L'électron rare
5eea6bcc5b
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.
52 lines
1.5 KiB
TOML
52 lines
1.5 KiB
TOML
[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[project]
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name = "torch-vqc"
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version = "0.1.0"
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description = "Pure-torch variational quantum circuit (VQC) with autograd training — ~3000× faster than PennyLane parameter-shift on 6-qubit StronglyEntanglingLayers."
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readme = "README.md"
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requires-python = ">=3.10"
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license = { text = "Apache-2.0" }
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authors = [{ name = "L'électron rare" }]
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keywords = ["quantum-machine-learning", "vqc", "pytorch", "autograd", "pennylane", "variational-quantum-circuit"]
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classifiers = [
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"Development Status :: 3 - Alpha",
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"Intended Audience :: Science/Research",
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"License :: OSI Approved :: Apache Software License",
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.10",
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"Programming Language :: Python :: 3.11",
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"Programming Language :: Python :: 3.12",
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"Programming Language :: Python :: 3.13",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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"Topic :: Scientific/Engineering :: Physics",
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]
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dependencies = [
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"torch>=2.0",
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"numpy>=1.24",
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]
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[project.optional-dependencies]
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test = [
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"pytest>=7.0",
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"pennylane>=0.40",
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]
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dev = [
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"pytest>=7.0",
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"pennylane>=0.40",
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"ruff>=0.4",
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]
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[project.urls]
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Repository = "https://github.com/electron-rare/torch-vqc"
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Issues = "https://github.com/electron-rare/torch-vqc/issues"
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[tool.hatch.build.targets.wheel]
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packages = ["src/torch_vqc"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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addopts = "-v"
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