Generalized Probabilistic Approximate Optimization Algorithm.

ABSTRACT: We introduce the generalized Probabilistic Approximate Optimization Algorithm (PAOA), a classical variational Monte Carlo framework that extends and formalizes the recently introduced PAOA, enabling parameterized and fast sampling on present-day Ising machines and probabilistic computers. PAOA operates by iteratively modifying the couplings of a network of binary stochastic units, guided by cost evaluations from independent samples. We establish a direct correspondence between derivative-free updates and the gradient of the full Markov flow over the exponentially large state space, showing that PAOA admits a principled variational formulation. Simulated annealing emerges as a limiting case under constrained parameterizations, and we implement this regime on an FPGA-based probabilistic comp

SUBMITTER: Abdelrahman AS 

PROVIDER: S-EPMC12804969 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature