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ARXIV:2604.08432 · PHOTONIC COMPUTING · SUBMITTED 10 APR · 17:39 UTC · FRESHNESS STALE
ARXIV:2604.08432PHOTONIC COMPUTINGSUBMITTED 10 APR · 17:39 UTCFRESHNESS STALELuca Nogueira Calçado · Sergei K. Turitsyn · Egor Manuylovich · arXiv
Small-scale photonic Kolmogorov-Arnold networks implemented with standard telecom components for ultrafast inference across various tasks, overcoming optical-electrical-optical bottlenecks.
Opportunity summary
Pain Small-scale photonic Kolmogorov-Arnold networks implemented with standard telecom components for ultrafast inference across various tasks, overcoming optical-electrical-optical bottlenecks.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Small-scale photonic Kolmogorov-Arnold networks implemented with standard telecom components for ultrafast inference across various tasks, overcoming optical-electrical-optical bottlenecks. Here we introduce small-scale photonic Kolmogorov-Arnold networks (SSP-KANs) implemented entirely with standard telecommunications components.
Photonic neural networks promise ultrafast inference, yet most architectures rely on linear optical meshes with electronic nonlinearities, reintroducing optical-electrical-optical bottlenecks. Here we introduce small-scale photonic Kolmogorov-Arnold networks (SSP-KANs) implemented entirely with standard telecommunications components.
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Despite this constrained expressivity, SSP-KANs comprising only a few optical modules achieve strong nonlinear inference performance across classification, regression, and image recognition tasks, approaching…
Photonic Computing moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
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Small-scale photonic Kolmogorov-Arnold networks implemented with standard telecom components for ultrafast inference across various tasks, overcoming optical-electrical-optical bottlenecks.
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10.48550/arXiv.2604.08432Small-scale photonic Kolmogorov-Arnold networks implemented with standard telecom components for ultrafast inference across various tasks, overcoming optical-electrical-optical bottlenecks.
Abstract
Photonic neural networks promise ultrafast inference, yet most architectures rely on linear optical meshes with electronic nonlinearities, reintroducing optical-electrical-optical bottlenecks. Here we introduce small-scale photonic Kolmogorov-Arnold networks (SSP-KANs) implemented entirely with standard telecommunications components. Each network edge employs a trainable nonlinear module composed of a Mach-Zehnder interferometer, semiconductor optical amplifier, and variable optical attenuators, providing a four-parameter transfer function derived from gain saturation and interferometric mixing. Despite this constrained expressivity, SSP-KANs comprising only a few optical modules achieve strong nonlinear inference performance across classification, regression, and image recognition tasks, approaching software baselines with significantly fewer parameters. A four-module network achieves 98.4\% accuracy on nonlinear classification benchmarks inaccessible to linear models. Performance remains robust under realistic hardware impairments, maintaining high accuracy down to 6-bit input resolution and 14 dB signal-to-noise ratio. By using a fully differentiable physics model for end-to-end optimisation of optical parameters, this work establishes a practical pathway from simulation to experimental demonstration of photonic KANs using commodity telecom hardware.
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PROBLEM
Small-scale photonic Kolmogorov-Arnold networks implemented with standard telecom components for ultrafast inference across various tasks, overcoming optical-electrical-optical bottlenecks. Here we introduce small-scale photonic Kolmogorov-Arnold networks (SSP-KANs) implemented...
METHOD
Photonic neural networks promise ultrafast inference, yet most architectures rely on linear optical meshes with electronic nonlinearities, reintroducing optical-electrical-optical bottlenecks. Here we introduce small-scale photonic Kolmogorov-Arnold networks (SSP-KANs) implement...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Despite this constrained expressivity, SSP-KANs comprising only a few optical modules achieve strong nonlinear inference performance across classification, regression, and image recognition tasks, approac...
WHY NOW
Photonic Computing moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Small-scale photonic Kolmogorov-Arnold networks implemented with standard telecom components for ultrafast inference across various tasks, overcoming optical-electrical-optical bottlenecks. Here we introduce small-scale photonic Kolmogorov-Arnold networks (SSP-KANs) implemented entirely with standard telecommunications components.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Photonic neural networks promise ultrafast inference, yet most architectures rely on linear optical meshes with electronic nonlinearities, reintroducing optical-electrical-optical bottlenecks. Here we introduce small-scale photonic Kolmogorov-Arnold networks (SSP-KANs) implemented entirely with standard telecommunications components.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Despite this constrained expressivity, SSP-KANs comprising only a few optical modules achieve strong nonlinear inference performance across classification, regression, and image recognition tasks, approaching software baselines with significantly fewer parameters. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Photonic Computing moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Small-scale photonic Kolmogorov-Arnold networks implemented with standard telecom components for ultrafast inference across various tasks, overcoming optical-electrical-optical bottlenecks.
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