Kernel Boundary SVM/QML Prior

Status: EXTERNAL_REFERENCE_WITH_CANDIDATE_STACK_MAPPING

Sources:

scikit-learn SVM user guide: https://scikit-learn.org/stable/modules/svm.html
Quantum Machine Learning overview: https://thequantuminsider.com/2026/05/08/what-is-quantum-machine-learning/

Why This Matters

Support Vector Machines give the stack a clean boundary primitive:

feature field -> margin boundary -> support-vector witnesses -> replayable decision

The useful part is not generic classification. The useful part is that the decision boundary depends on a sparse subset of training points: the support vectors. In Research Stack terms, those support vectors are candidate receipt leaves for the boundary.

This maps naturally to SMN/eigenmass work:

all observed rows/cells
  -> kernel feature space
  -> support-vector boundary witnesses
  -> Mass Number / SMN gate context
  -> ADMIT, HOLD, or QUARANTINE

Classical Kernel Surface

SVMs support classification, regression, and outlier/novelty detection. Their strength is high-dimensional boundary finding, especially when the number of dimensions is large. Their risk is overfitting and compute blow-up when kernel choice, regularization, and sample size are not controlled.

For stack use:

SVM term	Stack mapping
Support vector	Boundary witness leaf
Margin	Admissibility buffer
Kernel	Feature-space projection law
Decision function	Route/gate score
`sample_weight` / `class_weight`	SMN/evidence-load weighting
One-class SVM	Outlier / quarantine detector

Quantum Kernel Surface

The QML article is useful because it describes the same kernel pattern with a different feature-map backend:

classical data
  -> quantum feature map / circuit encoding
  -> sampled quasi-probabilities
  -> kernel matrix
  -> classical SVM decision

That makes quantum kernels a possible future kernel backend, not a new claim class. The stack can keep the same receipt requirements:

kernel backend declared
support witnesses emitted
kernel matrix hash recorded
decision function replayed
negative controls pass

Claim Boundary

Allowed:

Use classical SVMs as a baseline boundary detector.
Treat support vectors as sparse boundary receipts.
Use one-class SVM as a quarantine/outlier prior.
Treat quantum kernels as an optional future backend for kernel-matrix generation.

HOLD:

Quantum advantage.
Commercially relevant QSVM superiority.
Any hardware quantum claim.
Any physical mass claim from semantic support vectors.
Any proof claim without Lean/replay receipts.

First Fixture

Recommended first fixture:

input: DESI/MaNGA joined-cell eigenmass features
label: avalanche-candidate vs non-candidate, or HOLD/ADMIT proxy
baseline: linear SVM and RBF SVM
receipt: support indices, support-vector hash, kernel params, decision scores,
         train/test split hash, shuffled-label negative control
decision: ADMIT_FIXTURE or HOLD

The kernel must be treated as a projection law, not as authority. The support vectors are useful only if the replay receipt closes.

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