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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 — scpn-quantum-control

CI License: AGPL-3.0 Python 3.10+ Qiskit 2.2+ OpenSSF Best Practices OpenSSF Scorecard Ruff mypy PyPI PyPI Downloads All-time Downloads

Quantum simulation of coupled Kuramoto oscillator networks on IBM superconducting hardware, with a hardware evidence ledger separating theory, simulator, unmitigated hardware, mitigated hardware, and noise-limited claims.

Status Snapshot — 2026-05-18

Area Public status
Package line Version 0.9.7, Python >=3.10, Qiskit >=2.2,<3.0.
Generic compiler surface scpn_quantum_control.kuramoto_core validates arbitrary K_nm/omega inputs and compiles Hamiltonians, dense matrices, Trotter circuits, order-parameter measurements, and Kuramoto variant trajectories.
Hardware evidence Promoted raw-count campaigns: ibm_kingston DLA parity Phase 1, selected Phase 2 A+G/B-C/popcount controls, and the SCPN/FIM negative/falsification result for the tested digital circuit family. Legacy ibm_fez rows require artefact-level citation.
Paper 0 source-validation register Fully promoted through the source-accounting register from P0R00001 through P0R06211; the planner reports 0 remaining work orders and 0 remaining records. This is source-bounded ingestion and fixture preservation, not external validation evidence.
Paper 0 downstream programme Paper 0 Experimental Pathway defines the longer-term experimental agenda and methodology-paper route. Paper 27 is treated as a bounded implementation candidate, not the definitive programme source.
Claim source Hardware Status Ledger.

What this package does

The classical Kuramoto model for coupled oscillators maps directly to the quantum XY spin Hamiltonian. Superconducting qubits are native simulators of this physics: each qubit is an oscillator on the Bloch sphere, and the XX+YY coupling between qubits reproduces the \(\sin(\theta_j - \theta_i)\) interaction of the Kuramoto model.

This package provides three things:

  1. A compiler that takes any coupling matrix \(K_{nm}\) and natural frequencies \(\omega_i\) and produces executable Qiskit circuits for IBM hardware.

  2. 35 research modules probing the synchronization phase transition — synchronization witnesses, topological diagnostics, chaos measures, computational complexity bounds, and open-system dynamics. ~4 are novel constructions; ~8 are first applications of existing tools to Kuramoto-XY; the rest are standard many-body diagnostics.

  3. The SCPN 16-layer network as a built-in benchmark — the coupling matrix from Paper 27 of the Sentient-Consciousness Projection Network framework, where synchronization is the mechanism by which consciousness emerges across 16 ontological layers.

  4. The Paper 0 source-validation register as a source-accounting layer — generated validation modules, spec loaders, fixtures, and tests preserve ledger-bounded Paper 0 claims under an explicit non-hardware, non-external validation boundary.

Think of it as a quantum microscope for synchronization. Classical Kuramoto tells you when oscillators lock in step. This package tells you what the quantum state looks like at the transition, how hard it is to prepare, what its topology reveals, and where classical simulation fails.

Key results

Result Value
VQE ground-state row 0.05% (4-qubit, legacy ibm_fez artifact)
16-layer UPDE snapshot 46% error at depth 770 (NISQ-consistent)
Coherence wall depth 250–400 (Heron r2)
DLA dimension formula \(2^{2N-1} - 2\) (exact, all \(N\))
Research modules 35 (≈ 5 novel, ≈ 10 first-application)
IBM hardware evidence Legacy ibm_fez artifact rows + 342-circuit ibm_kingston Phase 1 DLA-parity raw-count dataset
DLA parity asymmetry (hardware) \(+10.8\,\%\) mean for depths \(\ge 4\), peak \(+17.5\,\%\) at depth 6, reproduced from data/phase1_dla_parity/
Test suite CI-gated suite, 97%+ coverage
Python modules 215 + 1 Rust crate (47 functions) + Julia tier (accel/julia/*.jl)

Package map

Subpackage Modules Purpose
paper0 470 Source-accounting validation modules and fixtures for processed Paper 0 records
analysis 57 Synchronisation probes: witnesses, witness discovery, QFI, PH, OTOC, Krylov, magic, BKT, DLA
hardware 37 IBM Quantum runner, plugin backends registry, AsyncHardwareRunner, trapped-ion backend, GPU offload, circuit cutting, fast sparse, qubit mapper (DynQ), provenance
phase 28 Time evolution: Trotter, VQE, ADAPT-VQE, VarQITE, AVQDS, QSVT, Floquet DTC, Lindblad, Kuramoto variants
bridge 13 \(K_{nm}\) → Hamiltonian, cross-repo adapters (sc-neurocore, SSGF, orchestrator)
applications 14 FMO photosynthesis, power grid, Josephson array, EEG, ITER, quantum EVS, application benchmark plugins
mitigation 9 ZNE, PEC, dynamical decoupling, Z₂ parity, CPDR, symmetry verification, GUESS, compound
qec 9 Toric code, repetition code UPDE, surface code, biological surface code, DLA-protected memory/scar prototypes, error budget, multi-scale, syndrome flow
control 8 QAOA-MPC, VQLS Grad-Shafranov, Petri nets, ITER disruption, topological optimiser
identity 7 VQE attractor, coherence budget, entanglement witness, fingerprint
qsnn 7 Quantum spiking neural networks (LIF, STDP, synapses, dynamic coupling, training)
crypto 7 BB84, Bell tests, topology-authenticated QKD, key hierarchy
gauge 6 U(1) Wilson loops, vortex detection, CFT, universality, confinement
ssgf 5 SSGF quantum integration
benchmarks 5 Classical vs quantum scaling, MPS baseline, GPU baseline, AppQSim
psi_field 5 U(1) compact lattice gauge: lattice, infoton, observables, SCPN mapping
forecasting 2 Held-out synchronisation forecasting over hardware traces and source-backed topology replays
accel 3 Multi-language dispatcher + Julia tier (Rust → Julia → Python fallback chain)
fep 3 Friston Free Energy Principle: variational free energy, predictive coding
tcbo 2 TCBO quantum observer
pgbo 2 PGBO quantum bridge
l16 2 Layer 16 quantum director

Quick example

Any coupling topology — bring your own \(K\) and \(\omega\):

from scpn_quantum_control import QuantumKuramotoSolver, build_kuramoto_ring

K, omega = build_kuramoto_ring(6, coupling=0.5, rng_seed=42)
solver = QuantumKuramotoSolver(6, K, omega)
result = solver.run(t_max=1.0, dt=0.1, trotter_per_step=2)
print(f"R(t): {result['R']}")

Detect synchronization on hardware with witness operators:

from scpn_quantum_control.analysis.sync_witness import evaluate_all_witnesses

# After running X-basis and Y-basis circuits on IBM hardware:
results = evaluate_all_witnesses(x_counts, y_counts, n_qubits=4)
for name, w in results.items():
    print(f"{name}: {'SYNCHRONIZED' if w.is_synchronized else 'incoherent'}")

Limitations

  • NISQ benchmarking only. Circuit depths >400 hit the coherence wall on Heron r2.
  • SCPN coupling matrix is from unpublished work. The \(K_{nm}\) parameterisation comes from Paper 27 (2025 working paper, no external citations). The Kuramoto→XY mapping is standard; the specific coupling structure is not independently validated.
  • No quantum advantage at this scale. At \(N=4\)–16, classical exact diagonalisation is faster. Advantage requires \(N \gg 20\) with error-corrected qubits.
  • IBM hardware claim hygiene. The promoted raw-count dataset is Phase 1 DLA parity on ibm_kingston; legacy ibm_fez rows must cite their committed artifact path. V2/frontier/queued-job outputs are unpromoted.

Documentation

Contact: protoscience@anulum.li | GitHub Discussions | www.anulum.li

ANULUM      Fortis Studio
Developed by ANULUM / Fortis Studio

Contact: protoscience@anulum.li | GitHub Discussions | www.anulum.li

ANULUM      Fortis Studio
Developed by ANULUM / Fortis Studio