pith:LO6524MP
Small-scale photonic Kolmogorov-Arnold networks using standard telecom nonlinear modules
Photonic Kolmogorov-Arnold networks built from a few standard telecom modules achieve 98.4% accuracy on nonlinear tasks
arxiv:2604.08432 v2 · 2026-04-09 · physics.optics · cs.AI
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\pithnumber{LO6524MPAKGZHFXV5RJVVDG2DA}
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Claims
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.
The four-parameter optical transfer function per edge is sufficiently expressive for the target tasks and that the end-to-end differentiable physics model accurately predicts real-device behavior without unmodeled impairments or fabrication variations.
Small photonic KANs using commodity telecom nonlinear modules reach 98.4% accuracy on nonlinear classification with only four modules and remain robust to hardware impairments.
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| First computed | 2026-05-20T00:05:44.543150Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
5bbddd718f028d9396f5ec535a8cda18182deeece178305507501648ecfe82d0
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/LO6524MPAKGZHFXV5RJVVDG2DA \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 5bbddd718f028d9396f5ec535a8cda18182deeece178305507501648ecfe82d0
Canonical record JSON
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