Separability falsification — is one IBM job really one quantum system?

The primary architecture claim for QPC: when K objective contextures run coupled in one circuit, the measurement statistics show cross-context correlation that K independent IBM jobs cannot reproduce. This page explains the task, the math in plain terms, and every IBM Fez result from June 2026.

Plain English

What this test is
Separability falsification — compare one coupled IBM job (transjunctions ON) against the same objectives run as independent jobs (transjunctions OFF).
Why we did it
An external AI review challenged whether “QPC” was just stacked layers or true parallel contextures. ICC is the proposed falsification: joint structure only the coupled arm can show.
What QPC computed
ICC (inter-context correlation) from raw shot counts at each context boundary — same readout pipeline on both arms; only wiring differs.
Headline result
Toy instance 3/3 pass (gaps 0.052–0.106) · Cerrado real data gap 0.088 · part of JSC levels L1–L3 with public job IDs.
What we claim — and do not
Claim: coupled runs show measurable joint structure on IBM Fez. Do not claim: ICC formula is public IP — verification is via our published job IDs and outcome tables.

Part of the Joint Structure Challenge (levels L1–L3). Level L4 (merge failure) is on the challenge hub.

The task (no jargon)

  1. Build a quantum circuit with three objective blocks (e.g. carbon, biodiversity, social).
  2. Coupled arm: connect blocks with transjunction gates → submit one job to IBM.
  3. Control arm: run each block alone (no transjunctions) → submit three jobs, then pair samples as if they were independent.
  4. Ask: does the coupled run show extra correlation between adjacent objectives that the control cannot match?

If yes → QPC is doing something quantum-joint, not “three cloud jobs + Python merge.”

This test does not ask who got the best portfolio score. Optimization is a separate benchmark.

How we score separability

We compare coupled (one IBM job, transjunctions on) vs separable control (independent jobs, samples paired as if uncoupled). A proprietary inter-context correlation (ICC) witness is computed from shot statistics on aligned decision qubits at each context boundary. Both arms use identical readout and post-processing — only coupling differs.

Public pass criteria (outcomes only)

Exact ICC formula, readout schedule, and morphogram depth are proprietary. Independent verification uses published IBM job IDs below.

Toy soft-objective instance (15Q coupled, ibm_fez)

Synthetic three-contexture instance — fixed seeds for reproducibility. 4096 shots each.

SeedICC gapCoupled ICCPassCoupled job ID
70.1060.114d8i3vdtv8cos73f549cg
110.0520.062✓ (rerun)d8i429dv8cos73f54e60
420.1040.112d8i3vu66983c73drmk10

Strong pass: 3/3 seeds.

Cerrado real data (Goiás carbon portfolio)

Same open dataset as Ribeiro 2026 (arXiv:2602.09047). Three objective contextures in one 156Q Heron layout when noted.

InstanceLayoutDepthMean ICC gapVerdictJob ID
n=8, k=3shared~5000.088Passd8i4065v8cos73f54a8g
n=12, k=4shared4740.088Passd8i42ic2upec739loi50
n=28, k=7full width18830.025Mean faild8i43g42upec739loja0
n=28, k=7full width13290.031Heron pass*d8i44rc2upec739loktg

*Heron pass = per-boundary, not mean:

Transjunction boundaryCoupled ICCSeparable ICCGapPer-boundary pass
carbon ↔ biodiversity0.0050.010−0.005
biodiversity ↔ social0.1100.0120.098
social ↔ carbon0.0030.0010.001

Honest read at full Heron width: NISQ depth washes two of three boundaries; the biodiversity↔social bridge still shows strong non-separability. More shots did not help (8192-shot gap 0.010).

What this demonstrates (value for reviewers)

Joint Structure Challenge → ← Architecture program overview Cerrado optimization compare →