Can Constrained Quantum Annealing Be Effective in Noisy Quantum Annealers?
Ryoya Igata, Myonsok I, Yuya Seki, Yuta Mizuno, and Shohei Watabe

TL;DR
This paper compares penalty-based and constrained quantum annealing methods for graph partitioning under various noise models, finding that PQA is more stable in weak noise, but CQA can outperform under strong phase-flip noise, though both face practical challenges.
Contribution
The study provides a detailed analysis of PQA and CQA performance under realistic noise conditions, highlighting the limitations and potential of constrained quantum annealing in noisy environments.
Findings
PQA shows stable success probabilities across noise models.
CQA's performance varies significantly and experiences leakage.
CQA can outperform PQA under strong phase-flip noise, but success remains low.
Abstract
We investigate the performance of penalty-based quantum annealing (PQA) and constrained quantum annealing (CQA) in solving the graph partitioning problem under various noise models, including depolarizing, bit-flip, and phase-flip noise. We found that even in the absence of noise, the relative superiority of PQA or CQA is highly problem-dependent. PQA generally demonstrates relatively stable success probabilities, while CQA's performance varies significantly across problem instances. Notably, CQA experiences leakage from the constraint-satisfying subspace under most noise models, with the exception of phase-flip noise, where no leakage occurs exactly. The dependence of success probability on noise strength reveals that PQA consistently outperforms CQA in the weak noise regime, whereas CQA achieves higher success probabilities under strong phase-flip noise. However, even in this case,…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography
