A Quantum-Circuit Analogue for Probabilistic Decision Commitment

We present a minimal, fully reproducible quantum-circuit analogue for probabilistic decisioncommitment. A two-qubit variational quantum circuit, run on a classical statevector simulator,angle-encodes binary inputs and is trained (COBYLA) against a Pauli-Z expectation objective.Two experiments are reported: (1) an untrained circuit yields a probabilistic measurementspread that training reshapes toward a near-deterministic outcome — the collapse itself beingperformed by measurement — modelling an unresolved decision distribution resolving into acommitment; and (2) the same architecture learns XOR (4/4, zero training loss), the canonicalnon-linearly-separable function, with the entangling operation supplying the cross-featureinteraction required in this ansatz. We read the Pauli-Z expectation as a graded confidencesignal and propose a magnitude threshold as a measurable commit condition for probabilisticdecision systems, including external-scaffolding continuity frameworks for stateless languagemodels. We claim no quantum advantage and no quantum execution of language models; thecontribution is pedagogical clarity and a decision-commitment design lens — established resultsin quantum machine learning, reframed for decision discipline. All code is provided.