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 Image Identification with Programmable Matrix Transformation
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abstract
An optical neural network is proposed and demonstrated with programmable matrix transformation and nonlinear activation function of photodetection (square-law detection). Based on discrete phase-coherent spatial modes, the dimensionality of programmable optical matrix operations is 30~37, which is implemented by spatial light modulators. With this architecture, all-optical classification tasks of handwritten digits, objects and depth images are performed on the same platform with high accuracy. Due to the parallel nature of matrix multiplication, the processing speed of our proposed architecture is potentially as high as7.4T~74T FLOPs per second (with 10~100GHz detector)
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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.