Independent comparison on IDWKKLLDAAKQIL (14-mer): polycontextural single-pass circuits on IBM Fez vs IonQ counterdiabatic quantum optimization (BF-DCQO) on trapped ions — with audited tiers (raw quantum, hybrid repair, random-seeded control).
Kipu Quantum and IonQ reported BF-DCQO for protein-inspired models on trapped-ion hardware (46–61 qubits), using multi-body HUBO encodings and a hybrid loop: repeated quantum executions plus classical consensus / repair over samples (their Fig. 4 addresses when repair alone erases the quantum signal). arXiv:2604.26861
QPC does not implement BF-DCQO. It compiles the same Miyazawa–Jernigan physics into a two-contexture circuit (21 geometry qubits + 25 contact qubits) and submits it once per mode to IBM Runtime.
For each contact pair \((i,j)\), the circuit applies NISQ transjunction gates (CX / RY / RZ — no Toffoli) between the contact qubit \(c_{i,j}\) and up to six geometry qubits along the turn chain from residue \(i\) to \(j\), after separate RZ(MJ) biases on contacts and a geometry morphogram on the backbone. Contacts are thus entangled with the local fold segment in one schedule, rather than discovered through iterative counterdiabatic rounds like BF-DCQO.
| Tier / metric | IonQ BF-DCQO (literature) | QPC Fez (8 jobs merged) | Random control (no quantum) |
|---|---|---|---|
| Tier A — best raw sample | Mean ≈ −4.19 (strong per-shot ions) | +13.5 (NISQ noise on heavy-hex) | +8.8 to +15.8 (seed-dependent) |
| Pool quality — mean energy of best 200 unique outcomes before repair | — | Dominated by rare low-energy hits (best +13.5) | ~31 (uniform random; seeds 42, 7, 2026) |
| Tier D — same repair on sample pool | Consensus ≈ −8.698 | −9.74 | −7.96 / −10.65 / −9.74 (seeds 42, 7, 2026) |
| Eref (reference fold) | −8.698 | ||
| Verifiability | Published | 8 job IDs + JSON | qpc_protein_audit_baseline.json |
Honest headline: IonQ reaches Eref with their consensus pipeline on this peptide; on Fez, hybrid repair scores near or below that model energy, but our Tier D is not the same post-processing — and random repair can score lower still (audit best −10.65). IonQ leads on raw quantum per shot (ions + iterative BF-DCQO). QPC leads on schedule compactness (one pass, factored contextures, cross-hardware portability) and documents every tier for independent audit.
Six-sequence panel (paper Table I): The IonQ/Kipu study benchmarks
six peptides (46–61 qubits). This release reports the full audited Fez campaign on
IDWKKLLDAAKQIL only. The remaining five sequences were not run on IBM hardware here
because of cloud cost (each additional peptide needs many Runtime jobs at 46–61Q depth).
Classical random-repair controls for other sequences can be reproduced locally at no QPU charge via
--audit-random-baseline; we did not publish a partial multi-sequence table without matched quantum shots.
Eref vs Tier D best (−9.74): IonQ’s arXiv:2604.26861 gives Eref = −8.698 from a converged classical genetic algorithm on the same Robert et al. tetrahedral MJ Hamiltonian — a strong benchmark, not a proof that no lower energy exists in our discrete encoding. Their conclusion calls for quantum workflows more aware of folding structure; that is a modeling goal, not a unique “golden” energy. Our Tier D score applies the same repair code to Fez bitstrings; a value below Eref can mean repair found a lower-cost state in the sampled subspace, not necessarily a new global fold better than the paper’s GA. The random audit (best Tier D −10.65 without quantum) shows classical repair alone can undershoot Eref on this instance — so sub-Eref Tier D is not by itself evidence of a better physical minimum.
qpc_protein_idwkk_fez.py --audit-random-baseline.This page compares energies on IBM Fez. QPC has already run verifiable workloads on IonQ Forte (Azure Quantum, Braket, portfolio optimization, RCS). A natural next step is the same 46Q polycontextural protein circuit on IonQ — where all-to-all coupling may improve Tier A raw scores while keeping the single-pass schedule.
The trapped-ion BF-DCQO result is a major achievement. QPC proposes a complementary question: can the same folding physics be driven by a fixed polycontextural circuit on any QPU, with energies after repair at the same ceiling — and with open job IDs? We publish the random-repair control precisely so reviewers can ask the same questions IonQ answered in Fig. 4. Joint benchmarks on IonQ using exported QPC circuits are welcome.
Sequence IDWKKLLDAAKQIL · Backend ibm_fez
→ Full results tables, all 8 job IDs, audit seeds, reproduce commands
qpc_protein_ALL_REFINED_v2.json — merged 8 jobs + Tier Dqpc_protein_audit_baseline.json — random Tier D controlqpc_protein_idwkk_fez.py · qpc_protein_build_results_html.py