AWS Integration

from braket.aws import (
    AwsDevice, AwsDeviceType, AwsQuantumTask, AwsQuantumTaskBatch, 
    AwsQuantumJob, AwsSession, DirectReservation
)
from braket.jobs import (
    hybrid_job, CheckpointConfig, InstanceConfig, OutputDataConfig,
    S3DataSourceConfig, StoppingCondition, LocalQuantumJob,
    load_job_checkpoint, load_job_result, save_job_checkpoint, save_job_result,
    get_checkpoint_dir, get_hyperparameters, get_input_data_dir, 
    get_job_device_arn, get_job_name, get_results_dir,
    Framework, retrieve_image
)
from braket.tracking import Tracker
from braket.tasks import QuantumTask, QuantumTaskBatch

from braket.aws import AwsDevice, AwsDeviceType
from enum import Enum

class AwsDeviceType(Enum):
    """AWS Braket device type enumeration."""
    SIMULATOR = "SIMULATOR"
    QPU = "QPU"

class AwsDevice:
    """Amazon Braket implementation of quantum devices."""
    
    def __init__(self, arn: str, aws_session: 'AwsSession' = None):
        """
        Initialize AWS quantum device.
        
        Args:
            arn: AWS device ARN
            aws_session: Optional AWS session, creates default if None
        """
        self.arn = arn
        self.aws_session = aws_session
    
    @staticmethod
    def get_devices(
        arns: list[str] = None, 
        names: list[str] = None,
        types: list[AwsDeviceType] = None,
        statuses: list[str] = None,
        provider_names: list[str] = None,
        aws_session: 'AwsSession' = None
    ) -> list['AwsDevice']:
        """
        Get AWS Braket devices matching criteria.
        
        Args:
            arns: Device ARNs to filter by
            names: Device names to filter by  
            types: Device types to filter by
            statuses: Device statuses to filter by
            provider_names: Provider names to filter by
            aws_session: AWS session for API calls
            
        Returns:
            list[AwsDevice]: Matching devices
        """
        pass
    
    def run(
        self, 
        task_specification, 
        shots: int = 1000,
        inputs: dict = None,
        gate_definitions: dict = None,
        reservation_arn: str = None,
        tags: dict = None
    ) -> 'AwsQuantumTask':
        """
        Execute quantum task on AWS device.
        
        Args:
            task_specification: Circuit, AHS program, or annealing problem
            shots: Number of measurement shots
            inputs: Input parameters for parameterized programs
            gate_definitions: Custom gate definitions
            reservation_arn: Device reservation ARN
            tags: AWS resource tags
            
        Returns:
            AwsQuantumTask: Quantum task execution handle
        """
        pass
    
    def run_batch(
        self,
        task_specifications: list,
        shots: int = 1000,
        max_parallel: int = 10,
        inputs: list[dict] = None,
        tags: dict = None
    ) -> 'AwsQuantumTaskBatch':
        """
        Execute batch of quantum tasks.
        
        Args:
            task_specifications: List of circuits/programs
            shots: Number of measurement shots per task
            max_parallel: Maximum parallel executions
            inputs: Input parameters for each task
            tags: AWS resource tags
            
        Returns:
            AwsQuantumTaskBatch: Batch execution handle
        """
        pass
    
    @property
    def name(self) -> str:
        """Device name."""
        pass
    
    @property
    def status(self) -> str:
        """Current device status."""
        pass
    
    @property
    def type(self) -> AwsDeviceType:
        """Device type (QPU or Simulator)."""
        pass
    
    @property
    def provider_name(self) -> str:
        """Device provider name."""
        pass
    
    @property
    def properties(self) -> dict:
        """Device capabilities and properties."""
        pass

# Device discovery examples
def discover_aws_devices() -> dict[str, list[AwsDevice]]:
    """
    Discover and categorize available AWS Braket devices.
    
    Returns:
        dict: Categorized devices by provider and type
    """
    devices = {
        'simulators': AwsDevice.get_devices(types=[AwsDeviceType.SIMULATOR]),
        'qpus': AwsDevice.get_devices(types=[AwsDeviceType.QPU]),
        'ionq': AwsDevice.get_devices(provider_names=['IonQ']),
        'rigetti': AwsDevice.get_devices(provider_names=['Rigetti']),
        'oqc': AwsDevice.get_devices(provider_names=['Oxford Quantum Computing']),
        'quera': AwsDevice.get_devices(provider_names=['QuEra'])
    }
    
    return devices

def select_optimal_device(circuit, requirements: dict) -> AwsDevice:
    """
    Select optimal device based on circuit and requirements.
    
    Args:
        circuit: Quantum circuit to execute
        requirements: Performance and cost requirements
        
    Returns:
        AwsDevice: Selected optimal device
    """
    all_devices = AwsDevice.get_devices()
    
    # Filter by availability and qubit count
    available_devices = []
    for device in all_devices:
        if (device.status == 'ONLINE' and 
            device.properties.get('qubitCount', 0) >= circuit.qubit_count):
            available_devices.append(device)
    
    # Select based on requirements
    if requirements.get('cost_priority', False):
        # Prefer simulators for cost
        simulators = [d for d in available_devices if d.type == AwsDeviceType.SIMULATOR]
        return simulators[0] if simulators else available_devices[0]
    
    elif requirements.get('real_hardware', False):
        # Prefer QPUs for real quantum effects
        qpus = [d for d in available_devices if d.type == AwsDeviceType.QPU]
        return qpus[0] if qpus else available_devices[0]
    
    else:
        # Default selection
        return available_devices[0]

def analyze_device_capabilities(device: AwsDevice) -> dict:
    """
    Analyze device capabilities and constraints.
    
    Args:
        device: AWS device to analyze
        
    Returns:
        dict: Comprehensive device capability analysis
    """
    properties = device.properties
    
    capabilities = {
        'basic_info': {
            'name': device.name,
            'provider': device.provider_name,
            'type': device.type.value,
            'status': device.status
        },
        'quantum_specs': {},
        'gate_set': {},
        'connectivity': {},
        'timing_info': {},
        'error_rates': {}
    }
    
    # Quantum specifications
    if 'qubitCount' in properties:
        capabilities['quantum_specs']['qubit_count'] = properties['qubitCount']
    
    # Gate set analysis
    if 'supportedOperations' in properties:
        capabilities['gate_set'] = properties['supportedOperations']
    
    # Connectivity graph
    if 'connectivity' in properties:
        capabilities['connectivity'] = properties['connectivity']
    
    # Timing constraints
    if 'timing' in properties:
        capabilities['timing_info'] = properties['timing']
    
    # Error characteristics
    if 'fidelity' in properties:
        capabilities['error_rates'] = properties['fidelity']
    
    return capabilities

def check_circuit_compatibility(circuit, device: AwsDevice) -> dict:
    """
    Check circuit compatibility with device.
    
    Args:
        circuit: Quantum circuit to validate
        device: Target AWS device
        
    Returns:
        dict: Compatibility analysis results
    """
    properties = device.properties
    
    compatibility = {
        'is_compatible': True,
        'issues': [],
        'warnings': [],
        'recommendations': []
    }
    
    # Check qubit count
    if circuit.qubit_count > properties.get('qubitCount', 0):
        compatibility['is_compatible'] = False
        compatibility['issues'].append(f"Circuit requires {circuit.qubit_count} qubits, device has {properties.get('qubitCount', 0)}")
    
    # Check gate support
    supported_gates = properties.get('supportedOperations', [])
    for instruction in circuit.instructions:
        gate_name = instruction.operator.__class__.__name__
        if gate_name not in supported_gates:
            compatibility['warnings'].append(f"Gate {gate_name} may not be natively supported")
    
    # Check connectivity (for QPUs)
    if device.type == AwsDeviceType.QPU and 'connectivity' in properties:
        connectivity_graph = properties['connectivity']
        for instruction in circuit.instructions:
            if len(instruction.target) == 2:  # Two-qubit gate
                qubit_pair = tuple(instruction.target)
                if qubit_pair not in connectivity_graph.get('fullyConnected', []):
                    compatibility['warnings'].append(f"Two-qubit gate on {qubit_pair} may require SWAP operations")
    
    return compatibility

from braket.aws import AwsQuantumTask, AwsQuantumTaskBatch
from braket.tasks import QuantumTask

class AwsQuantumTask(QuantumTask):
    """AWS quantum task execution interface."""
    
    def __init__(self, arn: str, aws_session: 'AwsSession' = None):
        """
        Initialize AWS quantum task.
        
        Args:
            arn: Task ARN
            aws_session: AWS session for API calls
        """
        self.arn = arn
        self.aws_session = aws_session
    
    def result(self) -> 'TaskResult':
        """
        Get task execution results.
        
        Returns:
            TaskResult: Quantum task results
        """
        pass
    
    def state(self) -> str:
        """
        Get current task state.
        
        Returns:
            str: Task state (QUEUED, RUNNING, COMPLETED, FAILED, CANCELLED)
        """
        pass
    
    def cancel(self) -> None:
        """Cancel running task."""
        pass
    
    def metadata(self) -> dict:
        """
        Get task metadata.
        
        Returns:
            dict: Task execution metadata
        """
        pass
    
    @property
    def id(self) -> str:
        """Task ID."""
        pass
    
    @staticmethod
    def get(arn: str, aws_session: 'AwsSession' = None) -> 'AwsQuantumTask':
        """
        Get existing task by ARN.
        
        Args:
            arn: Task ARN
            aws_session: AWS session
            
        Returns:
            AwsQuantumTask: Task instance
        """
        pass

class AwsQuantumTaskBatch:
    """Batch execution of quantum tasks."""
    
    def __init__(self, tasks: list[AwsQuantumTask]):
        """
        Initialize task batch.
        
        Args:
            tasks: List of quantum tasks
        """
        self.tasks = tasks
    
    def results(self) -> list:
        """
        Get results from all tasks in batch.
        
        Returns:
            list: Results from completed tasks
        """
        pass
    
    def unsuccessful(self) -> list[AwsQuantumTask]:
        """
        Get unsuccessful tasks.
        
        Returns:
            list[AwsQuantumTask]: Failed or cancelled tasks
        """
        pass
    
    def retry_unsuccessful_tasks(self) -> 'AwsQuantumTaskBatch':
        """
        Retry failed tasks.
        
        Returns:
            AwsQuantumTaskBatch: New batch with retried tasks
        """
        pass

# Task execution examples
def execute_quantum_task_with_monitoring(circuit, device: AwsDevice) -> dict:
    """
    Execute quantum task with comprehensive monitoring.
    
    Args:
        circuit: Quantum circuit to execute
        device: Target AWS device
        
    Returns:
        dict: Task results and execution information
    """
    import time
    
    # Submit task
    task = device.run(circuit, shots=1000)
    
    execution_info = {
        'task_arn': task.arn,
        'submission_time': time.time(),
        'device_arn': device.arn,
        'status_history': []
    }
    
    # Monitor execution
    while task.state() not in ['COMPLETED', 'FAILED', 'CANCELLED']:
        status = task.state()
        execution_info['status_history'].append({
            'status': status,
            'timestamp': time.time()
        })
        
        print(f"Task status: {status}")
        time.sleep(5)  # Check every 5 seconds
    
    # Get final results
    if task.state() == 'COMPLETED':
        result = task.result()
        execution_info['completion_time'] = time.time()
        execution_info['results'] = {
            'measurement_counts': result.measurement_counts,
            'task_metadata': result.task_metadata
        }
    else:
        execution_info['error_info'] = task.metadata()
    
    return execution_info

def execute_parameter_sweep(circuit_template, parameter_ranges: dict, device: AwsDevice) -> dict:
    """
    Execute parameter sweep across quantum circuit parameters.
    
    Args:
        circuit_template: Parameterized circuit template
        parameter_ranges: Parameter ranges to sweep
        device: Target AWS device
        
    Returns:
        dict: Parameter sweep results
    """
    import itertools
    from braket.parametric import FreeParameter
    
    # Generate parameter combinations
    param_names = list(parameter_ranges.keys())
    param_values = list(parameter_ranges.values())
    param_combinations = list(itertools.product(*param_values))
    
    # Create circuits for each parameter combination
    circuits = []
    for combination in param_combinations:
        param_dict = dict(zip(param_names, combination))
        circuit = circuit_template.copy()
        
        # Bind parameters
        for param_name, value in param_dict.items():
            circuit = circuit.substitute_free_parameter(FreeParameter(param_name), value)
        
        circuits.append((circuit, param_dict))
    
    # Execute batch
    task_specs = [circuit for circuit, _ in circuits]
    batch = device.run_batch(task_specs, shots=1000)
    
    # Collect results
    results = batch.results()
    sweep_results = {
        'parameter_combinations': [params for _, params in circuits],
        'results': results,
        'analysis': {}
    }
    
    # Analyze results
    for i, (result, params) in enumerate(zip(results, sweep_results['parameter_combinations'])):
        sweep_results['analysis'][str(params)] = {
            'measurement_counts': result.measurement_counts,
            'expectation_values': getattr(result, 'values', [])
        }
    
    return sweep_results

from braket.aws import AwsSession
import boto3

class AwsSession:
    """AWS authentication and session management."""
    
    def __init__(
        self,
        boto_session: boto3.Session = None,
        braket_user_agents: list[str] = None,
        config: dict = None
    ):
        """
        Initialize AWS session for Braket.
        
        Args:
            boto_session: Pre-configured boto3 session
            braket_user_agents: Custom user agent strings
            config: Session configuration options
        """
        self.boto_session = boto_session
        self.braket_user_agents = braket_user_agents
        self.config = config or {}
    
    @property
    def region(self) -> str:
        """Current AWS region."""
        pass
    
    @property
    def account_id(self) -> str:
        """AWS account ID."""
        pass
    
    def get_device(self, arn: str) -> AwsDevice:
        """
        Get device using this session.
        
        Args:
            arn: Device ARN
            
        Returns:
            AwsDevice: Device with this session
        """
        pass

def create_aws_session_with_profile(profile_name: str, region: str = 'us-east-1') -> AwsSession:
    """
    Create AWS session using named profile.
    
    Args:
        profile_name: AWS credential profile name
        region: AWS region for Braket services
        
    Returns:
        AwsSession: Configured session
    """
    boto_session = boto3.Session(profile_name=profile_name, region_name=region)
    return AwsSession(boto_session=boto_session)

def create_aws_session_with_keys(access_key: str, secret_key: str, region: str = 'us-east-1') -> AwsSession:
    """
    Create AWS session using access keys.
    
    Args:
        access_key: AWS access key ID
        secret_key: AWS secret access key  
        region: AWS region for Braket services
        
    Returns:
        AwsSession: Configured session
    """
    boto_session = boto3.Session(
        aws_access_key_id=access_key,
        aws_secret_access_key=secret_key,
        region_name=region
    )
    return AwsSession(boto_session=boto_session)

def validate_aws_session(session: AwsSession) -> dict:
    """
    Validate AWS session configuration and permissions.
    
    Args:
        session: AWS session to validate
        
    Returns:
        dict: Validation results and recommendations
    """
    validation = {
        'is_valid': True,
        'region': session.region,
        'account_id': session.account_id,
        'permissions': {},
        'recommendations': []
    }
    
    try:
        # Test basic Braket permissions
        devices = AwsDevice.get_devices(aws_session=session)
        validation['permissions']['device_access'] = True
        validation['permissions']['accessible_devices'] = len(devices)
        
    except Exception as e:
        validation['is_valid'] = False
        validation['permissions']['device_access'] = False
        validation['error'] = str(e)
        validation['recommendations'].append("Check Braket service permissions")
    
    # Check for cost optimization
    if validation['region'] != 'us-east-1':
        validation['recommendations'].append("Consider us-east-1 for lowest latency to Braket services")
    
    return validation

from braket.aws import DirectReservation

class DirectReservation:
    """Context manager for using reserved quantum devices on AWS Braket."""
    
    def __init__(
        self,
        device,
        reservation_arn: str = None
    ):
        """
        Initialize direct reservation context.
        
        Args:
            device: Braket device or device ARN with reservation
            reservation_arn: AWS Braket Direct reservation ARN
        """
        self.device = device
        self.reservation_arn = reservation_arn
    
    def __enter__(self) -> 'DirectReservation':
        """
        Enter reservation context and apply reservation to quantum tasks.
        
        Returns:
            DirectReservation: Self for context management
        """
        pass
    
    def __exit__(self, exc_type, exc_val, exc_tb):
        """Exit reservation context and restore normal device access."""
        pass
    
    def start(self) -> 'DirectReservation':
        """
        Start reservation without context manager.
        
        Returns:
            DirectReservation: Self for method chaining
        """
        pass
    
    def stop(self) -> 'DirectReservation':
        """
        Stop reservation manually.
        
        Returns:
            DirectReservation: Self for method chaining
        """
        pass

# Reservation management examples
def use_device_reservation(device_arn: str, reservation_arn: str, circuits: list) -> dict:
    """
    Execute circuits using a reserved quantum device.
    
    Args:
        device_arn: AWS device ARN with reservation
        reservation_arn: Reservation ARN for exclusive access
        circuits: List of quantum circuits to execute
        
    Returns:
        dict: Execution results with reservation metadata
    """
    from braket.aws import AwsDevice
    
    device = AwsDevice(device_arn)
    reservation_results = {
        'reservation_arn': reservation_arn,
        'device_arn': device_arn,
        'circuits_executed': len(circuits),
        'results': [],
        'cost_analysis': {},
        'reservation_utilization': {}
    }
    
    # Execute circuits within reservation context
    with DirectReservation(device=device, reservation_arn=reservation_arn):
        for i, circuit in enumerate(circuits):
            task = device.run(circuit, shots=1000)
            result = task.result()
            
            reservation_results['results'].append({
                'circuit_index': i,
                'task_arn': task.arn,
                'measurement_counts': result.measurement_counts,
                'execution_duration': result.task_metadata.get('executionDuration', 'unknown')
            })
    
    # Analyze reservation utilization
    reservation_results['reservation_utilization'] = {
        'total_tasks': len(circuits),
        'estimated_time_savings': 'No queue wait time',
        'dedicated_access': True,
        'billing_model': 'Reservation-based'
    }
    
    return reservation_results

def manage_reservation_lifecycle(device_arn: str) -> dict:
    """
    Demonstrate reservation lifecycle management.
    
    Args:
        device_arn: AWS device ARN for reservation
        
    Returns:
        dict: Reservation management workflow
    """
    reservation_workflow = {
        'phases': [
            'reservation_planning',
            'reservation_creation', 
            'reservation_usage',
            'reservation_monitoring',
            'reservation_completion'
        ],
        'implementation_guide': {}
    }
    
    # Reservation planning phase
    planning_recommendations = {
        'optimal_duration': 'Based on workload analysis',
        'cost_estimation': {
            'reserved_vs_on_demand': 'Calculate savings for extended usage',
            'minimum_duration': '1 hour minimum for most devices',
            'cancellation_policy': 'Check device-specific policies'  
        },
        'scheduling_considerations': [
            'Device availability windows',
            'Time zone coordination for team access',
            'Buffer time for job completion'
        ]
    }
    
    # Usage optimization
    usage_optimization = {
        'batch_execution': 'Group circuits for efficient execution',
        'parallel_jobs': 'Use multiple job slots if available',
        'monitoring_tools': 'Track utilization during reservation',
        'fallback_strategy': 'Plan for unexpected issues'
    }
    
    reservation_workflow['implementation_guide'] = {
        'planning': planning_recommendations,
        'optimization': usage_optimization,
        'monitoring': {
            'key_metrics': ['utilization_rate', 'cost_per_shot', 'queue_time_saved'],
            'alerting': 'Set up notifications for underutilization'
        }
    }
    
    return reservation_workflow

def calculate_reservation_cost_benefit(usage_pattern: dict, device_pricing: dict) -> dict:
    """
    Calculate cost benefits of device reservations vs on-demand usage.
    
    Args:
        usage_pattern: Expected usage patterns
        device_pricing: Device pricing information
        
    Returns:
        dict: Cost-benefit analysis for reservations
    """
    analysis = {
        'usage_input': usage_pattern,
        'pricing_input': device_pricing,
        'cost_comparison': {},
        'recommendation': {}
    }
    
    # Calculate on-demand costs
    shots_per_month = usage_pattern.get('shots_per_month', 100000)
    on_demand_rate = device_pricing.get('per_shot', 0.00075)  # Example pricing
    
    on_demand_monthly_cost = shots_per_month * on_demand_rate
    
    # Calculate reservation costs (example)
    reservation_hourly_rate = device_pricing.get('reservation_hourly', 1.50)
    hours_per_month = usage_pattern.get('hours_per_month', 40)
    
    reservation_monthly_cost = hours_per_month * reservation_hourly_rate
    
    # Cost comparison
    monthly_savings = on_demand_monthly_cost - reservation_monthly_cost
    break_even_shots = reservation_monthly_cost / on_demand_rate
    
    analysis['cost_comparison'] = {
        'on_demand_monthly': on_demand_monthly_cost,
        'reservation_monthly': reservation_monthly_cost,
        'monthly_savings': monthly_savings,
        'savings_percentage': (monthly_savings / on_demand_monthly_cost) * 100 if on_demand_monthly_cost > 0 else 0,
        'break_even_shots': break_even_shots
    }
    
    # Recommendation
    if monthly_savings > 0:
        analysis['recommendation'] = {
            'use_reservations': True,
            'reason': f'Save ${monthly_savings:.2f} per month ({analysis["cost_comparison"]["savings_percentage"]:.1f}%)',
            'optimal_reservation_hours': hours_per_month
        }
    else:
        analysis['recommendation'] = {
            'use_reservations': False,
            'reason': 'On-demand pricing more cost-effective for current usage',
            'threshold_usage': f'Consider reservations above {break_even_shots:.0f} shots/month'
        }
    
    return analysis

from braket.jobs import hybrid_job, InstanceConfig, CheckpointConfig, OutputDataConfig

@hybrid_job(device="ml.m5.large")
def basic_hybrid_algorithm():
    """
    Basic hybrid quantum-classical algorithm.
    
    This job demonstrates the structure of hybrid algorithms
    combining quantum circuit execution with classical optimization.
    """
    from braket.aws import AwsDevice
    from braket.jobs import get_job_device_arn, save_job_result
    import numpy as np
    
    # Get quantum device for this job
    device_arn = get_job_device_arn()
    device = AwsDevice(device_arn)
    
    def quantum_cost_function(params: np.ndarray) -> float:
        """Quantum cost function using variational circuit."""
        from braket.circuits import Circuit
        from braket.circuits.gates import Ry, CNot
        from braket.circuits.observables import Z
        
        circuit = Circuit()
        for i, param in enumerate(params):
            circuit.ry(i, param)
        
        circuit.cnot(0, 1)
        circuit.expectation(observable=Z() @ Z(), target=[0, 1])
        
        task = device.run(circuit, shots=1000)
        result = task.result()
        return result.values[0]
    
    # Classical optimization
    from scipy.optimize import minimize
    initial_params = np.random.random(2) * np.pi
    
    result = minimize(
        quantum_cost_function,
        initial_params,
        method='COBYLA',
        options={'maxiter': 50}
    )
    
    # Save results
    save_job_result({
        'optimal_parameters': result.x.tolist(),
        'optimal_value': float(result.fun),
        'optimization_success': result.success
    })

class InstanceConfig:
    """Configuration for job compute instance."""
    
    def __init__(
        self, 
        instance_type: str, 
        instance_count: int = 1,
        volume_size_gb: int = 30
    ):
        """
        Initialize instance configuration.
        
        Args:
            instance_type: EC2 instance type (e.g., 'ml.m5.large')
            instance_count: Number of instances
            volume_size_gb: EBS volume size in GB
        """
        self.instance_type = instance_type
        self.instance_count = instance_count
        self.volume_size_gb = volume_size_gb

class CheckpointConfig:
    """Configuration for job checkpointing."""
    
    def __init__(
        self,
        s3_uri: str,
        local_path: str = "/opt/braket/checkpoints"
    ):
        """
        Initialize checkpoint configuration.
        
        Args:
            s3_uri: S3 URI for checkpoint storage
            local_path: Local checkpoint directory
        """
        self.s3_uri = s3_uri
        self.local_path = local_path

class OutputDataConfig:
    """Configuration for job output data."""
    
    def __init__(
        self,
        s3_path: str,
        kms_key_id: str = None
    ):
        """
        Initialize output data configuration.
        
        Args:
            s3_path: S3 path for output data
            kms_key_id: KMS key for encryption
        """
        self.s3_path = s3_path
        self.kms_key_id = kms_key_id

# Advanced job configuration
@hybrid_job(
    device="ml.p3.2xlarge",  # GPU instance for ML
    instance_config=InstanceConfig("ml.p3.2xlarge", instance_count=1),
    checkpoint_config=CheckpointConfig("s3://my-bucket/checkpoints/"),
    output_data_config=OutputDataConfig("s3://my-bucket/results/")
)
def advanced_variational_algorithm():
    """
    Advanced hybrid algorithm with ML and checkpointing.
    
    Demonstrates quantum machine learning with classical neural networks
    and comprehensive checkpoint management.
    """
    import torch
    import torch.nn as nn
    from braket.jobs import (
        save_job_checkpoint, load_job_checkpoint,
        get_hyperparameters, save_job_result
    )
    
    # Get job hyperparameters
    hyperparams = get_hyperparameters()
    learning_rate = hyperparams.get('learning_rate', 0.01)
    epochs = hyperparams.get('epochs', 100)
    
    # Define hybrid model
    class HybridQuantumClassicalModel(nn.Module):
        def __init__(self):
            super().__init__()
            self.classical_layers = nn.Sequential(
                nn.Linear(4, 16),
                nn.ReLU(),
                nn.Linear(16, 2)  # 2 quantum parameters
            )
            
        def quantum_layer(self, params):
            """Quantum variational layer."""
            from braket.circuits import Circuit
            from braket.circuits.gates import Ry, CNot
            from braket.circuits.observables import Z
            
            circuit = Circuit()
            circuit.ry(0, params[0])
            circuit.ry(1, params[1])
            circuit.cnot(0, 1)
            circuit.expectation(observable=Z() @ Z(), target=[0, 1])
            
            device_arn = get_job_device_arn()
            device = AwsDevice(device_arn)
            task = device.run(circuit, shots=1000)
            return task.result().values[0]
            
        def forward(self, x):
            classical_out = self.classical_layers(x)
            quantum_out = self.quantum_layer(classical_out)
            return quantum_out
    
    # Training loop with checkpointing
    model = HybridQuantumClassicalModel()
    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
    
    # Load checkpoint if exists
    try:
        checkpoint = load_job_checkpoint()
        model.load_state_dict(checkpoint['model_state'])
        optimizer.load_state_dict(checkpoint['optimizer_state'])
        start_epoch = checkpoint['epoch']
    except:
        start_epoch = 0
    
    # Training loop (simplified)
    for epoch in range(start_epoch, epochs):
        # Training step would go here
        loss = torch.randn(1)  # Placeholder
        
        # Save checkpoint every 10 epochs
        if epoch % 10 == 0:
            save_job_checkpoint({
                'model_state': model.state_dict(),
                'optimizer_state': optimizer.state_dict(),
                'epoch': epoch,
                'loss': float(loss)
            })
    
    # Save final results
    save_job_result({
        'final_model_state': model.state_dict(),
        'training_loss': float(loss),
        'total_epochs': epochs
    })

from braket.jobs import (
    get_checkpoint_dir, get_hyperparameters, get_input_data_dir,
    get_job_device_arn, get_job_name, get_results_dir
)

def get_job_device_arn() -> str:
    """
    Get quantum device ARN for current job.
    
    Returns:
        str: Device ARN assigned to job
    """
    pass

def get_job_name() -> str:
    """
    Get current job name.
    
    Returns:
        str: Job name
    """
    pass

def get_hyperparameters() -> dict:
    """
    Get job hyperparameters.
    
    Returns:
        dict: Hyperparameter dictionary
    """
    pass

def get_input_data_dir(channel: str = "training") -> str:
    """
    Get input data directory path.
    
    Args:
        channel: Data channel name
        
    Returns:
        str: Input data directory path
    """
    pass

def get_results_dir() -> str:
    """
    Get results output directory path.
    
    Returns:
        str: Results directory path
    """
    pass

def get_checkpoint_dir() -> str:
    """
    Get checkpoint directory path.
    
    Returns:
        str: Checkpoint directory path
    """
    pass

def save_job_checkpoint(checkpoint_data: dict) -> None:
    """
    Save job checkpoint data.
    
    Args:
        checkpoint_data: Data to checkpoint
    """
    pass

def load_job_checkpoint() -> dict:
    """
    Load job checkpoint data.
    
    Returns:
        dict: Loaded checkpoint data
    """
    pass

def save_job_result(result_data: dict) -> None:
    """
    Save job result data.
    
    Args:
        result_data: Results to save
    """
    pass

def load_job_result(job_arn: str) -> dict:
    """
    Load results from completed job.
    
    Args:
        job_arn: Job ARN
        
    Returns:
        dict: Job results
    """
    pass

# Job utilities example
def comprehensive_job_setup() -> dict:
    """
    Setup comprehensive job environment and configuration.
    
    Returns:
        dict: Job environment information
    """
    job_info = {
        'job_name': get_job_name(),
        'device_arn': get_job_device_arn(),
        'hyperparameters': get_hyperparameters(),
        'directories': {
            'input_data': get_input_data_dir(),
            'results': get_results_dir(),
            'checkpoints': get_checkpoint_dir()
        }
    }
    
    # Setup logging
    import logging
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
        handlers=[
            logging.FileHandler(f"{get_results_dir()}/job.log"),
            logging.StreamHandler()
        ]
    )
    
    logger = logging.getLogger('QuantumJob')
    logger.info(f"Starting job: {job_info['job_name']}")
    logger.info(f"Using device: {job_info['device_arn']}")
    
    return job_info

from braket.tracking import Tracker

class Tracker:
    """Tracks quantum computing costs and resource usage."""
    
    def __init__(self):
        """Initialize cost tracker."""
        pass
    
    def __enter__(self) -> 'Tracker':
        """Context manager entry."""
        return self
    
    def __exit__(self, exc_type, exc_val, exc_tb) -> None:
        """Context manager exit."""
        pass
    
    def quantum_tasks_cost(self) -> float:
        """
        Get total cost for quantum tasks.
        
        Returns:
            float: Total cost in USD
        """
        pass
    
    def simulator_tasks_cost(self) -> float:
        """
        Get total cost for simulator tasks.
        
        Returns:
            float: Total cost in USD
        """
        pass
    
    def quantum_task_statistics(self) -> dict:
        """
        Get detailed quantum task statistics.
        
        Returns:
            dict: Task execution statistics
        """
        pass
    
    def simulator_task_statistics(self) -> dict:
        """
        Get detailed simulator task statistics.
        
        Returns:
            dict: Simulator execution statistics
        """
        pass

def track_quantum_experiment_costs(experiment_function: callable, *args, **kwargs) -> dict:
    """
    Track costs for quantum experiment execution.
    
    Args:
        experiment_function: Function that runs quantum experiments
        *args: Arguments for experiment function
        **kwargs: Keyword arguments for experiment function
        
    Returns:
        dict: Cost analysis and experiment results
    """
    with Tracker() as tracker:
        # Run experiment
        results = experiment_function(*args, **kwargs)
        
        # Collect cost information
        cost_analysis = {
            'total_cost': tracker.quantum_tasks_cost() + tracker.simulator_tasks_cost(),
            'quantum_cost': tracker.quantum_tasks_cost(),
            'simulator_cost': tracker.simulator_tasks_cost(),
            'quantum_stats': tracker.quantum_task_statistics(),
            'simulator_stats': tracker.simulator_task_statistics(),
            'cost_per_shot': 0.0,
            'results': results
        }
        
        # Calculate cost per shot
        total_shots = (
            cost_analysis['quantum_stats'].get('shots', 0) +
            cost_analysis['simulator_stats'].get('shots', 0)
        )
        
        if total_shots > 0:
            cost_analysis['cost_per_shot'] = cost_analysis['total_cost'] / total_shots
        
    return cost_analysis

def optimize_experiment_for_cost(circuits: list, target_accuracy: float = 0.01) -> dict:
    """
    Optimize experiment execution for cost while maintaining accuracy.
    
    Args:
        circuits: List of quantum circuits to execute
        target_accuracy: Target measurement accuracy
        
    Returns:
        dict: Optimized execution plan with cost estimates
    """
    optimization_plan = {
        'execution_strategy': {},
        'estimated_costs': {},
        'recommendations': []
    }
    
    # Analyze circuits for optimization opportunities
    for i, circuit in enumerate(circuits):
        circuit_analysis = {
            'qubit_count': circuit.qubit_count,
            'depth': circuit.depth,
            'gate_count': len(circuit.instructions)
        }
        
        # Recommend device based on circuit characteristics
        if circuit.qubit_count <= 10 and circuit.depth <= 20:
            # Small circuit - use simulator
            recommended_device = "Local Simulator"
            estimated_cost = 0.0
            optimization_plan['recommendations'].append(
                f"Circuit {i}: Use local simulator (free) - small circuit"
            )
        elif circuit.qubit_count <= 20:
            # Medium circuit - use cloud simulator
            recommended_device = "AWS SV1"
            estimated_cost = 0.075 * (2 ** min(circuit.qubit_count, 17))
            optimization_plan['recommendations'].append(
                f"Circuit {i}: Use SV1 simulator - cost effective for {circuit.qubit_count} qubits"
            )
        else:
            # Large circuit - may need QPU or advanced simulator
            recommended_device = "AWS QPU or TN1"
            estimated_cost = 0.30 + (1000 * 0.0003)  # Task fee + shots
            optimization_plan['recommendations'].append(
                f"Circuit {i}: Consider QPU for {circuit.qubit_count} qubits"
            )
        
        optimization_plan['execution_strategy'][f'circuit_{i}'] = {
            'recommended_device': recommended_device,
            'estimated_cost': estimated_cost,
            'analysis': circuit_analysis
        }
    
    # Calculate total estimated cost
    total_cost = sum(
        strategy['estimated_cost'] 
        for strategy in optimization_plan['execution_strategy'].values()
    )
    optimization_plan['estimated_costs']['total'] = total_cost
    
    return optimization_plan

from braket.aws import DirectReservation

class DirectReservation:
    """Direct quantum device reservation interface."""
    
    def __init__(self, arn: str, aws_session: 'AwsSession' = None):
        """
        Initialize device reservation.
        
        Args:
            arn: Reservation ARN
            aws_session: AWS session for API calls
        """
        self.arn = arn
        self.aws_session = aws_session
    
    @property
    def device_arn(self) -> str:
        """Reserved device ARN."""
        pass
    
    @property
    def start_time(self) -> str:
        """Reservation start time."""
        pass
    
    @property
    def end_time(self) -> str:
        """Reservation end time."""
        pass
    
    @property
    def status(self) -> str:
        """Reservation status."""
        pass
    
    def cancel(self) -> None:
        """Cancel reservation."""
        pass

def create_device_reservation(device_arn: str, start_time: str, duration_minutes: int) -> DirectReservation:
    """
    Create direct device reservation.
    
    Args:
        device_arn: Target device ARN
        start_time: ISO format start time
        duration_minutes: Reservation duration in minutes
        
    Returns:
        DirectReservation: Device reservation handle
    """
    # This is a placeholder - actual implementation would use AWS API
    pass

def execute_with_reservation(reservation: DirectReservation, circuits: list) -> dict:
    """
    Execute circuits using device reservation.
    
    Args:
        reservation: Active device reservation
        circuits: Circuits to execute during reservation
        
    Returns:
        dict: Execution results and reservation utilization
    """
    device = AwsDevice(reservation.device_arn)
    
    results = []
    for circuit in circuits:
        task = device.run(circuit, shots=1000, reservation_arn=reservation.arn)
        results.append(task.result())
    
    return {
        'results': results,
        'reservation_info': {
            'device_arn': reservation.device_arn,
            'start_time': reservation.start_time,
            'end_time': reservation.end_time,
            'utilization': len(circuits)
        }
    }