Stanford inventors have developed a framework that performs digitally verifiable photonic matrix-vector multiplication in integrated photonic networks, which may potentially enable energy-efficient hash functions and cryptocurrency mining.
Stanford researchers in The Fan Group have developed an optical device that can fine tune the color of each photon in a stream of light. Existing methods simply reroute photons of a particular frequency, but do not actually change the photons frequencies.
Researchers at Stanford University have designed a scalable photonic quantum computer which does not require single-photon detectors and which uses minimal quantum resources: one coherently controlled atom.
Machine learning models currently require extensive computational resources and this demand is growing rapidly with new models and applications being introduced.
Engineers in Prof. Shanhui Fan's laboratory have developed an efficient, scalable, in-situ method to train, configure and tune complex photonic circuits for artificial intelligence and machine learning.