Quantum-enhanced greedy combinatorial optimization solver

Author:

Dupont Maxime1ORCID,Evert Bram1,Hodson Mark J.1,Sundar Bhuvanesh1ORCID,Jeffrey Stephen1,Yamaguchi Yuki1,Feng Dennis1,Maciejewski Filip B.23ORCID,Hadfield Stuart23ORCID,Alam M. Sohaib23ORCID,Wang Zhihui23,Grabbe Shon2,Lott P. Aaron23,Rieffel Eleanor G.2,Venturelli Davide23ORCID,Reagor Matthew J.1ORCID

Affiliation:

1. Rigetti Computing, Berkeley, CA 94710, USA.

2. QuAIL, NASA Ames Research Center, Moffett Field, CA 94035, USA.

3. USRA Research Institute for Advanced Computer Science, Mountain View, CA 94035, USA.

Abstract

Combinatorial optimization is a broadly attractive area for potential quantum advantage, but no quantum algorithm has yet made the leap. Noise in quantum hardware remains a challenge, and more sophisticated quantum-classical algorithms are required to bolster their performance. Here, we introduce an iterative quantum heuristic optimization algorithm to solve combinatorial optimization problems. The quantum algorithm reduces to a classical greedy algorithm in the presence of strong noise. We implement the quantum algorithm on a programmable superconducting quantum system using up to 72 qubits for solving paradigmatic Sherrington-Kirkpatrick Ising spin glass problems. We find the quantum algorithm systematically outperforms its classical greedy counterpart, signaling a quantum enhancement. Moreover, we observe an absolute performance comparable with a state-of-the-art semidefinite programming method. Classical simulations of the algorithm illustrate that a key challenge to reaching quantum advantage remains improving the quantum device characteristics.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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