Quantum approximate optimization of non-planar graph problems on a planar superconducting processor
Faster algorithms for combinatorial optimization could prove transformative for diverse areas such as logistics, finance and machine learning. Accordingly, the possibility of quantum enhanced optimization has driven much interest in quantum technologies. Here we demonstrate the application of the Go...
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| Published in: | Nature physics Vol. 17; no. 3; pp. 332 - 336 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group
01.03.2021
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| Subjects: | |
| ISSN: | 1745-2473, 1745-2481 |
| Online Access: | Get full text |
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| Abstract | Faster algorithms for combinatorial optimization could prove transformative for diverse areas such as logistics, finance and machine learning. Accordingly, the possibility of quantum enhanced optimization has driven much interest in quantum technologies. Here we demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA). Like past QAOA experiments, we study performance for problems defined on the planar connectivity graph native to our hardware; however, we also apply the QAOA to the Sherrington–Kirkpatrick model and MaxCut, non-native problems that require extensive compilation to implement. For hardware-native problems, which are classically efficient to solve on average, we obtain an approximation ratio that is independent of problem size and observe that performance increases with circuit depth. For problems requiring compilation, performance decreases with problem size. Circuits involving several thousand gates still present an advantage over random guessing but not over some efficient classical algorithms. Our results suggest that it will be challenging to scale near-term implementations of the QAOA for problems on non-native graphs. As these graphs are closer to real-world instances, we suggest more emphasis should be placed on such problems when using the QAOA to benchmark quantum processors.It is hoped that quantum computers may be faster than classical ones at solving optimization problems. Here the authors implement a quantum optimization algorithm over 23 qubits but find more limited performance when an optimization problem structure does not match the underlying hardware. |
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| AbstractList | Faster algorithms for combinatorial optimization could prove transformative for diverse areas such as logistics, finance and machine learning. Accordingly, the possibility of quantum enhanced optimization has driven much interest in quantum technologies. Here we demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA). Like past QAOA experiments, we study performance for problems defined on the planar connectivity graph native to our hardware; however, we also apply the QAOA to the Sherrington–Kirkpatrick model and MaxCut, non-native problems that require extensive compilation to implement. For hardware-native problems, which are classically efficient to solve on average, we obtain an approximation ratio that is independent of problem size and observe that performance increases with circuit depth. For problems requiring compilation, performance decreases with problem size. Circuits involving several thousand gates still present an advantage over random guessing but not over some efficient classical algorithms. Our results suggest that it will be challenging to scale near-term implementations of the QAOA for problems on non-native graphs. As these graphs are closer to real-world instances, we suggest more emphasis should be placed on such problems when using the QAOA to benchmark quantum processors.It is hoped that quantum computers may be faster than classical ones at solving optimization problems. Here the authors implement a quantum optimization algorithm over 23 qubits but find more limited performance when an optimization problem structure does not match the underlying hardware. |
| Author | Kostritsa Fedor Ryan, Babbush Quintana, Chris Gidney, Craig O’Brien Thomas E Yeh Ping Jamie, Yao Z Buell, David A Ho, Alan Brooks, Foxen Mohseni Masoud Eppens, Daniel Graff, Rob Putterman Harald Collins, Roberto Lindmark, Mike Niu Murphy Yuezhen Sung, Kevin J Martinis, John M McEwen, Matt Xiao, Mi Zalcman, Adam Kelly, Julian Sank, Daniel White, Theodore Dunsworth, Andrew Hong, Sabrina Harrigan, Matthew P Neven Hartmut Zhou, Leo Ostby, Eric Farhi, Edward Arya Kunal Kafri Dvir O’Gorman Bryan Landhuis, David Leib, Martin Smelyanskiy Vadim Laptev Pavel Broughton, Michael Streif, Michael Fowler, Austin Barends Rami Habegger, Steve Huang, Trent Boixo Sergio Giustina Marissa Bacon, Dave McClean, Jarrod R Martin, Orion Arute Frank Atalaya Juan Burkett, Brian Mutus Josh Bardin, Joseph C Lucero, Erik Kim, Seon Strain, Doug Chiaro, Ben Ioffe, L B Isakov, Sergei V Korotkov, Alexander N Mruczkiewicz Wojciech Roushan Pedram Courtney, William Petukhov, Andre Rubin, Nicholas C Bushnell, Nicholas Kechedzhi Kostyantyn Chen, Yu Neukart Florian Naaman Ofer Demura, Sean Klimov, |
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| Copyright | The Author(s), under exclusive licence to Springer Nature Limited 2021. |
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| DOI | 10.1038/s41567-020-01105-y |
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| Snippet | Faster algorithms for combinatorial optimization could prove transformative for diverse areas such as logistics, finance and machine learning. Accordingly, the... |
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| SubjectTerms | Algorithms Combinatorial analysis Gates (circuits) Graph theory Graphs Hardware Logistics Machine learning Microprocessors Optimization Optimization algorithms Quantum computers Qubits (quantum computing) Superconductivity |
| Title | Quantum approximate optimization of non-planar graph problems on a planar superconducting processor |
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