Central Thesis

QNFM proposes that time-series covariance structure — the way variables co-move — carries geometric information that is invisible to methods which flatten or linearize covariance matrices. A quantum circuit with a fixed measurement basis acts as a geometric probe: it reads the directional properties of the covariance manifold without adapting to the data.

Papers 1 and 2 established the first-order invariant (polarity) in EEG data. Paper 3 extends the framework to predict higher-order invariants and cross-domain applicability.

Higher-Order Invariants — Testable Predictions

These are predictions, not findings. Each requires empirical validation with specific falsification criteria defined in the invariant hierarchy.

Cross-Domain Validation Plan

If QNFM is a general framework (not just an EEG trick), the same instrument philosophy should apply to other domains where covariance structure carries signal.

Polarity as 1st-Order Invariant

Paper 3 positions polarity (established by Papers 1 and 2) as the foundation of the invariant hierarchy, not a standalone finding. The framework predicts that polarity is the simplest measurable invariant, and that higher-order invariants carry additional discriminative information.

Confirmed: polarity exists and is measurable. Hypothesized: it is the first member of a hierarchy. The distinction matters — the hierarchy is a prediction of the framework, not an established fact.

Status

Drafting. No timeline commitments. The theoretical framework must be mature enough to make falsifiable predictions before submission.

← Paper 2 Findings ↑ Framework →