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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Vydané v:Nature physics Ročník 17; číslo 3; s. 332 - 336
Hlavní autori: Harrigan, Matthew P, Sung, Kevin J, Neeley, Matthew, Satzinger, Kevin J, Arute Frank, Arya Kunal, Atalaya Juan, Bardin, Joseph C, Barends Rami, Boixo Sergio, Broughton, Michael, Buckley, Bob B, Buell, David A, Burkett, Brian, Bushnell, Nicholas, Chen, Yu, Chen, Zijun, Chiaro, Ben, Collins, Roberto, Courtney, William, Demura, Sean, Dunsworth, Andrew, Eppens, Daniel, Fowler, Austin, Brooks, Foxen, Gidney, Craig, Giustina Marissa, Graff, Rob, Habegger, Steve, Ho, Alan, Hong, Sabrina, Huang, Trent, Ioffe, L B, Isakov, Sergei V, Evan, Jeffrey, Zhang, Jiang, Jones, Cody, Kafri Dvir, Kechedzhi Kostyantyn, Kelly, Julian, Kim, Seon, Klimov, Paul V, Korotkov, Alexander N, Kostritsa Fedor, Landhuis, David, Laptev Pavel, Lindmark, Mike, Leib, Martin, Martin, Orion, Martinis, John M, McClean, Jarrod R, McEwen, Matt, Megrant Anthony, Xiao, Mi, Mohseni Masoud, Mruczkiewicz Wojciech, Mutus Josh, Naaman Ofer, Neill, Charles, Neukart Florian, Niu Murphy Yuezhen, O’Brien Thomas E, O’Gorman Bryan, Ostby, Eric, Petukhov, Andre, Putterman Harald, Quintana, Chris, Roushan Pedram, Rubin, Nicholas C, Sank, Daniel, Skolik Andrea, Smelyanskiy Vadim, Strain, Doug, Streif, Michael, Szalay, Marco, Vainsencher Amit, White, Theodore, Jamie, Yao Z, Yeh Ping, Zalcman, Adam, Zhou, Leo, Neven Hartmut, Bacon, Dave, Lucero, Erik, Farhi, Edward, Ryan, Babbush
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group 01.03.2021
Predmet:
Algorithms
Combinatorial analysis
Gates (circuits)
Graph theory
Graphs
Hardware
Logistics
Machine learning
Microprocessors
Optimization
Optimization algorithms
Quantum computers
Qubits (quantum computing)
Superconductivity
ISSN:1745-2473, 1745-2481
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:1745-2473
1745-2481
DOI:10.1038/s41567-020-01105-y