Quantum Neural Field Mapping as the umbrella theory connecting fixed geometric probes, measurable invariants, and application domains. This paper is in development — the content below describes the research direction, not established findings.
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.
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.
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.
Drafting. No timeline commitments. The theoretical framework must be mature enough to make falsifiable predictions before submission.