Variational quantum algorithms, M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, H. Tang, J. van Rooij, and Liang Jiang, 2021Nature Reviews Physics, Vol. 3DOI: 10.1038/s42254-021-00348-9 - A comprehensive review of Variational Quantum Algorithms, discussing their components, applications, and challenges, including classical optimization.
Adam: A Method for Stochastic Optimization, Diederik P. Kingma, Jimmy Ba, 20143rd International Conference for Learning RepresentationsDOI: 10.48550/arXiv.1412.6980 - Introduces the Adam optimizer, a widely used adaptive learning rate optimization algorithm for training deep neural networks.
Numerical Optimization, Jorge Nocedal and Stephen J. Wright, 2006 (Springer)DOI: 10.1007/978-0-387-40065-5 - A standard textbook on optimization, providing in-depth coverage of methods like BFGS and other quasi-Newton algorithms (2nd edition).
Benchmarking variational quantum algorithms for chemistry, Kishor Bharti, Anna Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Andreas Mekemech, Jacob Shabani, and Alán Aspuru-Guzik, 2020npj Quantum Information, Vol. 6 (Nature Publishing Group (part of Springer Nature))DOI: 10.1038/s41534-020-00287-7 - This paper provides a practical comparison and analysis of various classical optimizers in the context of VQAs for quantum chemistry problems.