Quantum Algorithms for Finding Vacua in the ADK Landscape
hep-th, quant-ph
Submitted: 2026-10-05
Updated: 2026-10-05
Comments: 18 pages, 6 figures
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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.
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(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.
Sources
- Planck 2018 results. VI. Cosmological parameters
- Hierarchies from Fluxes in String Compactifications
- de Sitter Vacua in String Theory
- Flux compactifications in string theory: a comprehensive review
- Flux Compactification
- Quantization of Four-form Fluxes and Dynamical Neutralization of the Cosmological Constant
- The Anthropic Landscape of String Theory
- The statistics of string/M theory vacua
- Counting Flux Vacua
- Distributions of flux vacua
- The String Landscape and the Swampland
- A Finite Landscape?
- Finiteness and the Swampland
- Computational complexity of the landscape I
- Computational Complexity of Vacua and Near-Vacua in Field and String Theory
- Predictive Landscapes and New Physics at a TeV
- Computational complexity of the landscape II - Cosmological considerations
- Fast optimization algorithms and the cosmological constant
- Branes with Brains: Exploring String Vacua with Deep Reinforcement Learning
- Searching the Landscape of Flux Vacua with Genetic Algorithms
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