Verified Neuro-Symbolic Verification of Quantum Neural Network Outputs via Bounded State Determinism

Quantum Neural Network (QNN) outputs are probabilistic, unauditable, and provide no formal guarantee of internal consistency. This paper presents a seven-layer verified neuro-symbolic pipeline that addresses this gap. The pipeline applies Bounded State Determinism to QNN outputs, treating each predicted probability as a stimulus verified against a formally specified prior. It produces: a V8 lambda distance from the uniform prior, a Clamp Guard drift classification per output, a Scallop Datalog sum invariant, a CAIME tamper-evident chain hash, and a Z3 formal envelope proof. Demonstrated on the QNN football predictions of Sun and Chu (Scientific Reports, 2025), the pipeline produces a VALID verdict with lambda distance 9.97×10⁻³ and a five-link tamper-evident audit trail. The contribution is a verification layer, not a prediction model: a system that cannot determine whether a QNN prediction is correct, but can formally certify whether it is internally consistent, probabilistically bounded, and tamper-evident. Related publications:- NALP Paper: https://doi.org/10.5281/zenodo.20065953- Reproduction Log: https://doi.org/10.5281/zenodo.20063061- Canonical Audit: https://doi.org/10.5281/zenodo.20066792