Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization. (arXiv:2201.08629v1 [quant-ph])

Quantum machine learning has emerged as a potential practical application of
near-term quantum devices. In this work, we study a two-layer hybrid
classical-quantum classifier in which a first layer of quantum stochastic
neurons implementing generalized linear models (QGLMs) is followed by a second
classical combining layer. The input to the first, hidden, layer is obtained
via amplitude encoding in order to leverage the exponential size of the fan-in
of the quantum neurons in the number of qubits per neuron. To …

arxiv hybrid optimization stochastic training