Continuous-Variable Deep Quantum Neural Networks for Flexible Learning of Structured Classical Information (Communications in Computational Physics, 2021)
Quantum computation using optical modes has been well-established in itsability to construct deep neural networks. These networks have been shown to beflexible both architecturally as well as in terms of the type of data being processed.We leverage this property of the Continuous-Variable (CV) model to construct stackedsingle mode networks that are shown to learn structured classical information, whileplacing no restrictions on the size of the network, and at the same time maintaining itscomplexity. The hallmark of the CV model is its ability to forge non-linear functionsusing a set of gates that allows it to remain completely unitary. The proposed modelexemplifies that the appropriate photonic hardware can be integrated with presentday optical communication systems to meet our information processing requirements.In this paper, using the Strawberry Fields software library on the MNIST dataset ofhand-written digits, we demonstrate the adaptability of the network to learn classicalinformation in a multitude of machine learning tasks to very large fidelities.
Keywords: Quantum computing; continuous variable model; quantum optics.