A wide range of applications from Auto-ML [15] to chemistry [6] and drug design [3] require optimizing ablack-boxobjectivefunction (i.e.,itsclosed-form expression, gradient, andconvexity are unknown) through observing noisy function evaluations.

Diffusion regulates numerous natural processes and the dynamics of many successful generative models.

One leading algorithmic paradigm on NISQ computers is theVariational Quantum Algorithm (VQA) with a few prominent examples like the Variational Quantum Eignensolver (VQE) [50], quantum approximate optimization algorithm (QAOA) [20], and more in [4]. Quantum machine learning isafast-developing emerging field (e.g., see the survey [5]) where variational quantum algorithms (VQAs) (e.g., see thesurvey[4]areoneofthemost promising candidates forNISQ applications.

In this paper, we propose anew class of low-cardinality algorithm that generalizes the local update tomaximize asemidefinite relaxation derived from max-k-cut.