Landscape-Similarity-Guided Optimization in Divide-and-Conquer QAOA
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arXiv
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| Format: | Preprint |
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2026
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| author | Sang, Sokea Hour, Leanghok Lee, Sanghyeon Patra, Aniket Park, Hee Chul Park, Moon Jip Han, Youngsun |
| author_facet | Sang, Sokea Hour, Leanghok Lee, Sanghyeon Patra, Aniket Park, Hee Chul Park, Moon Jip Han, Youngsun |
| contents | Across diverse synthetic and real-world interaction graphs, the variational landscapes of reduced Quantum Approximate Optimization Algorithm (QAOA) instances obtained via variable freezing exhibit a robust universality. Leveraging this structure, we introduce Doubly Optimized QAOA (DO-QAOA), which lowers runtime and quantum measurement overhead while maintaining a competitive approximation ratio gap (ARG). Adapting the replica-overlap framework of spin-glass physics, we define a landscape-overlap order parameter $q$ to quantify geometric correlations between energy landscapes, revealing a sharp landscape-similarity transition as graph connectivity is tuned. Notwithstanding this transition, the dominant convex features of nearly all conditioned sub-instances remain aligned across both phases. Exploiting this persistence, DO-QAOA collapses the nominal $2^m$ reduced instances generated by freezing $m$ qubits into $K = O(1)$ effective landscape classes, eliminating the exponential proliferation in $m$. By leveraging landscape structure, DO-QAOA provides a scalable route to hybrid quantum-classical optimization under realistic hardware constraints, with potential applicability across variational quantum algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21689 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Landscape-Similarity-Guided Optimization in Divide-and-Conquer QAOA Sang, Sokea Hour, Leanghok Lee, Sanghyeon Patra, Aniket Park, Hee Chul Park, Moon Jip Han, Youngsun Quantum Physics Across diverse synthetic and real-world interaction graphs, the variational landscapes of reduced Quantum Approximate Optimization Algorithm (QAOA) instances obtained via variable freezing exhibit a robust universality. Leveraging this structure, we introduce Doubly Optimized QAOA (DO-QAOA), which lowers runtime and quantum measurement overhead while maintaining a competitive approximation ratio gap (ARG). Adapting the replica-overlap framework of spin-glass physics, we define a landscape-overlap order parameter $q$ to quantify geometric correlations between energy landscapes, revealing a sharp landscape-similarity transition as graph connectivity is tuned. Notwithstanding this transition, the dominant convex features of nearly all conditioned sub-instances remain aligned across both phases. Exploiting this persistence, DO-QAOA collapses the nominal $2^m$ reduced instances generated by freezing $m$ qubits into $K = O(1)$ effective landscape classes, eliminating the exponential proliferation in $m$. By leveraging landscape structure, DO-QAOA provides a scalable route to hybrid quantum-classical optimization under realistic hardware constraints, with potential applicability across variational quantum algorithms. |
| title | Landscape-Similarity-Guided Optimization in Divide-and-Conquer QAOA |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2602.21689 |