Latest AI updates

Noise-regularized training of quantum circuit Born machines under a finite shot budget

This paper analyzes how hardware noise affects generalization in quantum circuit Born machines (QCBMs) trained with maximum mean discrepancy. It proves that the generalization gap is bounded by the complexity of the noisy model's reachable distributions, and that scrambled local Pauli noise acts as a regularizer with an optimal intermediate strength. The authors propose GENIE, a framework that selects a noise level during training using held-out data under a fixed execution budget, with provably bounded regret. Simulations on synthetic and census data show GENIE matches the best of four noise-control strategies, while fixed noise levels often underperform.