s capabilities in terms of integrating classical and quantum ecosystems [3]. Additionally, we will evaluate the usefulness of TensorFlow Quantum for solving combinatorial optimizations problems, specifically the Traveling Salesman Problem, with the Quantum Approximate Optimization Algorithm [4].

References

[1] M. Schuld, I. Sinayskiy, and F. Petruccione. An introduction to quantum machine learning. Contemporary Physics, 2014.

[2] M. Broughton, G. Verdon, T. McCourt, et al. TensorFlow Quantum: A Software Framework for Quantum Machine Learning. arXiv preprint, 2020.

[3] J. Zawalska. Assessment of TensorFlow Quantum. Master’s thesis supervised by Katarzyna Rycerz, PhD, Institute of Computer Sciece, AGH University of Science and Technology, Krakow, September 2021.

[4] E. Farhi, J. Goldstone, and S. Gutmann. A quantum approximate optimization algorithm. arXiv preprint, 2014.

Bio: Justyna Zawalska works at ACC Cyfronet AGH at the Quantum Computing Laboratory. Recently, she has been graduated with a master’s degree in Computer Science at AGH.