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Found 9 papers, 4 papers with code
Training of Physical Neural Networks
Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems.
Scaling on-chip photonic neural processors using arbitrarily programmable wave propagation
On-chip photonic processors for neural networks have potential benefits in both speed and energy efficiency but have not yet reached the scale at which they can outperform electronic processors.
The hardware is the software
Human brains and bodies are not hardware running software: the hardware is the software.
Quantum-noise-limited optical neural networks operating at a few quanta per activation
We experimentally demonstrated MNIST classification with a test accuracy of 98% using an optical neural network with a hidden layer operating in the single-photon regime; the optical energy used to perform the classification corresponds to 0. 008 photons per multiply-accumulate (MAC) operation, which is equivalent to 0. 003 attojoules of optical energy per MAC.
Optical Transformers
We conclude that with well-engineered, large-scale optical hardware, it may be possible to achieve a $100 \times$ energy-efficiency advantage for running some of the largest current Transformer models, and that if both the models and the optical hardware are scaled to the quadrillion-parameter regime, optical computers could have a $>8, 000\times$ energy-efficiency advantage over state-of-the-art digital-electronic processors that achieve 300 fJ/MAC.
Image sensing with multilayer, nonlinear optical neural networks
In image sensing, a measurement, such as of an object's position, is performed by computational analysis of a digitized image.
Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems
Deep neural networks have become a pervasive tool in science and engineering.
An optical neural network using less than 1 photon per multiplication
Here, we experimentally demonstrate an optical neural network achieving 99% accuracy on handwritten-digit classification using ~3. 2 detected photons per weight multiplication and ~90% accuracy using ~0. 64 photons (~$2. 4 \times 10^{-19}$ J of optical energy) per weight multiplication.
Efficient simulation of ultrafast quantum nonlinear optics with matrix product states
Ultra-short pulses propagating in nonlinear nanophotonic waveguides can simultaneously leverage both temporal and spatial field confinement, promising a route towards single-photon nonlinearities in an all-photonic platform.
Quantum Physics Optics
