A 0.651-approximation to quantum Max Cut via Rydberg atoms
Abstract
Quantum Max Cut, also known as the anti-ferromagnetic Heisenberg Hamiltonian, is a QMA-complete problem which serves as a benchmark for approximation algorithms in quantum physics. Here we develop a hybrid approximation algorithm to quantum Max Cut, which uses the natural quantum dynamics of Rydberg atom systems in combination with semidefinite programming and randomized rounding. It achieves a conditional approximation ratio of $0.651$, compared to the best-known ratio of $0.614$ that relies on...
Description / Details
Quantum Max Cut, also known as the anti-ferromagnetic Heisenberg Hamiltonian, is a QMA-complete problem which serves as a benchmark for approximation algorithms in quantum physics. Here we develop a hybrid approximation algorithm to quantum Max Cut, which uses the natural quantum dynamics of Rydberg atom systems in combination with semidefinite programming and randomized rounding. It achieves a conditional approximation ratio of , compared to the best-known ratio of that relies on semidefinite programming alone. The algorithm is robust in the sense that the advantage persists even if the annealing procedure of the Rydberg atom system obtains a state whose energy is only of its true ground state energy. Our approach opens a new route for hybrid quantum-classical algorithms that combine quantum with classical optimization methods.
Source: arXiv:2606.27224v1 - http://arxiv.org/abs/2606.27224v1 PDF: https://arxiv.org/pdf/2606.27224v1 Original Link: http://arxiv.org/abs/2606.27224v1
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Jun 26, 2026
Quantum Computing
Quantum Physics
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