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Updated: May 12, 2025

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Scaling Advantage in Approximate Optimization with Quantum Annealing.

Humberto Munoz-Bauza1,2, Daniel Lidar3,4,5,6

  • 1NASA Ames Research Center, Quantum Artificial Intelligence Lab. (QuAIL), Moffett Field, California 94035, USA.

Physical Review Letters
|May 9, 2025
PubMed
Summary
This summary is machine-generated.

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Quantum annealing shows a scaling advantage in approximate optimization, outperforming classical algorithms. This quantum annealing correction (QAC) method achieves a speedup for finding low-energy states in complex problems.

Area of Science:

  • Quantum Computing
  • Computational Physics
  • Optimization Algorithms

Background:

  • Quantum annealers are advancing for complex optimization and simulation.
  • A quantum advantage in exact optimization remains elusive.
  • Approximate optimization is a key area for demonstrating quantum benefits.

Purpose of the Study:

  • To demonstrate a quantum annealing scaling advantage in approximate optimization.
  • To compare quantum annealing performance against a leading classical algorithm (PT-ICM).
  • To introduce and validate quantum annealing correction (QAC) for error suppression.

Main Methods:

  • Implemented quantum annealing correction (QAC) using an error-correcting code.
  • Utilized the D-Wave Advantage quantum annealer with over 1,300 logical qubits.

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Last Updated: May 12, 2025

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Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
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  • Benchmarked performance on 2D spin-glass problems using time-to-epsilon metric.
  • Compared against parallel tempering with isoenergetic cluster moves (PT-ICM).

    • Main Results:

    • Quantum annealing with QAC demonstrated a scaling advantage over PT-ICM.
    • Achieved a speedup in sampling low-energy states with an optimality gap of at least 1.0%.
    • Showcased the effectiveness of QAC in suppressing errors for quantum annealing.

    Conclusions:

    • This study presents the first algorithmic quantum speedup in approximate optimization.
    • Quantum annealing, enhanced by QAC, offers a viable path for tackling complex optimization challenges.
    • Error suppression techniques are crucial for realizing quantum advantage in annealing hardware.