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Area of Science:

  • Quantum computing
  • Computational physics
  • Optimization algorithms

Background:

  • Physical quantum annealers operate at unavoidable finite temperatures.
  • Fixed finite temperature limits the scalability and competitiveness of quantum annealers as optimizers.

Purpose of the Study:

  • To identify a fundamental limitation in fixed finite temperature quantum annealers.
  • To establish a temperature scaling law for quantum annealers to function as scalable optimizers.

Main Methods:

  • Theoretical derivation of a temperature scaling law.
  • Experimental validation of the derived scaling law.
  • Computational simulations to corroborate findings.

Main Results:

  • Demonstrated a fundamental limitation of fixed finite temperature quantum annealers.
  • Derived a temperature scaling law requiring temperature reduction with problem size (logarithmic or power-law).
  • Experimental and simulation results confirmed the derived scaling law.

Conclusions:

  • Quantum annealer performance as scalable optimizers is fundamentally limited by fixed finite temperatures.
  • Annealer temperature must be scaled down with problem size for competitive optimization.
  • Findings have significant implications for the design and application of practical quantum annealers.