QASM256-Quantum Robotics

# Define a robot control problem
robot_control = QuantumOptimization(
    state_space="robot_arm_movement",
    objective="minimize_energy",
    constraints={"collision_avoidance": True}
)

# Optimize control commands
control_sequence = Solve(robot_control)
ExecuteRobotCommands(control_sequence)
print("Optimized Control Sequence:", control_sequence)

# Define swarm coordination problem
swarm = QuantumSwarm(
    robots=["Drone1", "Drone2", "Drone3"],
    task="search_and_rescue",
    constraints={"energy_efficiency": True}
)

# Optimize swarm actions
swarm_strategy = OptimizeSwarm(swarm)
print("Optimized Swarm Strategy:", swarm_strategy)


# Set up real-time quantum feedback loop
feedback = QuantumFeedback(
    sensors=["Lidar", "Camera"],
    quantum_model="AdaptiveControlNN"
)

# Run feedback for robotic navigation
while not MissionComplete():
    feedback.Update()
    print("Robot State:", feedback.GetState())



# Define an energy grid optimization problem
grid = EnergyGrid(
    nodes=["Solar", "Wind", "Battery"],
    objective="maximize_efficiency",
    constraints={"carbon_emissions": 0}
)

# Solve the optimization
grid_result = QuantumOptimize(grid)
print("Optimal Energy Distribution:", grid_result)


# Define hydrogen engine parameters
engine = HydrogenEngine(
    combustion_model="QuantumFluidDynamics",
    energy_input=100,
    compression_ratio=8.0
)

# Simulate combustion process
simulation_result = SimulateEngine(engine, steps=500)
print("Simulation Result:", simulation_result)



# Define battery optimization problem
battery = QuantumBattery(
    model="LithiumIon",
    objective="maximize_lifetime",
    constraints={"temperature_limit": 50}
)

# Solve for optimal usage cycles
battery_strategy = OptimizeBattery(battery)
print("Optimal Battery Strategy:", battery_strategy)


# Set up real-time quantum optimizer
real_time_optimizer = RealTimeQuantumOptimize(
    data_stream="sensor_data.csv",
    objective="dynamic_adaptation",
    refresh_rate=1  # Every second
)

# Run optimizer in real-time
while True:
    optimized_result = real_time_optimizer.Update()
    print("Real-Time Optimization:", optimized_result)



# Define quantum data sources
data_sources = QuantumDataSources(["Source1", "Source2", "Source3"])

# Fuse data into a unified model
fused_model = QuantumFuse(data_sources, method="Bayesian")
print("Fused Quantum Model:", fused_model)

# Initialize adaptive quantum model
adaptive_model = AdaptiveQuantumModel(
    architecture="SelfOptimizingNN",
    qubits=Register(256)
)

# Continuously adapt the model
for new_data in DataStream("real_time_data.csv"):
    adaptive_model.Update(new_data)
    print("Updated Model:", adaptive_model)


# Define a stress test case
stress_test QuantumStressTest {
    qubits = Register(1024)
    circuit = Circuit(qubits)

    for i in range(1024):
        AddGate(circuit, "H", qubits[i])

    ExecuteCircuit(circuit)
}
RunTest(QuantumStressTest)


# Validate a quantum workflow
workflow = QuantumWorkflow(
    steps=[
        "Initialize qubits",
        "Entangle pairs",
        "Apply Grover's algorithm",
        "Measure results"
    ]
)

validation_result = ValidateWorkflow(workflow)
assert(validation_result.success)



# Define a fuzz test case
fuzz_test SecureFuzzTest {
    pipeline = SecurePipeline(steps=["Encrypt", "Optimize", "Decrypt"])
    FuzzTest(pipeline, iterations=10000)
}
RunTest(SecureFuzzTest)