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...
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| Published in | New journal of physics Vol. 20; no. 11; pp. 113022 - 113035 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bristol
IOP Publishing
14.11.2018
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| Subjects | |
| Online Access | Get full text |
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| Abstract | 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 Tr ( ) between two quantum states and . 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 = , 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. |
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| AbstractList | 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 Tr ( ) between two quantum states and . 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 = , 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. 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. 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. |
| Author | Suba, Yi it Coles, Patrick J Cincio, Lukasz Sornborger, Andrew T |
| Author_xml | – sequence: 1 givenname: Lukasz surname: Cincio fullname: Cincio, Lukasz organization: Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, United States of America – sequence: 2 givenname: Yi it surname: Suba fullname: Suba, Yi it organization: Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, United States of America – sequence: 3 givenname: Andrew T surname: Sornborger fullname: Sornborger, Andrew T email: lcincio@lanl.gov organization: Information Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, United States of America – sequence: 4 givenname: Patrick J surname: Coles fullname: Coles, Patrick J organization: Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, United States of America |
| BackLink | https://www.osti.gov/biblio/1482266$$D View this record in Osti.gov |
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| Cites_doi | 10.1016/j.jmr.2004.11.004 10.1103/PhysRevA.98.032309 10.1088/1367-2630/aa5e47 10.1103/PhysRevLett.87.167902 10.1109/ISCAS.2018.8351885 10.1103/PhysRevLett.113.130503 10.1103/PhysRevApplied.6.054005 10.26421/QIC8.5-6-8 10.1126/sciadv.1501531 10.1109/CLEOE-IQEC.2013.6801703 10.1103/PhysRevA.86.032324 10.1088/2058-9565/aaa331 10.1007/s10710-009-9080-7 10.1038/nature23459 10.1016/j.physleta.2017.08.043 10.26421/QIC14.3-4-7 10.1088/2058-9565/aaa5cc 10.1103/PhysRevA.53.2855 10.1103/PhysRevLett.116.230504 10.1103/PhysRevA.87.052330 10.1023/B:AIRE.0000006605.86111.79 10.1103/PhysRevB.96.195136 10.1126/science.aao4309 10.1103/PhysRevA.87.032341 |
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| Copyright | 2018 The Author(s). Published by IOP Publishing Ltd on behalf of Deutsche Physikalische Gesellschaft 2018. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| StartPage | 113022 |
| SubjectTerms | Algorithms Computer Science Error reduction Information Science Machine learning Mathematics MATHEMATICS AND COMPUTING Physics Quantum computers Quantum computing quantum computing algorithms Quantum entanglement state overlap Support vector machines |
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| Title | Learning the quantum algorithm for state overlap |
| URI | https://iopscience.iop.org/article/10.1088/1367-2630/aae94a https://www.proquest.com/docview/2312528618 https://www.osti.gov/biblio/1482266 https://doaj.org/article/13a7edbef4314f59ad07c6ca80c24561 |
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