4.8 Article

Quantum optimization of maximum independent set using Rydberg atom arrays

Journal

SCIENCE
Volume 376, Issue 6598, Pages 1209-+

Publisher

AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.abo6587

Keywords

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Funding

  1. DARPA ONISQ program [W911NF2010021]
  2. Center for Ultracold Atoms
  3. National Science Foundation
  4. Vannevar Bush Faculty Fellowship
  5. US Department of Energy [DE-SC0021013]
  6. DOE Quantum Systems Accelerator Center [7568717]
  7. Army Research Office MURI
  8. Amazon Web Services
  9. DOE CSG award fellowship [DE-SC0020347]
  10. National Defense Science and Engineering Graduate (NDSEG) fellowship
  11. NSF Graduate Research Fellowship Program [DGE1745303]
  12. Fannie and John Hertz Foundation
  13. Max Planck/Harvard Research Center for Quantum Optics
  14. U.S. Department of Energy [DE-SC0019030]
  15. DARPA [W911NF2010021]
  16. Simons investigator fellowships
  17. NSF [CCF 1565264, DMS-2134157]
  18. DOE [DE-SC0022199]
  19. Army Research Office [W911NF-21-10367]
  20. FAS Division of Science Research Computing Group at Harvard University
  21. QuEra Computing
  22. U.S. Department of Energy (DOE) [DE-SC0022199, DE-SC0020347] Funding Source: U.S. Department of Energy (DOE)
  23. U.S. Department of Defense (DOD) [W911NF2010021] Funding Source: U.S. Department of Defense (DOD)

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By using Rydberg atom arrays, we experimentally investigate quantum algorithms for solving the maximum independent set problem and observe quantum speedup on the hardest graphs.
Realizing quantum speedup for practically relevant, computationally hard problems is a central challenge in quantum information science. Using Rydberg atom arrays with up to 289 qubits in two spatial dimensions, we experimentally investigate quantum algorithms for solving the maximum independent set problem. We use a hardware-efficient encoding associated with Rydberg blockade, realize closed-loop optimization to test several variational algorithms, and subsequently apply them to systematically explore a class of graphs with programmable connectivity. We find that the problem hardness is controlled by the solution degeneracy and number of local minima, and we experimentally benchmark the quantum algorithm's performance against classical simulated annealing. On the hardest graphs, we observe a superlinear quantum speedup in finding exact solutions in the deep circuit regime and analyze its origins.

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