Variational quantum amplitude estimation

• URL: http://arxiv.org/abs/2109.03687v2
• Date: Mon, 28 Feb 2022 18:44:57 GMT
• Title: Variational quantum amplitude estimation
• Authors: Kirill Plekhanov, Matthias Rosenkranz, Mattia Fiorentini, Michael Lubasch
• Abstract summary: In the context of Monte Carlo (MC) integration, we numerically show that shallow circuits can accurately approximate many amplitude amplification steps. We combine the variational approach with maximum likelihood amplitude estimation. To reduce the variational cost, we propose adaptive VQAE and numerically show in 6 to 12 qubit simulations that it can outperform classical MC sampling.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose to perform amplitude estimation with the help of constant-depth quantum circuits that variationally approximate states during amplitude amplification. In the context of Monte Carlo (MC) integration, we numerically show that shallow circuits can accurately approximate many amplitude amplification steps. We combine the variational approach with maximum likelihood amplitude estimation [Y. Suzuki et al., Quantum Inf. Process. 19, 75 (2020)] in variational quantum amplitude estimation (VQAE). VQAE typically has larger computational requirements than classical MC sampling. To reduce the variational cost, we propose adaptive VQAE and numerically show in 6 to 12 qubit simulations that it can outperform classical MC sampling.

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