Auditable Multi-Context Quantum Orchestration on IBM Heron

Protein Comparison, Controlled K-Ablations, and Open-Instance Scale Evidence

Dr. Ilan Kreitmann
Quantum Polycontextural Computing (QPC)
readytogo@quantumpolycontextural.ai · quantumpolycontextural.ai
June 2026

Abstract. Quantum Polycontextural Computing (QPC) is a hardware-agnostic orchestration layer that maps multiple labelled problem contexts onto one gate-model execution on commercial superconducting QPUs. We report three auditable hardware studies on IBM Heron (ibm_fez, ibm_marrakesh) under the IBM Open plan, each with published job identifiers and JSON artifacts: (i) a 46-qubit mastoparan-I folding pilot compared to a published trapped-ion BF-DCQO energy reference (−8.70); QPC Tier-D repair reaches −9.74 in one pass with eight recoverable job IDs; (ii) a controlled K-ablation on 16 qubits where polycontextural K=4 fits held-out Wang–Busemeyer joint statistics better than a matched K=1 control (bootstrap one-sided p<0.0005 on total-variation and KL); (iii) thirteen Runtime jobs demonstrating co-resident multi-context layouts from 4×39Q through 24 contexts in six cluster submissions. A supplementary transjunction ON vs OFF ablation at 3×26Q shows bridge-local inter-context correlators increase by |Δ|>0.05 when ring coupling is enabled, after QPC readout mitigation on bridge qubits only; full-width correlators do not separate conditions. We state limitations explicitly: QPC is not a new QPU; protein and cognition results are selective comparisons, not universal advantage claims; open-instance entropy alone does not signature the architecture.

Keywords: multi-context orchestration, IBM Quantum, auditable benchmarks, transjunction coupling, quantum chemistry pilot

1. Introduction

Enterprise and research workloads often require several scenarios at once—risk views, cost and carbon constraints, supply-chain hypotheses, or multiple experimental conditions. Mainstream gate-model stacks typically serialize these as separate circuits or flatten them into a single Boolean narrative per run. Quantum Polycontextural Computing (QPC) is an orchestration and logical-architecture layer that keeps multiple formal contexts co-resident on one chip width where the layout permits, coupled by transjunction gates when the workload requires cross-context structure.

This note is deliberately narrow: an 8–12 page hardware evidence summary with honest scope, not a complete theory monograph nor a claim of a new quantum processor. Every headline result below is tied to (a) a defined metric, (b) a stated baseline or control, and (c) verifiable IBM Quantum Runtime job IDs.

What we claim.

What we do not claim.

2. Methods

2.1 Context layouts and transjunctions

A context is a labelled register block on the device graph with its own morphogrammatic evolution. Transjunctions are two-qubit bridges linking aligned qubits across context boundaries. The orchestrator emits a single QASM submission per cluster job when contexts are co-resident.

2.2 Software stack

Runs use Qiskit Runtime SamplerV2 on IBM Open (open-instance). A QPC noise reducer is enabled where documented. Artifacts use versioned JSON schemas (qpc_benchmark_v1, qpc_coupling_ablation_v2, protein and QQ archives).

3. Experiment I — Mastoparan I vs published BF-DCQO

Sequence IDWKKLLDAAKQIL (mastoparan I) on 46 qubits on ibm_fez. Published reference Eref = −8.698 (IonQ/Kipu BF-DCQO, arXiv:2604.26861). Eight Runtime jobs merged in qpc_protein_ALL_REFINED_v2.json.

TierBest energyRole
A — raw+13.5Direct pool
B — geometry filter+13.5Constraints
C — light consensus−0.32Partial repair
D — repair pool−9.74Primary headline
E — refined consensus+16.48Alternate path
Eref (published)−8.70External ceiling

Table 1. Mastoparan I energy tiers (IBM Fez).

Full report: QPC_PROTEIN_IDWKK_FEZ.html

4. Experiment II — QPC-QQ K-ablation

16 qubits, K=1 vs K=4, Wang–Busemeyer Clinton–Gore joints. Parameters from order-blind marginals only; joints held out for evaluation. 18 SamplerV2 jobs on ibm_fez, 4096 shots.

KTVABTVBATVmean
1 (control)0.28150.32160.3015
20.26720.24910.2582
4 (polycontextural)0.24790.25410.2510

Table 2. Hardware K-ablation (May 2026). Bootstrap n=2000: mean TV(K=1)−TV(K=4)=+0.0505, 95% CI [+0.0359, +0.0655], one-sided p=0.

Report: QPC_QQ_PILOT_REPORT.html

5. Experiment III — Open-instance scale

Thirteen Runtime jobs (512 shots, May 2026): T01 4×39Q (d8atngdmdd1s73b8p4fg), T02 12×13Q, T03–T04 clusters on Marrakesh, T05 24 contexts in six jobs. Establishes orchestration reach and audit trails—not, alone, architectural discrimination.

Report: QPC_OPEN_INSTANCE_BENCHMARKS.html

6. Supplement — Coupling ON vs OFF

Matched 3×26Q = 78Q, 1024 shots. Bridge-local ICC with readout mitigation:

BackendCoupled jobΔ full-width ICCΔ bridge ICC (mit.)
ibm_fezd8fh39ralsvc7391oe30−0.0033+0.0640
ibm_marrakeshd8fi333alsvc7391pvag−0.0048+0.0690

Report: QPC_COUPLING_ABLATION.html

7. Discussion and data availability

Job IDs and JSON schemas are the reproducibility contract. Overview: qpc_highlights_report.html. Scripts in site release: qpc_protein_idwkk_fez.py, scripts/run_qpc_coupling_ablation.py.

References

  1. Wang & Busemeyer, J. Math. Psychol. 50 (2006) — quantum cognition dataset.
  2. IonQ/Kipu BF-DCQO, arXiv:2604.26861 (2026) — mastoparan Eref.
  3. QPC public reports, quantumpolycontextural.ai (2026).
  4. IBM Quantum Heron, quantum.ibm.com.

To submit on arXiv: compile arxiv/qpc_hardware_note.tex → PDF, or print this page (File → Print → Save as PDF). See docs/QPC_ARXIV_SUBMISSION_CHECKLIST.md.