Photonic kernel machine learning for ultrafast spectral analysis
arXiv:2110.15241 · doi:10.1103/PhysRevApplied.17.034077
Abstract
We introduce photonic kernel machines, a scheme for ultrafast spectral analysis of noisy radio-frequency signals from single-shot optical intensity measurements. The approach combines the versatility of machine learning and the speed of photonic hardware to reach unprecedented throughput rates. We theoretically describe some of the key underlying principles, and then numerically illustrate the reached performances on a photonic lattice-based implementation. We apply the technique both to picosecond pulsed radio-frequency signals, on energy-spectral-density estimation and a shape classification task, and to continuous signals, on a frequency tracking task. The presented optical computing scheme is resilient to noise while requiring minimal control on the photonic-lattice parameters, making it readily implementable in realistic state-of-the-art photonic platforms.
arXiv comment19 pages, 11 figures. Final version accepted in PRApplied
References in corpus (34)
- Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors
- All-Optical Machine Learning Using Diffractive Deep Neural Networks
- A Survey of the Recent Architectures of Deep Convolutional Neural Networks
- Supervised learning with quantum enhanced feature spaces
- Recent Advances in Physical Reservoir Computing: A Review
- Parallel convolution processing using an integrated photonic tensor core
- Quantum machine learning in feature Hilbert spaces
- Optoelectronic Reservoir Computing
- Vowel recognition with four coupled spin-torque nano-oscillators
- All-optical Reservoir Computing
- Physical reservoir computing -- An introductory perspective
- Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets
- Large-Scale Optical Reservoir Computing for Spatiotemporal Chaotic Systems Prediction
- Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons
- Supervised quantum machine learning models are kernel methods
- Quantum reservoir processing
- Quantum machine learning for quantum anomaly detection
- Optical Reservoir Computing using multiple light scattering for chaotic systems prediction
- Reservoir computing with the frequency, phase and amplitude of spin-torque nano-oscillators
- Polaritonic neuromorphic computing outperforms linear classifiers
- Quantum federated learning through blind quantum computing
- Random Projections through multiple optical scattering: Approximating kernels at the speed of light
- Quantum neuromorphic computing
- Photonic extreme learning machine by free-space optical propagation
- A sub-1-volt analog metal oxide memristive-based synaptic device for energy-efficient spike-based computing systems
- Neuromorphic computing in Ginzburg-Landau polariton lattice systems
- Experimental quantum kernel machine learning with nuclear spins in a solid
- Realising and compressing quantum circuits with quantum reservoir computing
- Creating and concentrating quantum resource states in noisy environments using a quantum neural network
- Kernel computations from large-scale random features obtained by Optical Processing Units
- Superpolynomial Quantum Enhancement in Polaritonic Neuromorphic Computing
- Equilibrium Propagation with Continual Weight Updates
- Protocol for secure quantum machine learning at a distant place
- Quantum secure learning with classical samples