Variational Quantum Amplitude Estimation


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 can exhibit a cubic quantum speedup over classical MC sampling if the variational cost is ignored. If this cost is taken into account, VQAE typically has larger computational requirements than classical MC sampling. To reduce the variational cost, we propose adaptive VQAE and numerically show that it can outperform classical MC sampling.

Kirill Plekhanov, Matthias Rosenkranz, Mattia Fiorentini, Michael Lubasch
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