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 — Kuramoto competitive benchmark¶
Kuramoto Competitive Benchmark¶
This page documents the external competitive harness that measures our Kuramoto toolkit against real third-party solvers on one deterministic Kuramoto forward problem. It is the cross-package counterpart to the in-repository Kuramoto Tier Benchmark: the tier benchmark compares our own Rust/Julia/Python backends against each other, while this harness compares our toolkit against independent external libraries.
The harness lives in
scpn_quantum_control.benchmarks.kuramoto_competitive_benchmark; the runner is
scripts/bench_kuramoto_competitive.py; the committed artefact is
docs/benchmarks/kuramoto_competitive.json.
What is compared¶
All solvers integrate the identical networked Kuramoto field
on one deterministic, seed-built problem, and report the final order parameter
r, its absolute error against the high-precision reference, and the wall-clock
time.
| Method | Family | Backend | Status |
|---|---|---|---|
ours_rk4 |
ours | kuramoto.kuramoto_rk4_trajectory (fixed step) |
live |
ours_dopri |
ours | kuramoto.kuramoto_dopri_trajectory (adaptive DOPRI5) |
live |
scipy_solve_ivp |
external | SciPy solve_ivp(RK45), tight tolerances |
live (reference) |
julia_diffeq |
external | Julia DifferentialEquations.jl(Tsit5) |
live |
networkdynamics_jl |
external | NetworkDynamics.jl | declared target |
dynamicalsystems_jl |
external | DynamicalSystems.jl | declared target |
scimlsensitivity_jl |
external | SciMLSensitivity.jl (differentiable competitor) | declared target |
jitcdde |
external | jitcdde (just-in-time C) | declared target |
The reference is the SciPy high-precision run (rtol=1e-10, atol=1e-12) when
SciPy is installed, otherwise our adaptive DOPRI5.
Fail-closed contract¶
Every external competitor is probed before it is run. A solver that is not
installed — or whose subprocess errors or times out — yields an
available=False row that carries the documented install command and the
reason, never a fabricated number. The four declared targets above are recorded
as such on a host without them, so the artefact is complete and reproducible
everywhere and the rows flip to live once the package is added.
Measured comparison (committed artefact)¶
The committed artefact was generated on the development workstation (11th-gen
Intel Core i5-11600K). Correctness errors are host-independent; the timings were
captured on a non-isolated, heavily loaded host (powersave governor, load
average ≈ 14.7), so per the claim boundary below they are functional and
reproducibility evidence, not an isolated performance claim. For clean
timing, run the runner on a quiesced, core-reserved host.
Problem: n = 12, t_max = 6.0, dt = 0.01, seed 20260628; reference
scipy_solve_ivp.
| Method | Available | r_final |
Error vs reference | Time (ms) |
|---|---|---|---|---|
ours_rk4 |
yes | 0.77562419 | 2.96e-11 | 3.036 |
ours_dopri |
yes | 0.77562558 | 1.39e-06 | 2.326 |
scipy_solve_ivp |
yes | 0.77562419 | — (reference) | 19.627 |
julia_diffeq |
yes | 0.77562419 | 1.76e-09 | 8.689 |
networkdynamics_jl |
no | — | — | — |
dynamicalsystems_jl |
no | — | — | — |
scimlsensitivity_jl |
no | — | — | — |
jitcdde |
no | — | — | — |
Reading: our integrators, SciPy solve_ivp, and Julia
DifferentialEquations.jl agree on the final order parameter to between
3e-11 and 2e-9 — three independent implementations corroborate the result,
which is the primary, host-independent claim. On this loaded host our adaptive
DOPRI5 was also the fastest available solver, but that ordering must be
re-measured on an isolated host before it is quoted as a performance result.
Reproduce¶
python scripts/bench_kuramoto_competitive.py # default n=12 problem
python scripts/bench_kuramoto_competitive.py --n 32 --t-max 10
To bring a declared target live, install it (the artefact prints the exact
command, e.g. julia -e 'using Pkg; Pkg.add("NetworkDynamics")') and re-run.
Claim boundary¶
- The order-parameter values and their cross-implementation agreement are the reproducible, host-independent quantities.
- Timings are functional and reproducibility evidence on the recorded host, not a production-latency, SLA, or universal-hardware claim. Competitor package versions and numerical tolerances are recorded in the artefact.
- The verdict reports honestly where a competitor is faster than our toolkit; absent competitors do not contribute to it.