We explore the parallel information processing capacity of a broadband diffractive optical network and demonstrate that a single diffractive network could perform a large group of arbitrarily-selected, complex-valued linear transformations between its input and output fields-of-view at different wavelengths, accessed sequentially or simultaneously. Through deep learning-based training of the thickness values of its diffractive features, we demonstrate that a wavelength-multiplexed diffractive processor can implement W>180 complex-valued linear transformations with a negligible error when its number of trainable diffractive features approaches 2W×I×O, where I and O refer to the number of input and output pixels, respectively.
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