🧪 Testing QuantumLangChain¶

# conftest.py
import pytest
from quantum_langchain.testing import QuantumTestHarness

@pytest.fixture
def quantum_simulator():
    """Quantum simulator fixture."""
    return QuantumTestHarness.create_simulator()

@pytest.fixture  
def mock_quantum_backend():
    """Mock quantum backend for fast tests."""
    return QuantumTestHarness.create_mock_backend()

# tests/unit/test_qlchain.py
import pytest
from quantum_langchain import QLChain

class TestQLChain:
    def test_initialization(self):
        """Test QLChain initialization."""
        chain = QLChain()
        assert chain.is_initialized

    def test_classical_processing(self):
        """Test classical LLM integration."""
        chain = QLChain(llm="mock")
        result = chain.invoke("test query")
        assert result is not None

# tests/unit/test_quantum_memory.py
import pytest
from quantum_langchain.memory import QuantumMemory

class TestQuantumMemory:
    def test_quantum_state_creation(self, quantum_simulator):
        """Test quantum state initialization."""
        memory = QuantumMemory(backend=quantum_simulator)
        state = memory.create_quantum_state("test data")
        assert memory.verify_quantum_state(state)

    def test_superposition_storage(self, quantum_simulator):
        """Test superposition-based storage."""
        memory = QuantumMemory(backend=quantum_simulator)
        memory.store_in_superposition(["data1", "data2", "data3"])
        retrieved = memory.quantum_retrieve("data2")
        assert "data2" in retrieved

# tests/integration/test_backends.py
import pytest
from quantum_langchain.backends import QiskitBackend, PennyLaneBackend

class TestQuantumBackends:
    @pytest.mark.parametrize("backend_class", [
        QiskitBackend,
        PennyLaneBackend
    ])
    def test_backend_initialization(self, backend_class):
        """Test backend initialization."""
        backend = backend_class()
        assert backend.is_available()

    def test_circuit_execution(self, quantum_simulator):
        """Test quantum circuit execution."""
        backend = QiskitBackend(simulator=quantum_simulator)
        circuit = backend.create_test_circuit()
        result = backend.execute(circuit)
        assert result.success

# tests/integration/test_memory_integration.py
from quantum_langchain import QLChain
from quantum_langchain.memory import QuantumMemory

class TestMemoryIntegration:
    def test_memory_chain_integration(self, quantum_simulator):
        """Test memory integration with chains."""
        memory = QuantumMemory(backend=quantum_simulator)
        chain = QLChain(memory=memory)

        # Store and retrieve
        chain.invoke("Remember: quantum is powerful")
        response = chain.invoke("What did I tell you about quantum?")
        assert "powerful" in response.lower()

# tests/e2e/test_quantum_rag.py
from quantum_langchain.chains import QuantumRAGChain
from quantum_langchain.vectorstores import QuantumChromaDB

class TestQuantumRAG:
    def test_complete_rag_workflow(self, quantum_simulator):
        """Test complete RAG workflow."""
        # Setup
        vectorstore = QuantumChromaDB(backend=quantum_simulator)
        rag_chain = QuantumRAGChain(
            vectorstore=vectorstore,
            llm="mock"
        )

        # Add documents
        docs = ["Quantum computing is revolutionary",
                "LangChain enables AI applications"]
        rag_chain.add_documents(docs)

        # Query
        result = rag_chain.invoke("Tell me about quantum computing")
        assert "revolutionary" in result.lower()

# tests/performance/test_quantum_performance.py
import time
import pytest
from quantum_langchain.memory import QuantumMemory

class TestQuantumPerformance:
    def test_quantum_memory_scalability(self, quantum_simulator):
        """Test memory performance with scale."""
        memory = QuantumMemory(backend=quantum_simulator)

        # Test with increasing data sizes
        for size in [10, 100, 1000]:
            start_time = time.time()

            # Store data
            data = [f"item_{i}" for i in range(size)]
            memory.bulk_store(data)

            # Retrieve data
            retrieved = memory.quantum_search("item_500")

            execution_time = time.time() - start_time
            assert execution_time < size * 0.1  # Linear scaling test

# tests/performance/test_benchmarks.py
from quantum_langchain.benchmarks import QuantumBenchmark

class TestBenchmarks:
    def test_quantum_advantage_benchmark(self):
        """Test quantum advantage measurement."""
        benchmark = QuantumBenchmark()

        classical_time = benchmark.run_classical_search(1000)
        quantum_time = benchmark.run_quantum_search(1000)

        # Expect quantum advantage for large datasets
        if benchmark.has_quantum_advantage():
            assert quantum_time < classical_time

# tests/error/test_quantum_errors.py
from quantum_langchain.errors import QuantumError
from quantum_langchain.backends import QiskitBackend

class TestQuantumErrors:
    def test_quantum_error_handling(self, quantum_simulator):
        """Test quantum error recovery."""
        backend = QiskitBackend(simulator=quantum_simulator)

        # Introduce noise
        backend.add_noise_model("depolarizing", 0.1)

        # Test error correction
        with pytest.raises(QuantumError):
            backend.execute_unreliable_circuit()

    def test_error_recovery(self, quantum_simulator):
        """Test automatic error recovery."""
        backend = QiskitBackend(
            simulator=quantum_simulator,
            error_correction=True
        )

        result = backend.execute_with_recovery()
        assert result.success