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Near-term noisy intermediate-scale quantum circuits can efficiently implementimplicit probabilistic models in discrete spaces, supporting distributions thatare practically infeasible to sample from using classical means. One of thepossible applications of such models, also known as Born machines, isprobabilistic inference, which is at the core of Bayesian methods. This paperstudies the use of Born machines for the problem of training binary Bayesianneural networks. In the proposed approach, a Born machine is used to model thevariational distribution of the binary weights of the neural network, and datafrom multiple tasks is used to reduce training data requirements on new tasks.The method combines gradient-based meta-learning and variational inference viaBorn machines, and is shown in a prototypical regression problem to outperformconventional joint learning strategies.

Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines