Quantum Algorithms for Finding Vacua in the ADK Landscape
Abstract
We study quantum algorithms for finding a vacuum with a small cosmological constant in the toy model for string Landscape proposed by Arkani-Hamed, Dimopoulos, and Kachru. First, we construct a function-value oracle and use it for Grover search with query complexity 𝒪(2N/2), where N is the number of scalar fields. Then, we point out that, by exploiting the linearity of the vacuum energy, the problem can be reduced to a collision problem between two blocks of the bit string. With this structure, we construct two quantum algorithms with query complexity 𝒪(2N/3). Compared with the pseudo-polynomial classical algorithm of Denef and Douglas, the quantum algorithms yield a large reduction in computational cost for the physically motivated parameter values.
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BibTeX
@misc{endo2026quantum,
title = {{Quantum Algorithms for Finding Vacua in the ADK Landscape}},
author = {Shirabe Endo and Yuta Hamada},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.07439},
eprint = {2610.07439},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.07439},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Quantum Algorithms for Finding Vacua in the ADK Landscape
AU - Shirabe Endo
AU - Yuta Hamada
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2610.07439
UR - https://arxiv.org/abs/2610.07439
AB - We study quantum algorithms for finding a vacuum with a small cosmological constant in the toy model for string Landscape proposed by Arkani-Hamed, Dimopoulos, and Kachru. First, we construct a function-value oracle and use it for Grover search with query complexity 𝒪(2^N/2), where N is the number of scalar fields. Then, we point out that, by exploiting the linearity of the vacuum energy, the problem can be reduced to a collision problem between two blocks of the bit string. With this structure, we construct two quantum algorithms with query complexity 𝒪(2^N/3). Compared with the pseudo-polynomial classical algorithm of Denef and Douglas, the quantum algorithms yield a large reduction in computational cost for the physically motivated parameter values.
ER -