Learning the quantum algorithm for state overlap
Short-depth algorithms are crucial for reducing computational error on near-term quantum computers, for which decoherence and gate infidelity remain important issues. Here we present a machine-learning approach for discovering such algorithms. We apply our method to a ubiquitous primitive: computing...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2018-01-01
|
| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/aae94a |
| _version_ | 1797750534828982272 |
|---|---|
| author | Lukasz Cincio Yiğit Subaşı Andrew T Sornborger Patrick J Coles |
| author_facet | Lukasz Cincio Yiğit Subaşı Andrew T Sornborger Patrick J Coles |
| author_sort | Lukasz Cincio |
| collection | DOAJ |
| description | Short-depth algorithms are crucial for reducing computational error on near-term quantum computers, for which decoherence and gate infidelity remain important issues. Here we present a machine-learning approach for discovering such algorithms. We apply our method to a ubiquitous primitive: computing the overlap $\mathrm{Tr}(\rho \sigma )$ between two quantum states ρ and σ . The standard algorithm for this task, known as the Swap Test, is used in many applications such as quantum support vector machines, and, when specialized to ρ = σ , quantifies the Renyi entanglement. Here, we find algorithms that have shorter depths than the Swap Test, including one that has a constant depth (independent of problem size). Furthermore, we apply our approach to the hardware-specific connectivity and gate sets used by Rigetti’s and IBM’s quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error—compared to the Swap Test—on these computers. |
| first_indexed | 2024-03-12T16:35:08Z |
| format | Article |
| id | doaj.art-13a7edbef4314f59ad07c6ca80c24561 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:35:08Z |
| publishDate | 2018-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-13a7edbef4314f59ad07c6ca80c245612023-08-08T14:55:01ZengIOP PublishingNew Journal of Physics1367-26302018-01-01201111302210.1088/1367-2630/aae94aLearning the quantum algorithm for state overlapLukasz Cincio0Yiğit Subaşı1Andrew T Sornborger2Patrick J Coles3Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, United States of AmericaTheoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, United States of AmericaInformation Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, United States of AmericaTheoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, United States of AmericaShort-depth algorithms are crucial for reducing computational error on near-term quantum computers, for which decoherence and gate infidelity remain important issues. Here we present a machine-learning approach for discovering such algorithms. We apply our method to a ubiquitous primitive: computing the overlap $\mathrm{Tr}(\rho \sigma )$ between two quantum states ρ and σ . The standard algorithm for this task, known as the Swap Test, is used in many applications such as quantum support vector machines, and, when specialized to ρ = σ , quantifies the Renyi entanglement. Here, we find algorithms that have shorter depths than the Swap Test, including one that has a constant depth (independent of problem size). Furthermore, we apply our approach to the hardware-specific connectivity and gate sets used by Rigetti’s and IBM’s quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error—compared to the Swap Test—on these computers.https://doi.org/10.1088/1367-2630/aae94aquantum computingmachine-learningstate overlap |
| spellingShingle | Lukasz Cincio Yiğit Subaşı Andrew T Sornborger Patrick J Coles Learning the quantum algorithm for state overlap New Journal of Physics quantum computing machine-learning state overlap |
| title | Learning the quantum algorithm for state overlap |
| title_full | Learning the quantum algorithm for state overlap |
| title_fullStr | Learning the quantum algorithm for state overlap |
| title_full_unstemmed | Learning the quantum algorithm for state overlap |
| title_short | Learning the quantum algorithm for state overlap |
| title_sort | learning the quantum algorithm for state overlap |
| topic | quantum computing machine-learning state overlap |
| url | https://doi.org/10.1088/1367-2630/aae94a |
| work_keys_str_mv | AT lukaszcincio learningthequantumalgorithmforstateoverlap AT yigitsubası learningthequantumalgorithmforstateoverlap AT andrewtsornborger learningthequantumalgorithmforstateoverlap AT patrickjcoles learningthequantumalgorithmforstateoverlap |