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Error-corrected quantum annealing with hundreds of qubits

Author

Listed:
  • Kristen L. Pudenz

    (University of Southern California
    Center for Quantum Information Science & Technology, University of Southern California
    Information Sciences Institute, University of Southern California)

  • Tameem Albash

    (Center for Quantum Information Science & Technology, University of Southern California
    University of Southern California)

  • Daniel A. Lidar

    (University of Southern California
    Center for Quantum Information Science & Technology, University of Southern California
    University of Southern California
    University of Southern California)

Abstract

Quantum information processing offers dramatic speedups, yet is susceptible to decoherence, whereby quantum superpositions decay into mutually exclusive classical alternatives, thus robbing quantum computers of their power. This makes the development of quantum error correction an essential aspect of quantum computing. So far, little is known about protection against decoherence for quantum annealing, a computational paradigm aiming to exploit ground-state quantum dynamics to solve optimization problems more rapidly than is possible classically. Here we develop error correction for quantum annealing and experimentally demonstrate it using antiferromagnetic chains with up to 344 superconducting flux qubits in processors that have recently been shown to physically implement programmable quantum annealing. We demonstrate a substantial improvement over the performance of the processors in the absence of error correction. These results pave the way towards large-scale noise-protected adiabatic quantum optimization devices, although a threshold theorem such as has been established in the circuit model of quantum computing remains elusive.

Suggested Citation

  • Kristen L. Pudenz & Tameem Albash & Daniel A. Lidar, 2014. "Error-corrected quantum annealing with hundreds of qubits," Nature Communications, Nature, vol. 5(1), pages 1-10, May.
  • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms4243
    DOI: 10.1038/ncomms4243
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    Cited by:

    1. Gili Rosenberg & Mohammad Vazifeh & Brad Woods & Eldad Haber, 2016. "Building an iterative heuristic solver for a quantum annealer," Computational Optimization and Applications, Springer, vol. 65(3), pages 845-869, December.
    2. Ajagekar, Akshay & You, Fengqi, 2022. "Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).

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