When Does Interference Help Learning? Kernel Geometry as a Pre-Experimental Test for Photonic Reservoir Computing

arXiv:2610.11360 · quant-ph, cs.CE · Submitted 2026-10-08 · Read on arXiv

quant-ph, cs.CE

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Alignment — 54× in this paper · explained in 1 episode
• Accuracy — 22× in this paper · explained in 1 episode
• Regularization — 12× in this paper · explained in 3 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Entanglement — 2× in this paper · explained in 3 episodes
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Phase Transitions — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Large-scale quantum reservoir learning with an analog quantum computer
• Photonic Quantum-Accelerated Machine Learning
• Hybrid Boson Sampling-Neural Network Architecture for Enhanced Classification
• Enhanced Image Recognition Using Gaussian Boson Sampling
• Harnessing Photon Indistinguishability in Quantum Extreme Learning Machines
• Supervised quantum machine learning models are kernel methods
• Large-scale quantum reservoir computing using a Gaussian Boson Sampler
• Finite-Time Analysis of Natural Actor-Critic for POMDPs
• Photon Number-Resolving Quantum Reservoir Computing

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