Overparameterized neural networks generalize well but are expensive to train. Ideally, one
would like to reduce their computational cost while retaining their generalization benefits …

Using unitary (instead of general) matrices in artificial neural networks (ANNs) is a promising
way to solve the gradient explosion/vanishing problem, as well as to enable ANNs to learn …

Universal unitary photonic devices can apply arbitrary unitary transformations to a vector of
input modes and provide a promising hardware platform for fast and energy-efficient …

Evolution strategies are a class of evolutionary algorithms for black-box optimization and
achieve state-of-the-art performance on many benchmarks and real-world applications …

Quantifying how far the output of a learning algorithm is from its target is an essential task in
machine learning. However, in quantum settings, the loss landscapes of commonly used …

Modern neural network architectures use structured linear transformations, such as low-rank
matrices, sparse matrices, permutations, and the Fourier transform, to improve inference …

Boson sampling is a sampling task proven to be hard to simulate efficiently using classical
computers under plausible assumptions, which makes it an appealing candidate for …

Fast transforms correspond to factorizations of the form, where each factor is sparse and
possibly structured. This paper investigates essential uniqueness of such factorizations, ie …

In this paper, we show that extending the butterfly operations from the FFT algorithm to a
general Butterfly Transform (BFT) can be beneficial in building an efficient block structure for …

In this paper, we show that extending the butterfly operations from the FFT algorithm to a
general Butterfly Transform (BFT) can be beneficial in building an efficient block structure for …