Quantum machine learning is a promising programming paradigm for the
optimization of quantum algorithms in the current era of noisy intermediate
scale quantum (NISQ) computers. A fundamental challenge in quantum machine
learning is generalization, as the designer targets performance under testing
conditions, while having access only to limited training data. Existing
generalization analyses, while identifying important general trends and scaling
laws, cannot be used to assign reliable and informative "error bars" to the
decisions made by quantum models. In this article, we propose a general
methodology that can reliably quantify the uncertainty of quantum models,
irrespective of the amount of training data, of the number of shots, of the
ansatz, of the training algorithm, and of the presence of quantum hardware
noise. The approach, which builds on probabilistic conformal prediction, turns
an arbitrary, possibly small, number of shots from a pre-trained quantum model
into a set prediction, e.g., an interval, that provably contains the true
target with any desired coverage level. Experimental results confirm the
theoretical calibration guarantees of the proposed framework, referred to as
quantum conformal prediction.

该论文提出了一种处理量子机器学习中普遍存在的不确定性的通用方法学，该方法可以在训练数据量有限、量子计算硬件噪声存在的情况下可靠地度量量子模型的不确定，并基于概率共同预测来达到对真实目标的可信度量。