This is a repository with some code examples to get a gentle introduction to classical data encoding in quantum circuits and how to use it to construct a variational quantum classifier, a function fitter and to learn energy profiles of Hamiltonians. It is the complementary material from my Q-Hack 2021 presentation (you can find the slides here and the recording here).
You will need to install Tequila[1] to run the notebook and play with them.
If you want to learn about Variational Quantum Algorithms and Noisy Intermediate-Scale Quantum (NISQ) computing, you can check the review [2].
- Single-qubit classifier [3]
- Meta-Variational Quantum Eigensolver for spin chains [4]
- Meta-VQE for chemistry [4]
- Quantum function fitter [5,6]
The Meta-VQE notebook come from this repository.
[1] 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].
[2] 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).
[3] Data re-uploading for a universal quantum classifier,
A. Pérez-Salinas, A. Cervera-Lierta, E. Gil-Fuster, J. I. Latorre,
Quantum 4, 226 (2020).
[4] The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation,
A. Cervera-Lierta, J. S. Kottmann, A. Aspuru-Guzik,
arXiv:2009.13545 [quant-ph] (2020).
[5] 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).
[6] 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).