论文标题
通过出生机器的贝叶斯二进制神经网络的量子辅助元学习
Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines
论文作者
论文摘要
近期嘈杂的中间尺度量子电路可以在离散空间中有效地实现隐式概率模型，从而支持使用经典手段来样本实际上不可行的分布。这种模型的可能应用之一，也称为出生的机器，是概率推断，它是贝叶斯方法的核心。本文研究了诞生机器来培训二元贝叶斯神经网络的问题。在拟议的方法中，出生的机器用于对神经网络的二进制重量的变分分布进行建模，而来自多个任务的数据用于减少对新任务的培训数据要求。该方法结合了基于梯度的元学习和通过出生机器的变异推断，并在原型回归问题中显示，以优于常规的联合学习策略。
Near-term noisy intermediate-scale quantum circuits can efficiently implement implicit probabilistic models in discrete spaces, supporting distributions that are practically infeasible to sample from using classical means. One of the possible applications of such models, also known as Born machines, is probabilistic inference, which is at the core of Bayesian methods. This paper studies the use of Born machines for the problem of training binary Bayesian neural networks. In the proposed approach, a Born machine is used to model the variational distribution of the binary weights of the neural network, and data from multiple tasks is used to reduce training data requirements on new tasks. The method combines gradient-based meta-learning and variational inference via Born machines, and is shown in a prototypical regression problem to outperform conventional joint learning strategies.
