qig-warp 0.6.3

Physics-based navigation for expensive computation — screening, cost prediction, convergence stopping

qig-warp

Physics-based navigation for expensive computation — screening, cost prediction, convergence stopping.

Any expensive computation has three questions: What can I skip? What will it cost? When should I stop? qig-warp answers all three from a small pilot, before the main computation runs.

The problem

You have an expensive function to evaluate across many parameters. Running everything takes hours. But most of the computation doesn't contribute to your answer — perturbations decay exponentially, cost scales predictably, and estimates converge long before you finish.

The solution

from qig_warp import WarpBubble

# Self-calibrating: discovers structure from 5 pilot probes
bubble = WarpBubble.auto()
result = bubble.navigate(fn=my_expensive_function, params=param_list, budget_s=3600)

# Result: computed 12/20 params in 30 min instead of 60 min
# Skipped the expensive ones that wouldn't change the answer
# Predicted values for skipped sites from decay profile

Three operations

Operation	Question	How it works
Screening	What can I skip?	Perturbation response decays exponentially. Sites beyond the decay length don't matter.
Bridge	What will it cost?	Cost scales as a power law with the control parameter. Knowing the exponent predicts runtime.
Convergence	When should I stop?	Successive estimates converge exponentially. The decay rate tells you when more computation is waste.

Four modes

bubble = WarpBubble.auto()                      # discovers constants from pilot probes
bubble = WarpBubble.qig_regime(h=3.0, J=1.0)    # regime-aware (physics calibrated)
bubble = WarpBubble.qig_frozen()                 # single calibration (v0.3 compatible)
bubble = WarpBubble.general(screening_length=0.5, bridge_exponent=0.8)  # user-specified

Use cases

Molecular simulation: Interatomic potentials decay with distance (screening). System-size cost scales as N² or N·log(N) (bridge). Energy minimization converges (convergence).

Drug discovery: Binding sites are local — only nearby residues matter (screening). Conformational search cost scales with flexibility (bridge). Docking scores stabilize (convergence).

ML hyperparameter search: Learning rate perturbations have limited range (screening). Training cost scales with model/data size (bridge). Loss curves flatten (convergence).

Climate ensemble forecasting: Weather patterns have finite spatial correlation (screening). Resolution scaling is predictable (bridge). Ensemble convergence tells you when to stop adding members (convergence).

Materials science: Grain boundary physics concentrates at the interface (screening). Simulation cost scales with supercell size (bridge). Elastic constants converge (convergence).

Performance

Validated on quantum physics lattice experiments (L=3 through L=6):

• Screening: 36% site reduction with <2.1% error
• Cost prediction: matched actual runtime to R²=0.999
• Bridge: predicted J-sweep cost within 5% across 7 coupling values

On a molecular dynamics benchmark:

• Auto-discovery found cost exponent within 0.5% of truth from 5 probes
• Budget-constrained: 12/15 evaluations, 29% time savings

Install

pip install qig-warp

Contact

Built by Braden Lang. For partnerships and research collaboration: braden.com.au