SPDX-License-Identifier: AGPL-3.0-or-later¶
Commercial license available¶
© Concepts 1996–2026 Miroslav Šotek. All rights reserved.¶
© Code 2020–2026 Miroslav Šotek. All rights reserved.¶
ORCID: 0009-0009-3560-0851¶
Contact: www.anulum.li | protoscience@anulum.li¶
scpn-quantum-control — Experiment Roadmap: March-June 2026¶
Experiment Roadmap: March-June 2026¶
Backend: ibm_fez (Heron r2, 156 qubits) Budget: 10 min QPU / month (free tier) Repository: scpn-quantum-control v0.9.0+
Update (March 17, 2026): 9 v1.0 modules implemented (PEC, trapped-ion, ITER disruption, quantum advantage, SNN/SSGF adapters, identity binding, QSNN training, fault-tolerant UPDE). All 20 experiments validated on AerSimulator. Hardware runs pending IBM Quantum API token.
Update (March 28, 2026): All 20 experiments completed on ibm_fez hardware.
22 jobs, 176,000+ shots. Results in results/ibm_hardware_2026-03-28/.
March and April roadmap experiments are now COMPLETED, not planned.
Experiment Inventory (20 total)¶
| # | Experiment | Qubits | Est. QPU | Month | Science Question |
|---|---|---|---|---|---|
| 1 | noise_baseline |
4 | 10s | Mar | Calibration drift Feb→Mar |
| 2 | kuramoto_4osc_zne [1,3,5] |
4 | 90s | Mar | Raw vs mitigated (linear ZNE) |
| 3 | kuramoto_8osc_zne [1,3,5] |
8 | 120s | Mar | Extend mitigation to depth-233 |
| 4 | vqe_8q_hardware |
8 | 60s | Mar | Scale VQE from 4→8 qubits |
| 5 | upde_16_dd |
16 | 60s | Mar | DD on full 16-layer system |
| 6 | kuramoto_4osc_trotter2 |
4 | 30s | Mar | Order-2 vs order-1 Trotter |
| 7 | sync_threshold |
4 | 50s | Apr | Kuramoto phase transition |
| 8 | ansatz_comparison_hw |
4 | 90s | Apr | Prove Knm ansatz wins on hardware |
| 9 | zne_higher_order [1..9] |
4 | 120s | Apr | Optimal extrapolation order |
| 10 | decoherence_scaling |
2-12 | 120s | Apr | Extract per-gate decay rate γ |
| 11 | vqe_landscape |
4 | 0s | Apr | Barren plateau detection (sim only) |
| 12 | kuramoto_4osc |
4 | 30s | — | Baseline (Feb, done) |
| 13 | kuramoto_8osc |
8 | 60s | — | 8-osc trajectory (Feb, done) |
| 14 | vqe_4q |
4 | 30s | — | VQE ground state (Feb, done) |
| 15 | vqe_8q |
8 | 0s | — | Statevector only (Feb, done) |
| 16 | qaoa_mpc_4 |
4 | 20s | — | Binary MPC (Feb, done) |
| 17 | upde_16_snapshot |
16 | 180s | — | 16-layer snapshot (Feb, done) |
| 18 | bell_test_4q |
4 | ~20s | Mar | CHSH violation certifies K_nm entanglement |
| 19 | correlator_4q |
4 | ~25s | Mar | ZZ cross-correlation validates K_ij topology |
| 20 | qkd_qber_4q |
4 | ~15s | Mar | QBER from hardware vs BB84 threshold |
March 2026 Plan (~370s QPU)¶
Priority experiments¶
1. noise_baseline (10s) — Repeat Feb depth-5 circuit. Detects backend drift. Compare hw_R to Feb value (0.8054). If drift > 2%, flag all subsequent results for calibration correction.
2. kuramoto_4osc_zne [1,3,5] (90s) — Gate-fold at 3 noise scales, Richardson linear extrapolation. Feb baseline: 7.3% error at depth 85. Expected: ZNE reduces to ~3-4%. Produces the "raw vs mitigated" figure for the paper.
3. kuramoto_8osc_zne [1,3,5] (120s) — Same ZNE protocol at 8 qubits. Feb: 20% error at depth 233. Expected: ZNE reduces to ~10-12%. Key question: does ZNE maintain effectiveness at higher depth?
4. vqe_8q_hardware (60s) — Statevector VQE optimization followed by single-shot hardware energy evaluation. Feb 4q result: 0.05% error. Expected 8q: 0.1-0.5% error (deeper ansatz, more CZ gates). Establishes scaling trend for VQE accuracy.
5. upde_16_dd (60s) — XY4 dynamical decoupling on all 16-qubit UPDE basis circuits. Compares R(DD) vs R(no-DD) vs classical. Tests whether idle-qubit DD helps at depth ~770 where decoherence dominates.
6. kuramoto_4osc_trotter2 (30s) — Suzuki-Trotter order 2 at identical dt/steps as order 1. Direct comparison: does higher Trotter order gain accuracy faster than decoherence penalty from deeper circuits?
Total: ~370s. Buffer: ~230s for reruns or calibration.
April 2026 Plan (~380s QPU)¶
Priority experiments¶
7. sync_threshold (50s) — Sweep K_base ∈ {0.05, 0.15, 0.30, 0.45, 0.60, 0.80} at 4 qubits. Each value = 3 circuits (Z/X/Y) at dt=0.1. Maps the Kuramoto synchronization bifurcation on quantum hardware: below critical coupling K_c, R stays low; above K_c, R jumps to order ~0.5+.
Science value: first quantum measurement of the Kuramoto phase transition. Validates quantum XY ↔ classical Kuramoto correspondence at the critical point. Publication-quality standalone result.
8. ansatz_comparison_hw (90s) — Three VQE ansatze (Knm-informed, TwoLocal, EfficientSU2) optimized on Statevector, then evaluated on hardware via Estimator. Compares hw_energy for each.
Science value: Feb sim-only benchmark showed Knm-informed converges fastest. This proves the advantage survives real hardware noise. Physics-informed circuit design is a hot topic — demonstrating it on real hardware is publishable in PRX Quantum or similar.
9. zne_higher_order [1,3,5,7,9] (120s) — 5-point ZNE with polynomial orders 1 (linear) and 2 (quadratic). Tests whether higher-order extrapolation recovers more signal or overfits.
Science value: systematic ZNE study on the same circuit family. Determines optimal extrapolation strategy for XY evolution on Heron r2. Contributes to the error mitigation literature.
10. decoherence_scaling (120s) — Run 1-Trotter-step evolution at 2, 4, 6, 8, 10, 12 qubits. Records depth and R for each. Fits R_hw = R_exact * exp(-γ * depth) to extract per-gate depolarization rate γ.
Science value: γ is a single number that characterizes the backend for our circuit family. Enables predictive modeling: "at depth D, expect error E." Compares to IBM published T1/T2 and gate errors.
11. vqe_landscape (0s QPU, sim only) — Sample 50 random parameter vectors for each ansatz, compute energy variance. Low variance = barren plateau. Tests whether Knm-informed ansatz avoids barren plateaus.
Science value: barren plateaus are the #1 obstacle to VQE scaling. Showing Knm-informed ansatz has higher variance (trainable landscape) is publishable. Reference: McClean et al., Nature Comm. 9, 4812 (2018).
Total: ~380s. Buffer: ~220s.
May 2026 Plan (Candidates)¶
Experiment ideas (not yet implemented)¶
A. Layer-selective qubit assignment — On Heron r2, assign strongly-coupled SCPN layers (L3, L4, L10) to the lowest-error physical qubits. Compare UPDE-16 R vs default Qiskit layout.
Budget: ~120s QPU. Requires reading backend calibration data
(backend.properties()) to rank qubits by error rate.
B. Readout error mitigation (M3) — Build measurement calibration matrix from all-0 and all-1 circuits, apply inverse to 4-osc and 8-osc counts. Separates readout error from gate error.
Budget: ~30s QPU (calibration circuits) + reprocessing of existing data.
C. Entanglement entropy measurement — Compute von Neumann entropy S(ρ_q) of per-qubit reduced density matrices during Kuramoto evolution. Requires randomized measurements (shadow tomography) or direct state tomography on ≤4 qubits.
Budget: ~180s QPU for 4-qubit partial tomography at 3 time steps.
D. QAOA with ZZ coupling terms — Extend binary MPC cost Hamiltonian to include inter-timestep correlations (ZZ terms). Tests whether richer cost structure improves QAOA control quality.
Budget: ~60s QPU. Requires extending QAOA_MPC.build_cost_hamiltonian().
E. Depth-optimal circuit decomposition — Rewrite Kuramoto evolution using native Heron r2 CZ+RZ+SX gate set directly (skip Trotter synthesis). Hand-optimized 4-qubit circuit should be 40-60% shallower.
Budget: ~30s QPU. High implementation effort but large depth savings.
June 2026 Plan (Candidates)¶
F. Quantum phase estimation — QPE on the XY Hamiltonian to extract eigenvalues. 4 qubits + 3 ancilla. Tests whether QPE is practical on NISQ for our Hamiltonian.
G. Variational quantum simulation — VQS (Li-Benjamin algorithm) as alternative to Trotter. Uses ansatz evolution instead of product formulas. Should be shallower for same accuracy.
H. Multi-circuit quantum error correction — Run toric code d=3 with MWPM decoder using Knm-weighted distances. Tests whether physics-aware decoding reduces logical error rate.
Paper Claims Strengthened by This Roadmap¶
| Claim | Feb Data | March+ Extension |
|---|---|---|
| VQE 0.05% | 4q | 8q (scaling proof) |
| Decoherence curve | 12 pts, qualitative | Fit γ, R² (quantitative) |
| 16-layer UPDE | 46% error | DD + ZNE (reduced error) |
| Trotter tradeoff | Order 1 only | Order 1 vs 2 comparison |
| Error mitigation | None | ZNE linear + quadratic |
| Ansatz design | Sim only | Hardware proof |
| Phase transition | Not measured | Bifurcation curve |
| Barren plateaus | Not measured | Landscape variance |
Hardware Notes¶
- Backend: ibm_fez, Heron r2, 156 qubits
- Native gates: CZ, ID, RZ, SX, X
- Median CZ error: ~0.5%
- T1/T2: ~300/200 μs
- Coherence wall: depth 250-400 (Feb calibration)
- Budget: 10 min QPU/month (free tier)
- Scheduling: first-come-first-served, typical queue 30s-5min