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  1. Home
  2. Initial State Encoding Via Reverse Quantum Annealing And H-gain Features.
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  2. Initial State Encoding Via Reverse Quantum Annealing And H-gain Features.

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Initial State Encoding via Reverse Quantum Annealing and H-Gain Features.

Elijah Pelofske1, Georg Hahn2, Hristo Djidjev1,3

  • 1CCS-3 Information Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545 USA.

IEEE Transactions on Quantum Engineering
|January 5, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study explores reverse annealing (RA) and h-gain (HG) features on D-Wave quantum annealers to improve optimization solutions. The h-gain technique offers a viable alternative to reverse annealing for certain problems.

Keywords:
Anneal scheduleBayesian optimizationD-WaveIsing modelQUBOh-gain (HG)maximum cutquantum annealing (QA)reverse annealing (RA)

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Area of Science:

  • Quantum Computing
  • Computational Optimization

Background:

  • D-Wave quantum annealers offer cloud access to quantum annealing for optimization.
  • Improving solution quality in quantum annealing is an active research area.

Purpose of the Study:

  • To investigate methods for enhancing quantum annealer solution quality by encoding initial states.
  • To compare the effectiveness of reverse annealing (RA) and h-gain (HG) features for initial state encoding.

Main Methods:

  • Exploration of D-Wave's reverse annealing (RA) feature for refining solutions.
  • Utilizing the h-gain (HG) feature to bias annealing from a specific initial state.
  • Development and testing of a hybrid RA-HG method and iterative application of both techniques.
  • Evaluation on combinatorial optimization problems like max-cut and max-clique, and on spin glasses using D-Wave hardware.

Main Results:

  • The h-gain (HG) feature is demonstrated as a viable alternative to RA for initial state encoding in quantum annealing.
  • Iterative application of RA and HG shows potential for monotonically improving solutions.
  • Performance of iterative RA and HG is analyzed on various problems and D-Wave hardware architectures (Chimera, Pegasus).

Conclusions:

  • Both RA and HG are effective for initial state encoding in quantum annealing.
  • Iterative application of these techniques can lead to progressively better solutions.
  • HG presents a promising alternative or complement to RA for optimizing problem-solving on quantum annealers.