6afdb4b148
Lucidia Core - AI reasoning engines for specialized domains: - Physicist (867 lines) - energy modeling, force calculations - Mathematician (760 lines) - symbolic computation, proofs - Geologist (654 lines) - terrain modeling, stratigraphy - Engineer (599 lines) - structural analysis, optimization - Painter (583 lines) - visual generation, graphics - Chemist (569 lines) - molecular analysis, reactions - Analyst (505 lines) - pattern recognition, insights - Plus: architect, researcher, mediator, speaker, poet, navigator Features: - FastAPI wrapper with REST endpoints for each agent - CLI with `lucidia list`, `lucidia run`, `lucidia api` - Codex YAML configurations for agent personalities - Quantum engine extensions 12,512 lines of Python across 91 files. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
"""Basic tests for the Quantum ML module."""
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from __future__ import annotations
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import importlib
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import pytest
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pytest.importorskip("torch")
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pytest.importorskip("qiskit")
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try:
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pytest.importorskip("qiskit_machine_learning")
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except ValueError as exc: # pragma: no cover - optional dependency mismatch
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pytest.skip(f"qiskit_machine_learning unavailable: {exc}")
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np = pytest.importorskip("numpy", reason="Install numpy or ask codex for help")
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pytest.importorskip("torch", reason="Install torch or ask codex for help")
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pytest.importorskip("qiskit", reason="Install qiskit or ask codex for help")
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pytest.importorskip("qiskit_machine_learning", reason="Install qiskit_machine_learning or ask codex for help")
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import torch
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from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
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import lucidia.quantum as qml
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from lucidia.quantum.kernels import fit_qsvc
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try:
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from lucidia.quantum.qnn import build_sampler_qnn
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from lucidia.quantum.torch_bridge import QModule
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except Exception as exc: # pragma: no cover - optional dependency path
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pytest.skip(
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f"quantum neural network components unavailable: {exc}",
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allow_module_level=True,
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)
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def test_feature_flag_off(monkeypatch):
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monkeypatch.setenv("LUCIDIA_QML", "off")
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importlib.reload(qml)
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assert not qml.is_enabled()
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with pytest.raises(RuntimeError):
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qml.get_backend()
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def test_sampler_qnn_gradients(monkeypatch):
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monkeypatch.setenv("LUCIDIA_QML", "on")
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importlib.reload(qml)
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feature_map = ZZFeatureMap(2)
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ansatz = RealAmplitudes(2, reps=1)
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qnn = build_sampler_qnn(feature_map, ansatz, input_size=2, weight_size=ansatz.num_parameters, num_classes=2)
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module = QModule(qnn, seed=1)
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x = torch.zeros((1, 2), requires_grad=True)
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out = module(x)
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out.backward(torch.ones_like(out))
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assert torch.all(torch.isfinite(x.grad))
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def test_qsvc_training(monkeypatch):
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monkeypatch.setenv("LUCIDIA_QML", "on")
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importlib.reload(qml)
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x = np.array([[0, 0], [1, 1]])
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y = np.array([0, 1])
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model = fit_qsvc(x, y)
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preds = model.predict(x)
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assert preds.shape == (2,)
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