Entanglement changes outcomes under high pressure, not low. Scenarios 1 and 2 (revenue pressure 0.9) diverged completely between CNOT and independent circuits. Scenarios 3 and 4 (pressure 0.1) converged. The CNOT gate reads variable relationships — under stress those relationships change the outcome.
2
The Scenario 3 anomaly is real and persistent. Low revenue (0.1) + high technical (0.9) selects ACTIVE SALES MODE (01) rather than DEEP TECHNICAL BUILD (10) at 90–96% confidence across all backends and both circuit types. The CNOT gate reads low revenue as avoidance behavior even when technical drive is high. This is the finding that no guardrail could generate.
3
Hardware and simulator are consistent. IonQ Forte-1 (real qubits) and AWS Braket SV1 (managed simulator) produce results within 2–5% of each other across all scenarios. The circuit design is valid — these are not simulation artifacts.
4
Classical replication is insufficient under high-pressure conditions. Independent qubits (no CNOT) cannot replicate the entangled circuit's output when both input signals are high. This directly addresses the "just use weighted probability" criticism — the entanglement layer produces qualitatively different context selection under the conditions where it matters most.
5
7 independent runs, 3 backends, consistent throughout. Local simulator (Python), AWS Braket SV1, and IonQ Forte-1 all confirm the same pattern. Shot counts ranged from 100 to 1,000. Confidence range: 83–98%.
ChronoAI Solutions · chronoaisolutions.com · Quantum Context Research Series · May 2026