Abstract
Data encoding methods and applications in variational quantum algorithms.
Location
Q-Hack 2021 (Xanadu Quantum Computing), Virtual
Based on the references:
- “Tequila: A platform for rapid development of quantum algorithms”,
J. S. Kottmann, S. Alperin-Lea, T. Tamayo-Mendoza, A. Cervera-Lierta, C. Lavigne, T.-C. Yen, V. Verteletskyi, P. Schleich, A. Anand, M. Degroote, S. Chaney, M. Kesibi, N. Grace Curnow, B. Solo, G. Tsilimigkounakis, C. Zendejas-Morales, A. F. Izmaylov, A. Aspuru-Guzik,
Quantum Science and Technology,
arXiv:2011.03057 [quant-ph].
Tequila repository.
- “Noisy intermediate-scale quantum (NISQ) algorithms”,
K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke, W.-K. Mok, S. Sim, L.-C. Kwek, A. Aspuru-Guzik,
arXiv:2101.08448 [quant-ph] (2021).
- “Data re-uploading for a universal quantum classifier”,
A. Pérez-Salinas, A. Cervera-Lierta, E. Gil-Fuster, J. I. Latorre,
Quantum 4, 226 (2020).
- “Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation”,
Alba Cervera-Lierta, Jakob S. Kottmann, Alán Aspuru-Guzik
PRX Quantum 2, 020329 (2021),
arXiv:2009.13545 [quant-ph]
- “One qubit as a Universal Approximant”,
A. Pérez-Salinas, D. López-Núñez, A. García-Sáez, P. Forn-Díaz, J. I. Latorre,
arXiv:2102.04032 [quant-ph] (2021).
- “The effect of data encoding on the expressive power of variational quantum machine learning models”,
M. Schuld, R. Sweke, J. J. Meyer,
arXiv:2008.08605 [quant-ph] (2020).
Senior Researcher
Quantum Computing scientist.