Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting
Abstract
Progress towards a quantum advantage using known heuristic methods for combinatorial optimization is impeded by hardware noise and limited qubit count. Here, we propose a noise-aware adaptive approach to quantum approximate optimization, Noise-Directed Adaptive Warm-Starting (ND-AWS), that builds on recent concepts such as Warm-Start QAOA and Noise-Directed Adaptive Remapping. By leveraging bitflip gauge transformations, our algorithm exploits amplitude-damping-like noise components. We experimentally implement high-performance quantum optimization ansätze on 100-qubit Ising Hamiltonians, showing that ND-AWS generally improves the performance over a non-gauge-transformed iterative Warm-Starting variant, at no additional circuit cost. This places our results among the highest-quality demonstrations of quantum optimization with similar ansätze at this scale. Crucially, the simplicity of the framework opens the door for future enhancements such as adaptive bias schedules, and integration with classical solvers.
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BibTeX
@misc{maciejewski2026quantum,
title = {{Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting}},
author = {Filip B. Maciejewski and Stuart Hadfield and Oscar Wallis and George Pennington and Sebastian Brandhofer and Stefan Woerner and Daniel J. Egger and Davide Venturelli},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.09368},
eprint = {2607.09368},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.09368},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting
AU - Filip B. Maciejewski
AU - Stuart Hadfield
AU - Oscar Wallis
AU - George Pennington
AU - Sebastian Brandhofer
AU - Stefan Woerner
AU - Daniel J. Egger
AU - Davide Venturelli
PY - 2026
DA - 2026/09/23
PB - arXiv
DO - 10.48550/arxiv.2607.09368
UR - https://arxiv.org/abs/2607.09368
AB - Progress towards a quantum advantage using known heuristic methods for combinatorial optimization is impeded by hardware noise and limited qubit count. Here, we propose a noise-aware adaptive approach to quantum approximate optimization, Noise-Directed Adaptive Warm-Starting (ND-AWS), that builds on recent concepts such as Warm-Start QAOA and Noise-Directed Adaptive Remapping. By leveraging bitflip gauge transformations, our algorithm exploits amplitude-damping-like noise components. We experimentally implement high-performance quantum optimization ansätze on 100-qubit Ising Hamiltonians, showing that ND-AWS generally improves the performance over a non-gauge-transformed iterative Warm-Starting variant, at no additional circuit cost. This places our results among the highest-quality demonstrations of quantum optimization with similar ansätze at this scale. Crucially, the simplicity of the framework opens the door for future enhancements such as adaptive bias schedules, and integration with classical solvers.
ER -