ChronoAI Quantum Experiments

Results Dashboard — May 2026 · IonQ Forte-1 + AWS Braket SV1 + Local Simulator

"Should I spend today building the quantum context layer or making cold calls?"
11
Total runs
3
Backends
4
Scenarios
94–96%
Confidence range
2/4
Scenarios changed by CNOT
Experiment 1 — Quantum context frame selection
IonQ Forte-1 (real hardware) AWS Braket SV1 Simulator Local Simulator
Scenario 1
Revenue pressure: 0.9
Technical: 0.2
→ STRATEGIC / BIG PICTURE (11)
11
89%
89%
10
14%
00
3%
IonQ: 83% SV1: 88% Local: 91%
Scenario 2
Revenue pressure: 0.9
Technical: 0.9
→ DEEP TECHNICAL BUILD (10)
10
96%
96%
11
3%
01
1%
IonQ: 96% SV1: 97% Local: 95%
Scenario 3
⚠ ANOMALY — Persistent across all backends
Revenue pressure: 0.1
Technical: 0.9
→ ACTIVE SALES MODE (01) — not technical build
01
96%
96%
10
3%
00
2%
IonQ: 94% SV1: 96% Local: 95–96%
Scenario 4
Revenue pressure: 0.1
Technical: 0.1
→ SCRAPPY / NO BUDGET (00)
00
96%
96%
01
3%
11
3%
IonQ: 96% SV1: 95% Local: 95%
Experiment 2 — Entanglement proof: CNOT vs independent qubits
IonQ Forte-1 (real hardware) AWS Braket SV1 Simulator

Hypothesis: CNOT entanglement produces non-obvious frame collapse that independent qubits cannot replicate.

Scenario 1 — High pressure, strategic task
Revenue: 0.9 · Technical: 0.2
Diverged — CNOT changed the outcome
With CNOT (RY + RY + CNOT)
STRATEGIC / BIG PICTURE (11)
11
89%
89%
10
7%
IonQ: 89% · SV1: 86%
Without CNOT (independent qubits)
DEEP TECHNICAL BUILD (10)
10
87%
87%
11
11%
IonQ: 87% · SV1: 86%
Scenario 2 — High pressure, technical task
Revenue: 0.9 · Technical: 0.9
Diverged — CNOT changed the outcome
With CNOT (RY + RY + CNOT)
DEEP TECHNICAL BUILD (10)
10
92%
92%
01
5%
IonQ: 92% · SV1: 95%
Without CNOT (independent qubits)
STRATEGIC / BIG PICTURE (11)
11
92%
92%
10
6%
IonQ: 92% · SV1: 98%
Scenario 3 — Low pressure, technical task
Revenue: 0.1 · Technical: 0.9
Converged — same outcome either way
With CNOT (RY + RY + CNOT)
ACTIVE SALES MODE (01)
01
90%
90%
IonQ: 90% · SV1: 94%
Without CNOT (independent qubits)
ACTIVE SALES MODE (01)
01
93%
93%
IonQ: 93% · SV1: 97%
Scenario 4 — Low pressure, big picture
Revenue: 0.1 · Technical: 0.1
Converged — same outcome either way
With CNOT (RY + RY + CNOT)
SCRAPPY / NO BUDGET (00)
00
93%
93%
IonQ: 93% · SV1: 98%
Without CNOT (independent qubits)
SCRAPPY / NO BUDGET (00)
00
95%
95%
IonQ: 95% · SV1: 97%
Key findings
1
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%.