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Found 7 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.
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 sampling of ground and low-energy Ising spin configurations with a coherent Ising machine
We show that the nonlinear stochastic dynamics of a measurement-feedback-based coherent Ising machine (MFB-CIM) in the presence of quantum noise can be exploited to sample degenerate ground and low-energy spin configurations of the Ising model.
Quantum Physics Optics
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
