Low-depth amplitude estimation on a trapped-ion quantum computer
Amplitude estimation (AE) is a fundamental quantum algorithmic primitive that enables quantum computers to achieve quadratic speedups for a large class of statistical estimation problems, including Monte Carlo methods. Recent works have succeeded in somewhat reducing the necessary resources for AE b...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2022-07-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.4.033034 |
| _version_ | 1797210715267792896 |
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| author | Tudor Giurgica-Tiron Sonika Johri Iordanis Kerenidis Jason Nguyen Neal Pisenti Anupam Prakash Ksenia Sosnova Ken Wright William Zeng |
| author_facet | Tudor Giurgica-Tiron Sonika Johri Iordanis Kerenidis Jason Nguyen Neal Pisenti Anupam Prakash Ksenia Sosnova Ken Wright William Zeng |
| author_sort | Tudor Giurgica-Tiron |
| collection | DOAJ |
| description | Amplitude estimation (AE) is a fundamental quantum algorithmic primitive that enables quantum computers to achieve quadratic speedups for a large class of statistical estimation problems, including Monte Carlo methods. Recent works have succeeded in somewhat reducing the necessary resources for AE by trading off some of the speedup for lower depth circuits, but high quality qubits are still needed for demonstrating such algorithms. Here, we report the results of an experimental demonstration of AE on a state-of-the-art trapped ion quantum computer. AE was used to estimate the inner product of randomly chosen four-dimensional unit vectors, and the low-depth AE algorithms were based on the maximum likelihood estimation (MLE) and the Chinese remainder theorem (CRT) techniques. Significant improvements in accuracy were observed for the MLE based approach when deeper quantum circuits were taken into account, including circuits with more than 90 two-qubit gates and depth 60, achieving a mean additive estimation error on the order of 10^{−2}. The CRT based approach was found to provided accurate estimates for many of the data points but was less robust against noise on average. Last, we analyze two more AE algorithms that take into account the specifics of the hardware noise to further improve the results. |
| first_indexed | 2024-04-24T10:15:00Z |
| format | Article |
| id | doaj.art-3d09f6a2ae524100ad60ffd2e0c0f820 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:15:00Z |
| publishDate | 2022-07-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-3d09f6a2ae524100ad60ffd2e0c0f8202024-04-12T17:22:45ZengAmerican Physical SocietyPhysical Review Research2643-15642022-07-014303303410.1103/PhysRevResearch.4.033034Low-depth amplitude estimation on a trapped-ion quantum computerTudor Giurgica-TironSonika JohriIordanis KerenidisJason NguyenNeal PisentiAnupam PrakashKsenia SosnovaKen WrightWilliam ZengAmplitude estimation (AE) is a fundamental quantum algorithmic primitive that enables quantum computers to achieve quadratic speedups for a large class of statistical estimation problems, including Monte Carlo methods. Recent works have succeeded in somewhat reducing the necessary resources for AE by trading off some of the speedup for lower depth circuits, but high quality qubits are still needed for demonstrating such algorithms. Here, we report the results of an experimental demonstration of AE on a state-of-the-art trapped ion quantum computer. AE was used to estimate the inner product of randomly chosen four-dimensional unit vectors, and the low-depth AE algorithms were based on the maximum likelihood estimation (MLE) and the Chinese remainder theorem (CRT) techniques. Significant improvements in accuracy were observed for the MLE based approach when deeper quantum circuits were taken into account, including circuits with more than 90 two-qubit gates and depth 60, achieving a mean additive estimation error on the order of 10^{−2}. The CRT based approach was found to provided accurate estimates for many of the data points but was less robust against noise on average. Last, we analyze two more AE algorithms that take into account the specifics of the hardware noise to further improve the results.http://doi.org/10.1103/PhysRevResearch.4.033034 |
| spellingShingle | Tudor Giurgica-Tiron Sonika Johri Iordanis Kerenidis Jason Nguyen Neal Pisenti Anupam Prakash Ksenia Sosnova Ken Wright William Zeng Low-depth amplitude estimation on a trapped-ion quantum computer Physical Review Research |
| title | Low-depth amplitude estimation on a trapped-ion quantum computer |
| title_full | Low-depth amplitude estimation on a trapped-ion quantum computer |
| title_fullStr | Low-depth amplitude estimation on a trapped-ion quantum computer |
| title_full_unstemmed | Low-depth amplitude estimation on a trapped-ion quantum computer |
| title_short | Low-depth amplitude estimation on a trapped-ion quantum computer |
| title_sort | low depth amplitude estimation on a trapped ion quantum computer |
| url | http://doi.org/10.1103/PhysRevResearch.4.033034 |
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