Light forms the global backbone of information transmission yet is rarely used for information transformation. Digital optical logic faces fundamental physical challenges1. Many analog approaches have been researched2,3,4, but analog optical co-processors have faced major economic challenges. Optical systems have never achieved competitive manufacturability, nor have they satisfied a sufficiently general processing demand better than digital electronic contemporaries. Incipient changes in the supply and demand for photonics have the potential to spark a resurgence in optical information processing.
Chip startup Lightmatter has received an infusion of $11 million from investors to help bring the world's first silicon photonics processor for AI to market. Using technology originally developed at MIT, the company is promising "orders of magnitude performance improvements over what's feasible using existing technologies."
At Xanadu we are developing a photonic quantum computer: a device that processes information stored in quantum states of light. We are very excited by the possibilities that this approach brings. Photonic quantum computers naturally use continuous degrees of freedom -- like the amplitude and phase of light -- to encode information. This continuous, or analog, structure makes photonic devices a natural platform for quantum versions of neural networks. How do we mimic a neural network using a photonic system?
The Princeton University researchers developed the world's first integrated silicon photonic neuromorphic chip. Each of these "neuron-like" nodes works with a specific wavelength of light. The light rapidly circulates in the node, and when released, it affects the output of a laser. When the laser output returns to the nodes, it completes the circuit.
Published this week on Arxiv, the new photonic neural network is so blazingly efficient that when pitted against a conventional CPU in solving differential equations, it performed roughly 2,000 times faster. According to one estimate, switching to all-light computing could eventually make the process millions of times faster. "Photonic neural networks leveraging silicon photonic platforms could access new regimes of ultrafast information processing for radio, control, and scientific computing," wrote the authors in their paper.