class HybridSwarmArchitecture:
    def __init__(self, num_agents, quantum_backend=None):
        self.agents = [EdgeAgent(i) for i in range(num_agents)]
        self.quantum_solver = QuantumOptimizer(backend=quantum_backend)
        self.classical_solver = ClassicalOptimizer()
        self.coordination_layer = SwarmCoordinator()

    async def coordinate_mission(self, terrain_map, science_goals):
        # Level 1: Local edge decisions (microseconds)
        local_paths = await asyncio.gather(*[
            agent.plan_local_path(terrain_map)
            for agent in self.agents
        ])

        # Level 2: Swarm-wide QUBO formulation for quantum solver
        if self.quantum_solver.available:
            qubo_problem = self.formulate_coordination_qubo(local_paths)
            quantum_solution = await self.quantum_solver.solve(qubo_problem)
            global_coordination = self.decode_quantum_solution(quantum_solution)
        else:
            # Fallback to classical optimization
            global_coordination = await self.classical_solver.optimize(local_paths)

        # Level 3: Dynamic reallocation based on discoveries
        return self.adapt_plan(global_coordination, science_goals)

class QuantumClassicalInterface:
    """Manages the handoff between quantum and classical solvers"""

    def __init__(self, problem_type="path_optimization"):
        self.problem_mapper = QUBOMapper(problem_type)
        self.solution_validator = SolutionValidator()
        self.hybrid_strategy = "quantum_first"

    async def solve_hybrid(self, problem_instance, timeout_ms=1000):
        """Execute hybrid solving with fallback mechanisms"""

        # Step 1: Problem decomposition
        quantum_subproblem, classical_subproblem = \
            self.decompose_problem(problem_instance)

        # Step 2: Parallel solving
        quantum_task = asyncio.create_task(
            self.solve_quantum(quantum_subproblem)
        )
        classical_task = asyncio.create_task(
            self.solve_classical(classical_subproblem)
        )

        # Step 3: Result integration with timeout
        try:
            quantum_result = await asyncio.wait_for(
                quantum_task, timeout=timeout_ms/2000
            )
            if self.solution_validator.validate(quantum_result):
                return self.integrate_solutions(
                    quantum_result,
                    await classical_task
                )
        except (asyncio.TimeoutError, QuantumHardwareError):
            # Fallback to fully classical solution
            print("Quantum timeout, using classical fallback")
            return await classical_task

        # Step 4: Classical refinement
        return self.classical_refinement(
            await classical_task,
            quantum_result if 'quantum_result' in locals() else None
        )

import numpy as np
import tensorflow.lite as tflite

class EdgeAgent:
    """Lightweight agent running on constrained hardware"""

    def __init__(self, agent_id, model_path="geology_detector.tflite"):
        self.id = agent_id
        self.position = np.zeros(3)
        self.battery = 1.0

        # Ultra-lightweight ML model for on-device inference
        self.detector = tflite.Interpreter(model_path=model_path)
        self.detector.allocate_tensors()

        # Local belief state
        self.local_map = ProbabilisticOccupancyGrid(resolution=0.1)
        self.science_targets = []

    async def autonomous_decision_cycle(self, sensor_data):
        """Main decision loop running at 10Hz on edge hardware"""

        # 1. Process sensor data (LiDAR, spectrometer, camera)
        processed_data = self.preprocess_sensors(sensor_data)

        # 2. Update local map and detect features
        features = self.detect_geological_features(processed_data)

        # 3. Update science priorities
        if features:
            self.update_science_priorities(features)

        # 4. Compute immediate navigation actions
        action = self.compute_safe_action()

        # 5. Compress and queue data for transmission
        compressed_update = self.compress_state_update()

        return action, compressed_update

    def detect_geological_features(self, sensor_data):
        """On-device ML inference for feature detection"""
        input_details = self.detector.get_input_details()
        output_details = self.detector.get_output_details()

        # Prepare input tensor
        input_data = np.expand_dims(sensor_data, axis=0).astype(np.float32)
        self.detector.set_tensor(input_details[0]['index'], input_data)

        # Run inference (typically < 10ms on edge hardware)
        self.detector.invoke()

        # Parse results
        predictions = self.detector.get_tensor(output_details[0]['index'])
        return self.threshold_predictions(predictions[0])

class AdaptiveSurveyRL:
    """Reinforcement learning for dynamic survey pattern selection"""

    def __init__(self):
        self.policy_network = self.build_policy_network()
        self.value_network = self.build_value_network()
        self.pattern_library = {
            'grid': GridPattern(),
            'radial': RadialPattern(),
            'spiral': SpiralPattern(),
            'feature_following': FeatureFollowingPattern()
        }

    def select_survey_pattern(self, state):
        """Select optimal survey pattern based on current state"""

        # State includes: terrain roughness, feature density,
        # agent distribution, remaining energy, time constraints
        state_vector = self.encode_state(state)

        # Policy network outputs pattern probabilities
        pattern_probs = self.policy_network.predict(state_vector)

        # Sample from distribution with temperature
        pattern_idx = self.sample_with_temperature(pattern_probs, temp=0.1)

        return list(self.pattern_library.values())[pattern_idx]

    async def learn_from_experience(self, trajectories):
        """Update policy based on collected experience"""
        # Uses Proximal Policy Optimization (PPO) for stable learning
        # in non-stationary environments
        advantages = self.compute_advantages(trajectories)

        for epoch in range(self.training_epochs):
            policy_loss = self.compute_policy_loss(trajectories, advantages)
            value_loss = self.compute_value_loss(trajectories)

            self.policy_network.update(policy_loss)
            self.value_network.update(value_loss)

            # Early stopping if KL divergence too high
            if self.kl_divergence > self.max_kl:
                break

class MultiModalGeologyDetector:
    """Fuses data from multiple sensors for robust detection"""

    def __init__(self):
        self.modalities = {
            'visible': RGBDetector(),
            'multispectral': SpectralAnalyzer(),
            'lidar': TopographyAnalyzer(),
            'radar': SubsurfaceRadar(),
            'xrf': CompositionAnalyzer()  # X-ray fluorescence
        }

        # Learned fusion weights from field testing
        self.fusion_weights = self.load_calibrated_weights()

    def detect_with_confidence(self, sensor_readings):
        """Fuse detections with confidence estimates"""

        modality_detections = {}
        modality_confidences = {}

        # Parallel inference across modalities
        for name, detector in self.modalities.items():
            if name in sensor_readings:
                detection, confidence = detector.process(
                    sensor_readings[name]
                )
                modality_detections[name] = detection
                modality_confidences[name] = confidence

        # Dempster-Shafer evidence theory for fusion
        fused_belief = self.dempster_shafer_fusion(
            modality_detections,
            modality_confidences
        )

        # Contextual refinement using geological priors
        refined_result = self.apply_geological_priors(fused_belief)

        return refined_result

class PredictiveSynchronization:
    """Maintains swarm coherence despite communication gaps"""

    def __init__(self, max_delay=300):  # 5 minutes max delay
        self.agent_models = {}  # Predictive models of other agents
        self.shared_mental_model = SharedBeliefState()
        self.sync_strategy = "optimistic_with_rollback"

    async def maintain_coherence(self, local_state, received_updates):
        """Maintain coherent swarm state despite delays"""

        # 1. Update predictive models based on received updates
        for update in received_updates:
            self.update_agent_model(update.agent_id, update)

        # 2. Predict current states of other agents
        predicted_states = self.predict_all_agent_states()

        # 3. Detect and resolve inconsistencies
        inconsistencies = self.detect_inconsistencies(
            local_state,
            predicted_states
        )

        if inconsistencies:
            # Use conflict-free replicated data type (CRDT) principles
            resolved_state = self.resolve_crdt_style(inconsistencies)

            # If quantum solver available, use it for optimal resolution
            if self.quantum_available:
                resolution = await self.quantum_conflict_resolution(
                    inconsistencies
                )
                resolved_state = self.merge_resolutions(
                    resolved_state,
                    resolution
                )

            return resolved_state

        return self.shared_mental_model.get_consensus_view()

class HierarchicalQuantumDecomposition:
    """Decomposes large problems for current quantum hardware"""

    def decompose_for_quantum(self, full_problem, max_qubits=1000):
        """Break down problem into quantum-solvable subproblems"""

        # Step 1: Community detection in coordination graph
        communities = self.detect_coordination_communities(full_problem)

        # Step 2: Solve intra-community with quantum
        quantum_solutions = []
        for community in communities:
            if self.estimate_qubits(community) <= max_qubits:
                qubo = self.formulate_community_qubo(community)
                solution = await self.quantum_solver.solve(qubo)
                quantum_solutions.append((community, solution))
            else:
                # Recursive decomposition
                sub_solutions = self.decompose_for_quantum(
                    community,
                    max_qubits
                )
                quantum_solutions.extend(sub_solutions)

        # Step 3: Solve inter-community with classical
        inter_community_problem = self.formulate_inter_community_problem(
            quantum_solutions
        )
        classical_solution = self.classical_solver.solve(
            inter_community_problem
        )

        # Step 4: Reintegrate solutions
        return self.reintegrate_solutions(
            quantum_solutions,
            classical_solution
        )

class EnergyAwareComputation:
    """Dynamically adjusts computation based on energy constraints"""

    def __init__(self, battery_capacity, compute_budget):
        self.energy_budget = battery_capacity
        self.compute_allocator = ComputeAllocator()
        self.quality_of_service = {
            'navigation': 1.0,      # Critical: never skip
            'obstacle_avoidance': 1.0,
            'science_detection': 0.8,  # Can reduce resolution
            'mapping': 0.6,           # Can reduce update rate
            'communication': 0.4      # Can compress aggressively
        }

    def allocate_compute_cycle(self, current_energy):
        """Allocate limited compute resources optimally"""

        energy_ratio = current_energy / self.energy_budget

        # Adaptive quality levels based on energy
        if energy_ratio < 0.3:
            # Energy conservation mode
            quality_levels = self.reduce_quality(self.quality_of_service, 0.5)
        elif energy_ratio < 0.6:
            # Balanced mode
            quality_levels = self.quality_of_service
        else:
            # High-performance mode
            quality_levels = self.increase_quality(self.quality_of_service, 1.2)

        # Use quantum-inspired optimization for allocation
        allocation_problem = self.formulate_allocation_qubo(quality_levels)

        if self.quantum_available:
            allocation = self.quantum_solver.solve(allocation_problem)
        else:
            allocation = self.greedy_allocation(quality_levels)

        return allocation