Spin-Boson Mapping of the Quantum Approximate Optimization Algorithm
Sami Boulebnane, Abid Khan, Minzhao Liu, Sami Boulebnane, Dylan Herman, Ruslan Shaydulin, Marco Pistoia · Physical Review Letters · 2026
The quantum approximate optimization algorithm (QAOA) achieves monotonically improving performance with circuit depth p, yet the study of the high-depth regime has been obstructed by the exponential in p cost of existing exact evaluation techniques. In this Letter, we prove that, in the infinite-size limit, the depth-p QAOA state for the Sherrington-Kirkpatrick (SK) model converges to the state of a spin coupled to p bosonic modes. We simulate the spi-boson system using matrix product states and provide numerical evidence that QAOA obtains a (1-ε) approximation to the optimal energy of the SK model with circuit depth O(n/ε^{1.13}) in the average case. The modest computational cost of our approach allows us to optimize QAOA parameters and observe that QAOA achieves ϵ≲2.2% at p=160 in the infinite-size limit, extending far beyond p≤20 accessible to prior exact methods. Our mapping provides a many-body route to study and optimize high-depth QAOA in regimes previously inaccessible to exact evaluation.