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© 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 — Application benchmark plugins

Application Benchmark Plugins

Application plugins expose domain datasets through the same QPU data artifact contract used by the Kuramoto-XY pipeline. The built-in plugins cover EEG, tokamak MHD mode locking, IEEE power-grid synchronisation, and Friston-style predictive coding.

Why this page exists

This page is for teams moving from abstract synchronisation models to domain-facing benchmarks with traceable outputs. It is intended to show where raw-domain data must pass before it can become a QPUDataArtifact and which routes are bounded by plugin and reproducibility requirements.

from scpn_quantum_control.applications import (
    compile_application_problem,
    run_application_benchmark_suite,
)

results = run_application_benchmark_suite()
problem = compile_application_problem("power_grid_ieee5")

Plugin Extras

The optional extras keep domain dependencies off the default install:

Extra	Intended domain stack
app-eeg	EEG/MEG file readers and MNE pipelines.
app-plasma	HDF5/tabular tokamak or plasma diagnostics.
app-power-grid	Power-system case readers and grid toolchains.
app-fep	Structured predictive-coding workflow configuration.
app-benchmarks	All four application stacks.

The packaged benchmark JSON files do not require those extras. The extras are for users who plug in external raw archives and want the same registry path to build a QPU-ready artifact.

Metric Boundary

The EEG, plasma, and power-grid plugins emit topology_similarity_proxy, a Spearman rank coefficient between the artifact K_nm matrix and a reference coupling matrix. This metric is a structural proxy only. It is not an EEG neural-dynamics reproduction, tokamak MHD solve, power-grid swing-equation simulation, FMO transport model, or Josephson device model. Legacy benchmark result objects still expose topology_correlation as a compatibility attribute, but new plugin payloads use the proxy label.

Packaged Datasets

The in-repo artifacts live in data/public_application_benchmarks/. Each file validates as QPUDataArtifact, carries array hashes, and can be adapted to the public KuramotoProblem facade.

Dataset	Plugin	Pipeline path
eeg_alpha_plv_8ch	eeg_alpha	PLV matrix → QPU artifact → EEG topology benchmark → Kuramoto facade.
iter_mhd_8mode	plasma_iter_mhd	NTM/RWM mode graph → QPU artifact → mode-locking benchmark → Kuramoto facade.
ieee5bus_power_grid	power_grid_ieee5	IEEE 5-bus constants → QPU artifact → grid synchronisation benchmark → Kuramoto facade.
friston_fep_6node	friston_fep	Precision graph + observations → QPU artifact → variational free energy + predictive-coding step.

Third-party plugins register factories under the scpn_quantum_control.application_plugins entry-point group. A broken plugin is logged and skipped so one domain adapter cannot block the rest of the benchmark suite.

Curated Researcher Workflows

The promoted researcher workflows are deliberately small and deterministic. They are meant to demonstrate the application boundary, provenance trail, and QPU-ready artefact format without presenting compact benchmark matrices as substitutes for raw domain archives.

Workflow	Promoted artefacts	Provenance boundary	Deterministic regeneration
GraphML/CSV topology import	External user-supplied graph or edge table converted to QPUDataArtifact.	Bring-your-own topology path; the repository does not ship private third-party graph archives.	Use the application-plugin registry and validate the converted artefact before adapting it to KuramotoProblem.
EEG alpha PLV	data/public_application_benchmarks/eeg_alpha_plv_8ch.json; measured audit artefacts in data/knm_physical_validation/, including eeg_alpha_plv_knm_comparison.json.	Public-literature benchmark matrix for examples; raw EDF cohorts stay outside Git under <private-local-record>. The K_nm audit keeps PLV non-promotional because it is an association observable, not a calibrated coupling magnitude.	scripts/build_real_eeg_plv_validation_dataset.py and scripts/compare_eeg_plv_cohorts.py regenerate the cohort artefacts; scripts/run_knm_physical_validation_audit.py --measured data/knm_physical_validation/measured_couplings.json --n-layers 8 regenerates the K_nm comparison.
IEEE power grid	data/public_application_benchmarks/ieee5bus_power_grid.json; data/knm_physical_validation/measured_couplings_power_grid_ieee5bus.json; data/knm_physical_validation/measured_couplings_power_grid_ieee14bus.json; data/knm_physical_validation/power_grid_ieee14bus_knm_comparison.json.	Public IEEE 5-bus constants are converted to swing-equation coupling; public IEEE 14-bus branch reactance and voltage constants provide a larger voltage-weighted admittance control. Both remain negative/control candidates until the measured-system promotion gate passes.	scripts/build_power_grid_measured_couplings.py regenerates the 5-bus artefact; add --case ieee14 for the 14-bus admittance candidate; run scripts/run_knm_physical_validation_audit.py --measured ... --n-layers 14 for the comparison.
Plasma/tokamak	data/public_application_benchmarks/iter_mhd_8mode.json.	Curated ITER-scale mode-locking topology from public MHD literature, not raw discharge traces.	run_application_benchmark_suite() includes the packaged plasma benchmark without optional HDF5 dependencies.
Notebook and example workflows	examples/02_kuramoto_xy_demo.py, examples/05_vqe_ansatz_comparison.py, examples/09_classical_vs_quantum_benchmark.py, examples/13_iter_disruption_demo.py, examples/18_end_to_end_pipeline.py, examples/19_sync_witness_operator.py, and examples/20_quantum_persistent_homology.py.	Notebooks remain support wrappers; reusable logic stays in src/, scripts/, and versioned example files.	Static example tests ensure promoted examples remain parseable, expose main(), and are listed in examples/README.md.

For a no-credential smoke path, run:

.venv-linux/bin/python - <<'PY'
from scpn_quantum_control.applications import run_application_benchmark_suite

results = run_application_benchmark_suite()
print(sorted(results))
PY

This command loads the packaged JSON artefacts only. It does not submit IBM jobs, download raw EEG data, or touch private datasets.