arXiv:2611.06630v1 Announce Type: cross
Abstract: Quantum circuit Born machines (QCBMs) can learn distributions that are hard to sample from classically, but their value depends on generalizing from finite training data. Here, we study how hardware noise affects this generalization. We show that, for any noise process, the generalization gap of a QCBM trained with the maximum mean discrepancy is bounded by the complexity of the distributions the noisy model can reach. Scrambled local Pauli noise contracts this complexity but biases the model towards the uniform distribution, so residual noise acts as a regularizer whose optimal strength can be intermediate. We propose GENIE, a framework that realizes a chosen noise level with capped error mitigation and selects it during training from held-out data within a fixed execution budget, with provably bounded regret. In simulations on synthetic and census data, GENIE stays close to the best of four noise-control strategies, whereas every fixed noise level falls short in many settings.
arxiv
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.
Provides a concrete theoretical and practical framework for noise-aware training of quantum generative models, with clear performance gains in simulations.
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