A semi-agnostic ansatz with variable structure for quantum machine learning

arXiv:2103.06712v4 Announce Type: replace-cross
Abstract: Quantum machine learning -- and specifically Variational Quantum Algorithms (VQAs) -- offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware …

abstract algorithms applications arxiv chemistry computers cs.lg data data science form machine machine learning materials materials science mathematics near paradigm programming quant-ph quantum quantum computers science stat.ml trains type