chore: initial import — torch-vqc v0.1.0
Extracted from micro-kiki for open-source release. Pure-torch VQC matching
PennyLane default.qubit at 1e-5 numerical precision, with autograd training
and batched inference.
Contents:
src/torch_vqc/
circuit.py — torch_vqc_forward (6-qubit StronglyEntanglingLayers)
router.py — TorchVQCRouter nn.Module + optional learned projection
__init__.py — public API exports
tests/ (10 tests, all passing)
test_circuit.py — forward match vs PennyLane, single + batched
test_training.py — loss decrease, accuracy, gradient flow, 20x speedup
test_projection.py — learned projection rescues hard tasks, shape checks
conftest.py — sys.path shim for tests without pip install -e
pyproject.toml — Apache-2.0, Python 3.10+, deps: torch+numpy, test: pennylane
README.md — quick start, benchmarks, conventions, citation
LICENSE — Apache 2.0 full text
docs/findings.md — scientific background (from micro-kiki Plan 6)
Measured speedup on 6-qubit 6-layer circuit with 120 training samples × 3 epochs:
PennyLane parameter-shift: ~99 s
torch-vqc autograd: ~30 ms
= ~3000x speedup.
L'électron rare
5eea6bcc5b