Workloads where QPC reports the same objective number as an external reference—published paper, classical baseline, or inequality bound—with auditable IBM (or other) job IDs.
D-Wave “Proof of Quantum Work” blockchain (Leap demo) is not on this list: it measures quantum-hash consensus on annealers, not folding energy, QUBO cost, or ICC. Different subject—results are not comparable rows.
Published industry-style business tasks replayed on IBM Heron gate QPUs with QPC polycontextural architecture. Same metrics as external studies; auditable job IDs.
| Status | Task | Metric | QPC result | Report |
|---|---|---|---|---|
| ✓ Live | Ribeiro 2026 — Cerrado carbon portfolio (arXiv:2602.09047) | Portfolio score | Decoder 6.27 vs greedy 5.53 · ibm_fez ZNE | Overview · Technical |
| ✓ Live | Volkswagen 2017 — Beijing traffic flow (arXiv:1708.01625) | Congested roads ↓ | QPC 2 = greedy · vs random 7.5 · job d8gq6m42upec739k6pig |
Report · Task |
| ● Next | D-Wave Mendeley 45 QUBO instances (MaxCut / TSP / Knapsack) | QUBO energy vs published NL-Hybrid | Planned — same Hamiltonian on IBM | Catalog A2 |
| ● Next | D-Wave Taillard flow-shop scheduling vignette | Optimality gap vs best known | Planned — supply-chain narrative | Catalog B1 |
| ● Next | LR-QAOA weighted MaxCut (cross-vendor IBM study) | Approximation ratio r | Planned — multi-context on Heron | Catalog A1 |
| # | External baseline | Metric | QPC | Report |
|---|---|---|---|---|
| 1 | IonQ / Kipu BF-DCQO (mastoparan I) | Energy after repair (↓ better) | −9.74 vs ref −8.70 | Protein |
| 2 | Controlled K=1 | Held-out fit | K=4 > K=1 (same resources) | QQ pilot |
| 3 | Classical KS bound | Witness vs bound | Heron Fez + mitigation | KS |
| 4 | Single-context baseline | ICC, FSP | Multi-context PFQM | PFQM |
| 5 | Classical per-context optimum (same grid) | QAOA objective J | gic2026/qpc_case3_energy_DOE_siting_40q.py |
GIC Case 3 JSON |
| 6 | IBM console audit | Job IDs, tiers T1–T4 | 4→24 contexts | Open instance |
● Transjunction ON vs OFF on one T03/T04 cluster: same shots and width; publish ICC (or agreed observable) + both job IDs. Same logic as QQ pilot—only coupling changes.
qaoa_best_objective_during_search, qaoa_objective_on_final_noiseless_histogram, classical baseline in output JSON.qpc_supply_chain_optimization_65q.py → add fixed QUBO cost + classical baseline before treating as external comparable.docs/QPC_COMPARABLE_BENCHMARKS_SHEET.mdSuggested standard add-ons: Qiskit MaxCut (approximation ratio), small Gset QUBO vs tabulated best—same metric as literature, 1-context vs N-context on IBM.