quantum programming

# Define a composite quantum gate
def CompositeGate(q1, q2, q3):
    H(q1)               # Hadamard on q1
    CX(q1, q2)          # CNOT between q1 and q2
    RZ(q3, theta=1.57)  # Rotate q3 around the Z-axis
    CX(q2, q3)          # CNOT between q2 and q3
    T(q1)               # T gate on q1

# Apply the composite gate
CompositeGate(qubits[0], qubits[1], qubits[2])


# Define a dynamic gate
def DynamicGate(q1, q2, condition):
    if condition == "high_fidelity":
        CX(q1, q2)
    elif condition == "low_fidelity":
        SWAP(q1, q2)
    else:
        H(q1)
        H(q2)

# Apply the dynamic gate
DynamicGate(qubits[0], qubits[1], condition="high_fidelity")



# Create entanglement in groups of qubits
group1 = qubits[0:64]
group2 = qubits[64:128]

Entangle(group1, group2)
print("Entanglement Success")

# Inspect a quantum register
state_info = InspectState(qubits)
print("Quantum State Info:", state_info)

# Monitor state transitions
for transition in MonitorTransitions(qubits):
    print("State Transition:", transition)


# Create a quantum tensor
tensor = QuantumTensor(shape=(4, 4), data=[[0, 1], [1, 0], [0, 0], [1, 1]])

# Perform tensor operations
transposed_tensor = Transpose(tensor)
contracted_tensor = Contract(tensor, mode="inner")

# Generate and distribute quantum keys
key_manager = QuantumKeyManager(size=256, encryption="AES-512")
key_manager.GenerateKeys(users=["Alice", "Bob"])

# Use the keys securely
encrypted_message = Encrypt("message", key_manager.GetKey("Alice"))
decrypted_message = Decrypt(encrypted_message, key_manager.GetKey("Bob"))
print("Decrypted Message:", decrypted_message)



# Define a quantum-secure blockchain
blockchain = QuantumBlockchain(encryption="SHA-512", consensus="quantum_proof_of_work")

# Add transactions to the blockchain
transaction = {"from": "Alice", "to": "Bob", "amount": 100}
blockchain.AddTransaction(transaction)

# Mine and verify blocks
blockchain.MineBlock(miner="Charlie")
print("Blockchain State:", blockchain.GetState())

# Define a molecule
molecule H2O {
    atoms = ["H", "O", "H"]
    bonds = [("H", "O"), ("O", "H")]
}

# Simulate the molecule
simulation_result = SimulateMolecule(molecule, steps=100)
print("Simulation Result:", simulation_result)





# Simulate protein folding
protein = ProteinFolding(
    sequence="AGCTTACGATCG",
    quantum_model="QuantumFoldingNN"
)
folding_result = protein.Simulate()
print("Protein Folding Result:", folding_result)





# Analyze patient data using quantum-enhanced methods
patient_data = SecureTensor("patient_genomics.csv", encryption="RSA-2048")

# Perform a quantum-driven analysis
treatment_plan = QuantumAnalyze(patient_data, objective="optimize_treatment")
print("Treatment Plan:", treatment_plan)




# Optimize a financial portfolio
portfolio = QuantumPortfolio(
    assets=["StockA", "StockB", "StockC"],
    constraints={"max_risk": 0.05}
)

# Perform optimization
optimal_allocation = OptimizePortfolio(portfolio)
print("Optimal Allocation:", optimal_allocation)


# Test a quantum circuit
test_case QuantumCircuitTest {
    circuit = Circuit(qubits)
    AddGate(circuit, "H", qubits[0:256])
    result = ExecuteCircuit(circuit)
    assert(result.success)
}
RunTest(QuantumCircuitTest)



# Validate an entire workflow
workflow = QuantumWorkflow(
    steps=[
        "Initialize 256 qubits",
        "Apply Hadamard gates",
        "Entangle adjacent pairs",
        "Measure all qubits"
    ]
)

# Execute and validate
workflow_result = ValidateWorkflow(workflow)
assert(workflow_result.success)

# Define and simulate a molecule
molecule CO2 {
    atoms = ["C", "O", "O"]
    bonds = [("C", "O"), ("C", "O")]
}

simulation_result = SimulateMolecule(molecule, steps=500)
print("Simulation Result:", simulation_result)


# Secure AI model definition
secure_model = SecureQuantumModel(
    architecture="QuantumTransformer",
    qubits=Register(256),
    encryption="AES-256"
)

# Train with encrypted data
Train(secure_model, SecureTensor("training_data.csv"))

# Inference with secure input
result = Predict(secure_model, SecureTensor("new_input.csv"))
print("Prediction:", result)


# Stream quantum measurements
stream = QuantumStream(qubits, rate=1000)  # 1000 measurements per second

for measurement in stream:
    print("Measurement:", measurement)


# Execute tasks in parallel
parallel {
    task1 = ExecuteCircuit(circuit1)
    task2 = ExecuteCircuit(circuit2)
}

# Wait for results
results = WaitAll(task1, task2)

# Functional transformations
data_pipeline = [
    Filter(lambda x: x.entanglement > 0.8),
    Map(lambda x: x * 2),
    Reduce(lambda acc, x: acc + x)
]

# Apply to quantum dataset
result = QuantumApply(data_pipeline, dataset)