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A semi-agnostic ansatz with variable structure for quantum machine learning
March 15, 2024, 4:43 a.m. | M. Bilkis, M. Cerezo, Guillaume Verdon, Patrick J. Coles, Lukasz Cincio
cs.LG updates on arXiv.org arxiv.org
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
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