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import pytest
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import torch
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import numpy as np
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from quantum_peer_model import QuantumPeerModel
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from quantum_circuit import QuantumCircuit
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from quantum_topology import ChernSimonsTopology
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from quantum_gates import QuantumGate
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@pytest.fixture
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def model():
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return QuantumPeerModel(device="cpu")
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@pytest.fixture
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def quantum_circuit():
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topology = ChernSimonsTopology(depth=3)
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return QuantumCircuit(topology)
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def test_circuit_initialization(quantum_circuit):
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"""Test quantum circuit initialization"""
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assert quantum_circuit is not None
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assert len(quantum_circuit.gates) > 0
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gate_types = {gate.gate_type for gate in quantum_circuit.gates}
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required_gates = {"H", "CNOT", "Phase", "X", "Z"}
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assert required_gates.issubset(gate_types)
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def test_quantum_evolution(quantum_circuit):
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"""Test quantum state evolution"""
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initial_state = np.zeros(quantum_circuit.topology.dimension)
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initial_state[0] = 1
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final_state = quantum_circuit.evolve(initial_state)
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assert isinstance(final_state, np.ndarray)
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assert np.allclose(np.sum(np.abs(final_state) ** 2), 1)
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def test_gate_operations():
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"""Test individual quantum gates"""
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gates = {
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"H": QuantumGate("H"),
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"X": QuantumGate("X"),
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"Z": QuantumGate("Z"),
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"Phase": QuantumGate("Phase"),
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"CNOT": QuantumGate("CNOT")
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}
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state = np.array([1, 0])
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h_state = gates["H"].apply(state, None)
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expected = np.array([1, 1]) / np.sqrt(2)
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assert np.allclose(h_state, expected)
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x_state = gates["X"].apply(state, None)
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assert np.allclose(x_state, np.array([0, 1]))
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
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def test_model_gpu_support():
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"""Test GPU support when available"""
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model = QuantumPeerModel(device="cuda")
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assert model.llm_interface.model.device.type == "cuda"
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response = model.generate("Test prompt")
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assert isinstance(response, str)
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def test_topology_scaling():
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"""Test topology scaling with different depths"""
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depths = [2, 3, 4]
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for depth in depths:
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topology = ChernSimonsTopology(depth)
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assert topology.dimension == 2 ** depth
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assert len(topology.connections) == (depth * (depth - 1)) // 2
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def test_model_generation(model):
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"""Test text generation with quantum enhancement"""
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prompt = "Explain quantum computing in"
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response = model.generate(
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prompt,
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max_length=50,
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quantum_params={"gates": ["H", "CNOT"]}
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)
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assert isinstance(response, str)
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assert len(response) > len(prompt) |
