A silicon photonic single-qubit classifier achieves about 86 percent accuracy when trained with an average of roughly two photons per sample, matching simulation.
All-Optical Machine Learning Using Diffractive Deep Neural Networks
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abstract
We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively. We experimentally demonstrated the success of this framework by creating 3D-printed D2NNs that learned to implement handwritten digit classification and the function of an imaging lens at terahertz spectrum. With the existing plethora of 3D-printing and other lithographic fabrication methods as well as spatial-light-modulators, this all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs.
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Experimental investigation of single qubit quantum classifier with small number of samples
A silicon photonic single-qubit classifier achieves about 86 percent accuracy when trained with an average of roughly two photons per sample, matching simulation.