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 amplif...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2022-03-01
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| Series: | Quantum |
| Online Access: | https://quantum-journal.org/papers/q-2022-03-17-670/pdf/ |
| _version_ | 1818312932229906432 |
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| author | Kirill Plekhanov Matthias Rosenkranz Mattia Fiorentini Michael Lubasch |
| author_facet | Kirill Plekhanov Matthias Rosenkranz Mattia Fiorentini Michael Lubasch |
| author_sort | Kirill Plekhanov |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T08:25:42Z |
| format | Article |
| id | doaj.art-5d2ed3193c6a4aef83d4866cbdd29828 |
| institution | Directory Open Access Journal |
| issn | 2521-327X |
| language | English |
| last_indexed | 2024-12-13T08:25:42Z |
| publishDate | 2022-03-01 |
| publisher | Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
| record_format | Article |
| series | Quantum |
| spelling | doaj.art-5d2ed3193c6a4aef83d4866cbdd298282022-12-21T23:53:54ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2022-03-01667010.22331/q-2022-03-17-67010.22331/q-2022-03-17-670Variational quantum amplitude estimationKirill PlekhanovMatthias RosenkranzMattia FiorentiniMichael LubaschWe 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.https://quantum-journal.org/papers/q-2022-03-17-670/pdf/ |
| spellingShingle | Kirill Plekhanov Matthias Rosenkranz Mattia Fiorentini Michael Lubasch Variational quantum amplitude estimation Quantum |
| title | Variational quantum amplitude estimation |
| title_full | Variational quantum amplitude estimation |
| title_fullStr | Variational quantum amplitude estimation |
| title_full_unstemmed | Variational quantum amplitude estimation |
| title_short | Variational quantum amplitude estimation |
| title_sort | variational quantum amplitude estimation |
| url | https://quantum-journal.org/papers/q-2022-03-17-670/pdf/ |
| work_keys_str_mv | AT kirillplekhanov variationalquantumamplitudeestimation AT matthiasrosenkranz variationalquantumamplitudeestimation AT mattiafiorentini variationalquantumamplitudeestimation AT michaellubasch variationalquantumamplitudeestimation |