Device Management and Local Simulation

from braket.devices import Device, Devices, LocalSimulator
from braket.emulation import Emulator, PassManager, ValidationPass
from braket.tasks import QuantumTask, QuantumTaskBatch
from braket.tasks import (
    GateModelQuantumTaskResult, AnalogHamiltonianSimulationQuantumTaskResult,
    AnnealingQuantumTaskResult, PhotonicModelQuantumTaskResult, ProgramSetQuantumTaskResult
)

from braket.devices import Device
from abc import ABC, abstractmethod

class Device(ABC):
    """Abstract base class for quantum devices."""
    
    @abstractmethod
    def run(
        self,
        task_specification,
        shots: int = 1000,
        **kwargs
    ) -> 'QuantumTask':
        """
        Execute quantum task on device.
        
        Args:
            task_specification: Circuit, AHS program, or annealing problem
            shots: Number of measurement shots
            **kwargs: Additional device-specific parameters
            
        Returns:
            QuantumTask: Task execution handle
        """
        pass
    
    @abstractmethod
    def run_batch(
        self,
        task_specifications: list,
        shots: int = 1000,
        max_parallel: int = 10,
        **kwargs
    ) -> 'QuantumTaskBatch':
        """
        Execute batch of quantum tasks.
        
        Args:
            task_specifications: List of task specifications
            shots: Number of measurement shots per task
            max_parallel: Maximum parallel executions
            **kwargs: Additional parameters
            
        Returns:
            QuantumTaskBatch: Batch execution handle
        """
        pass
    
    @property
    @abstractmethod
    def name(self) -> str:
        """Device name identifier."""
        pass
    
    @property
    @abstractmethod
    def status(self) -> str:
        """Current device status."""
        pass
    
    @property
    @abstractmethod
    def type(self) -> str:
        """Device type identifier."""
        pass
    
    @property
    @abstractmethod
    def provider_name(self) -> str:
        """Device provider name."""
        pass
    
    @property
    @abstractmethod
    def properties(self) -> dict:
        """Device capabilities and properties."""
        pass

class Devices:
    """Device enumeration utility."""
    
    @staticmethod
    def get_all() -> list[Device]:
        """
        Get all available devices.
        
        Returns:
            list[Device]: All available quantum devices
        """
        pass
    
    @staticmethod
    def get_by_type(device_type: str) -> list[Device]:
        """
        Get devices by type.
        
        Args:
            device_type: Device type filter
            
        Returns:
            list[Device]: Devices matching type
        """
        pass
    
    @staticmethod  
    def get_by_provider(provider: str) -> list[Device]:
        """
        Get devices by provider.
        
        Args:
            provider: Provider name filter
            
        Returns:
            list[Device]: Devices from provider
        """
        pass

from braket.devices import LocalSimulator
import numpy as np

class LocalSimulator(Device):
    """Local quantum circuit simulator."""
    
    def __init__(
        self,
        backend: str = "braket_sv",
        max_shots: int = 100000,
        seed: int = None
    ):
        """
        Initialize local simulator.
        
        Args:
            backend: Simulation backend ('braket_sv', 'braket_dm', 'braket_ahs')
            max_shots: Maximum number of shots per execution
            seed: Random seed for reproducible results
        """
        self.backend = backend
        self.max_shots = max_shots
        self.seed = seed
    
    def run(
        self,
        task_specification,
        shots: int = 1000,
        inputs: dict = None,
        **kwargs
    ) -> 'LocalQuantumTask':
        """
        Execute circuit on local simulator.
        
        Args:
            task_specification: Quantum circuit to execute
            shots: Number of measurement shots
            inputs: Parameter inputs for parameterized circuits
            **kwargs: Additional simulation parameters
            
        Returns:
            LocalQuantumTask: Local task execution
        """
        pass
    
    def run_batch(
        self,
        task_specifications: list,
        shots: int = 1000,
        max_parallel: int = None,
        inputs: list[dict] = None,
        **kwargs
    ) -> 'LocalQuantumTaskBatch':
        """
        Execute batch of circuits locally.
        
        Args:
            task_specifications: List of quantum circuits
            shots: Number of shots per circuit
            max_parallel: Parallel execution limit (uses CPU cores)
            inputs: Parameter inputs for each circuit
            **kwargs: Additional parameters
            
        Returns:
            LocalQuantumTaskBatch: Batch execution handle
        """
        pass
    
    @property
    def name(self) -> str:
        """Simulator name."""
        return f"LocalSimulator({self.backend})"
    
    @property
    def status(self) -> str:
        """Simulator status (always ONLINE for local)."""
        return "ONLINE"
    
    @property
    def type(self) -> str:
        """Device type."""
        return "SIMULATOR"
    
    @property
    def provider_name(self) -> str:
        """Provider name."""
        return "Local"
    
    @property
    def properties(self) -> dict:
        """Simulator capabilities."""
        return {
            "backend": self.backend,
            "max_shots": self.max_shots,
            "max_qubits": 25 if self.backend == "braket_sv" else 12,
            "supported_operations": self._get_supported_operations(),
            "supports_noise": self.backend == "braket_dm"
        }
    
    def _get_supported_operations(self) -> list[str]:
        """Get list of supported quantum operations."""
        if self.backend == "braket_sv":
            return ["H", "X", "Y", "Z", "S", "T", "Rx", "Ry", "Rz", "CNot", "CCNot", "Unitary"]
        elif self.backend == "braket_dm":
            return ["H", "X", "Y", "Z", "S", "T", "Rx", "Ry", "Rz", "CNot", "Noise"]
        else:
            return []

# Local simulation examples
def simulate_circuit_locally(circuit, shots: int = 1000, backend: str = "braket_sv") -> dict:
    """
    Simulate quantum circuit using local resources.
    
    Args:
        circuit: Quantum circuit to simulate
        shots: Number of measurement shots
        backend: Local simulation backend
        
    Returns:
        dict: Simulation results and performance metrics
    """
    import time
    
    simulator = LocalSimulator(backend=backend)
    
    start_time = time.time()
    task = simulator.run(circuit, shots=shots)
    result = task.result()
    end_time = time.time()
    
    return {
        'measurement_counts': result.measurement_counts,
        'measurement_probabilities': result.measurement_probabilities,
        'execution_time': end_time - start_time,
        'simulator_info': {
            'backend': backend,
            'shots': shots,
            'qubit_count': circuit.qubit_count,
            'circuit_depth': circuit.depth
        }
    }

def compare_simulation_backends(circuit) -> dict:
    """
    Compare different local simulation backends.
    
    Args:
        circuit: Circuit to simulate across backends
        
    Returns:
        dict: Performance comparison across backends
    """
    backends = ["braket_sv", "braket_dm"]
    comparison = {}
    
    for backend in backends:
        try:
            result = simulate_circuit_locally(circuit, shots=1000, backend=backend)
            comparison[backend] = {
                'execution_time': result['execution_time'],
                'memory_usage': 'Unknown',  # Would need profiling
                'accuracy': 'Exact' if backend == "braket_sv" else 'Approximate',
                'noise_support': backend == "braket_dm",
                'results': result['measurement_counts']
            }
        except Exception as e:
            comparison[backend] = {'error': str(e)}
    
    return comparison

def optimize_local_simulation(circuit, target_accuracy: float = 0.01) -> dict:
    """
    Optimize local simulation for best performance/accuracy tradeoff.
    
    Args:
        circuit: Circuit to optimize simulation for
        target_accuracy: Target measurement accuracy
        
    Returns:
        dict: Optimized simulation configuration
    """
    optimization = {
        'recommended_backend': 'braket_sv',
        'recommended_shots': 1000,
        'expected_time': 0.0,
        'expected_memory': 0,
        'rationale': []
    }
    
    # Analyze circuit characteristics
    qubit_count = circuit.qubit_count
    circuit_depth = circuit.depth
    has_noise = any(hasattr(inst.operator, 'noise') for inst in circuit.instructions)
    
    # Backend recommendation
    if has_noise:
        optimization['recommended_backend'] = 'braket_dm'
        optimization['rationale'].append("Circuit contains noise - using density matrix simulator")
    elif qubit_count > 20:
        optimization['recommended_backend'] = 'braket_dm'  
        optimization['rationale'].append(f"{qubit_count} qubits - density matrix more memory efficient")
    else:
        optimization['recommended_backend'] = 'braket_sv'
        optimization['rationale'].append("State vector simulator for exact results")
    
    # Shot count optimization
    if target_accuracy <= 0.001:
        optimization['recommended_shots'] = 100000
    elif target_accuracy <= 0.01:
        optimization['recommended_shots'] = 10000
    else:
        optimization['recommended_shots'] = 1000
    
    optimization['rationale'].append(f"Shots: {optimization['recommended_shots']} for {target_accuracy} accuracy")
    
    # Performance estimates
    if optimization['recommended_backend'] == 'braket_sv':
        optimization['expected_memory'] = 2 ** (qubit_count + 3)  # 8 bytes per amplitude
        optimization['expected_time'] = circuit_depth * 0.001 * (2 ** qubit_count)
    else:
        optimization['expected_memory'] = 2 ** (2 * qubit_count + 4)  # Density matrix
        optimization['expected_time'] = circuit_depth * 0.002 * (2 ** (2 * qubit_count))
    
    return optimization

from braket.tasks import QuantumTask

class LocalQuantumTask(QuantumTask):
    """Local quantum task execution."""
    
    def __init__(self, task_specification, shots: int, simulator: LocalSimulator):
        """
        Initialize local quantum task.
        
        Args:
            task_specification: Circuit or program to execute
            shots: Number of measurement shots
            simulator: Local simulator instance
        """
        self.task_specification = task_specification
        self.shots = shots
        self.simulator = simulator
        self._result = None
        self._state = "CREATED"
    
    def result(self) -> 'GateModelQuantumTaskResult':
        """
        Get task execution results.
        
        Returns:
            GateModelQuantumTaskResult: Task results
        """
        if self._result is None:
            self._execute()
        return self._result
    
    def state(self) -> str:
        """
        Get current task state.
        
        Returns:
            str: Task state (CREATED, RUNNING, COMPLETED, FAILED)
        """
        return self._state
    
    def _execute(self) -> None:
        """Execute the quantum task locally."""
        import time
        import numpy as np
        
        self._state = "RUNNING"
        start_time = time.time()
        
        try:
            # Simulate circuit execution
            if hasattr(self.task_specification, 'instructions'):
                # Quantum circuit
                circuit = self.task_specification
                
                # Basic state vector simulation (simplified)
                n_qubits = circuit.qubit_count
                state_vector = np.zeros(2**n_qubits, dtype=complex)
                state_vector[0] = 1.0  # |00...0⟩ initial state
                
                # Apply gates (simplified implementation)
                for instruction in circuit.instructions:
                    gate = instruction.operator
                    targets = instruction.target
                    # Gate application logic would go here
                
                # Sample measurements
                probabilities = np.abs(state_vector) ** 2
                samples = np.random.choice(
                    len(probabilities), 
                    size=self.shots, 
                    p=probabilities
                )
                
                # Count measurements
                measurement_counts = {}
                for sample in samples:
                    bitstring = format(sample, f'0{n_qubits}b')
                    measurement_counts[bitstring] = measurement_counts.get(bitstring, 0) + 1
                
                self._result = GateModelQuantumTaskResult(
                    measurement_counts=measurement_counts,
                    measurement_probabilities={
                        k: v/self.shots for k, v in measurement_counts.items()
                    },
                    task_metadata={
                        'shots': self.shots,
                        'execution_duration': time.time() - start_time,
                        'simulator_backend': self.simulator.backend
                    }
                )
                
            self._state = "COMPLETED"
            
        except Exception as e:
            self._state = "FAILED"
            self._result = {'error': str(e)}

class LocalQuantumTaskBatch:
    """Batch execution of local quantum tasks."""
    
    def __init__(self, tasks: list[LocalQuantumTask]):
        """
        Initialize local task batch.
        
        Args:
            tasks: List of local quantum tasks
        """
        self.tasks = tasks
    
    def results(self) -> list:
        """
        Get results from all tasks in batch.
        
        Returns:
            list: Results from all tasks
        """
        return [task.result() for task in self.tasks]
    
    def unsuccessful(self) -> list[LocalQuantumTask]:
        """
        Get unsuccessful tasks.
        
        Returns:
            list[LocalQuantumTask]: Failed tasks
        """
        return [task for task in self.tasks if task.state() == "FAILED"]

def execute_local_batch_efficiently(circuits: list, shots: int = 1000) -> dict:
    """
    Execute batch of circuits with optimal local resource utilization.
    
    Args:
        circuits: List of quantum circuits
        shots: Number of shots per circuit
        
    Returns:
        dict: Batch execution results with performance analytics
    """
    import multiprocessing
    import time
    from concurrent.futures import ThreadPoolExecutor
    
    simulator = LocalSimulator()
    start_time = time.time()
    
    # Determine optimal parallelization
    cpu_count = multiprocessing.cpu_count()
    max_parallel = min(len(circuits), cpu_count)
    
    batch_results = {
        'results': [],
        'execution_stats': {
            'total_circuits': len(circuits),
            'parallel_workers': max_parallel,
            'total_execution_time': 0,
            'average_time_per_circuit': 0,
            'throughput_circuits_per_second': 0
        }
    }
    
    # Execute circuits in parallel
    with ThreadPoolExecutor(max_workers=max_parallel) as executor:
        tasks = [
            executor.submit(simulator.run, circuit, shots)
            for circuit in circuits
        ]
        
        for i, task in enumerate(tasks):
            result = task.result().result()
            batch_results['results'].append({
                'circuit_index': i,
                'measurement_counts': result.measurement_counts,
                'measurement_probabilities': result.measurement_probabilities
            })
    
    # Calculate performance statistics
    end_time = time.time()
    total_time = end_time - start_time
    
    batch_results['execution_stats']['total_execution_time'] = total_time
    batch_results['execution_stats']['average_time_per_circuit'] = total_time / len(circuits)
    batch_results['execution_stats']['throughput_circuits_per_second'] = len(circuits) / total_time
    
    return batch_results

from braket.emulation import Emulator, PassManager, ValidationPass

class Emulator:
    """Quantum circuit emulator with compilation passes."""
    
    def __init__(
        self,
        pass_manager: PassManager = None,
        target_device: Device = None
    ):
        """
        Initialize quantum circuit emulator.
        
        Args:
            pass_manager: Compilation pass manager
            target_device: Target device for emulation properties
        """
        self.pass_manager = pass_manager or PassManager()
        self.target_device = target_device
    
    def emulate(
        self,
        circuit,
        shots: int = 1000,
        inputs: dict = None
    ) -> 'EmulationResult':
        """
        Emulate quantum circuit with compilation.
        
        Args:
            circuit: Quantum circuit to emulate
            shots: Number of measurement shots
            inputs: Input parameters for parameterized circuits
            
        Returns:
            EmulationResult: Emulation results with compilation info
        """
        pass
    
    def add_pass(self, pass_instance) -> 'Emulator':
        """
        Add compilation pass to emulator.
        
        Args:
            pass_instance: Compilation pass to add
            
        Returns:
            Emulator: Self for method chaining
        """
        self.pass_manager.add_pass(pass_instance)
        return self

class PassManager:
    """Compilation pass management for quantum circuits."""
    
    def __init__(self):
        """Initialize empty pass manager."""
        self.passes = []
    
    def add_pass(self, pass_instance) -> 'PassManager':
        """
        Add compilation pass.
        
        Args:
            pass_instance: Pass to add
            
        Returns:
            PassManager: Self for method chaining
        """
        self.passes.append(pass_instance)
        return self
    
    def run(self, circuit) -> 'Circuit':
        """
        Run all passes on circuit.
        
        Args:
            circuit: Input quantum circuit
            
        Returns:
            Circuit: Compiled quantum circuit
        """
        compiled_circuit = circuit
        for pass_instance in self.passes:
            compiled_circuit = pass_instance.run(compiled_circuit)
        return compiled_circuit

class ValidationPass:
    """Circuit validation compilation pass."""
    
    def __init__(self, target_device: Device = None):
        """
        Initialize validation pass.
        
        Args:
            target_device: Target device for validation
        """
        self.target_device = target_device
    
    def run(self, circuit) -> 'Circuit':
        """
        Validate circuit against device constraints.
        
        Args:
            circuit: Circuit to validate
            
        Returns:
            Circuit: Validated circuit (may raise ValidationError)
        """
        pass

# Emulation examples
def create_realistic_emulator(target_device_properties: dict) -> Emulator:
    """
    Create emulator that mimics real device behavior.
    
    Args:
        target_device_properties: Properties of target quantum device
        
    Returns:
        Emulator: Configured emulator with realistic behavior
    """
    pass_manager = PassManager()
    
    # Add validation pass for device constraints
    validation_pass = ValidationPass()
    pass_manager.add_pass(validation_pass)
    
    # Add device-specific compilation passes
    if 'connectivity' in target_device_properties:
        # Add SWAP insertion pass for limited connectivity
        # swap_pass = SwapInsertionPass(target_device_properties['connectivity'])
        # pass_manager.add_pass(swap_pass)
        pass
    
    if 'native_gates' in target_device_properties:
        # Add gate decomposition pass
        # decomposition_pass = GateDecompositionPass(target_device_properties['native_gates'])
        # pass_manager.add_pass(decomposition_pass)
        pass
    
    emulator = Emulator(pass_manager=pass_manager)
    return emulator

def benchmark_emulation_vs_simulation(circuit) -> dict:
    """
    Benchmark emulation vs direct simulation performance.
    
    Args:
        circuit: Circuit to benchmark
        
    Returns:
        dict: Performance comparison results
    """
    import time
    
    benchmark_results = {
        'circuit_info': {
            'qubit_count': circuit.qubit_count,
            'depth': circuit.depth,
            'gate_count': len(circuit.instructions)
        },
        'simulation': {},
        'emulation': {}
    }
    
    # Direct simulation
    simulator = LocalSimulator()
    start_time = time.time()
    sim_task = simulator.run(circuit, shots=1000)
    sim_result = sim_task.result()
    sim_time = time.time() - start_time
    
    benchmark_results['simulation'] = {
        'execution_time': sim_time,
        'compilation_time': 0,
        'result_counts': sim_result.measurement_counts
    }
    
    # Emulation with compilation
    emulator = Emulator()
    start_time = time.time()
    compile_start = time.time()
    compiled_circuit = emulator.pass_manager.run(circuit)
    compile_time = time.time() - compile_start
    
    emu_result = emulator.emulate(compiled_circuit, shots=1000)
    total_emu_time = time.time() - start_time
    
    benchmark_results['emulation'] = {
        'execution_time': total_emu_time,
        'compilation_time': compile_time,
        'optimizations_applied': len(emulator.pass_manager.passes),
        'compiled_circuit_depth': compiled_circuit.depth
    }
    
    # Performance analysis
    benchmark_results['analysis'] = {
        'speedup_factor': sim_time / total_emu_time if total_emu_time > 0 else float('inf'),
        'compilation_overhead': compile_time / total_emu_time if total_emu_time > 0 else 0,
        'depth_reduction': (circuit.depth - compiled_circuit.depth) / circuit.depth if circuit.depth > 0 else 0
    }
    
    return benchmark_results

from braket.tasks import (
    GateModelQuantumTaskResult, AnalogHamiltonianSimulationQuantumTaskResult,
    AnnealingQuantumTaskResult, PhotonicModelQuantumTaskResult, ProgramSetQuantumTaskResult
)

class GateModelQuantumTaskResult:
    """Results from gate-model quantum circuit execution."""
    
    def __init__(
        self,
        measurement_counts: dict = None,
        measurement_probabilities: dict = None,
        values: list = None,
        measured_qubits: list = None,
        task_metadata: dict = None
    ):
        """
        Initialize gate model task result.
        
        Args:
            measurement_counts: Raw measurement count data
            measurement_probabilities: Measurement probabilities
            values: Observable expectation values
            measured_qubits: List of measured qubit indices
            task_metadata: Task execution metadata
        """
        self.measurement_counts = measurement_counts or {}
        self.measurement_probabilities = measurement_probabilities or {}
        self.values = values or []
        self.measured_qubits = measured_qubits or []
        self.task_metadata = task_metadata or {}
    
    def get_counts(self) -> dict:
        """
        Get measurement counts.
        
        Returns:
            dict: Measurement counts by bitstring
        """
        return self.measurement_counts
    
    def get_probabilities(self) -> dict:
        """
        Get measurement probabilities.
        
        Returns:
            dict: Measurement probabilities by bitstring
        """
        return self.measurement_probabilities

class AnalogHamiltonianSimulationQuantumTaskResult:
    """Results from analog Hamiltonian simulation tasks."""
    
    def __init__(
        self,
        pre_sequence: list = None,
        post_sequence: list = None,
        task_metadata: dict = None
    ):
        """
        Initialize AHS task result.
        
        Args:
            pre_sequence: Pre-evolution measurements
            post_sequence: Post-evolution measurements  
            task_metadata: Task execution metadata
        """
        self.pre_sequence = pre_sequence or []
        self.post_sequence = post_sequence or []
        self.task_metadata = task_metadata or {}

class AnnealingQuantumTaskResult:
    """Results from quantum annealing tasks."""
    
    def __init__(
        self,
        record_array: list = None,
        variable_count: int = 0,
        task_metadata: dict = None
    ):
        """
        Initialize annealing task result.
        
        Args:
            record_array: Annealing solution records
            variable_count: Number of problem variables
            task_metadata: Task execution metadata
        """
        self.record_array = record_array or []
        self.variable_count = variable_count
        self.task_metadata = task_metadata or {}

class PhotonicModelQuantumTaskResult:
    """Results from photonic quantum computing tasks."""
    
    def __init__(
        self,
        measurements: list = None,
        task_metadata: dict = None
    ):
        """
        Initialize photonic task result.
        
        Args:
            measurements: Photonic measurement data
            task_metadata: Task execution metadata
        """
        self.measurements = measurements or []
        self.task_metadata = task_metadata or {}

class ProgramSetQuantumTaskResult:
    """Results from program set execution tasks."""
    
    def __init__(
        self,
        results: list = None,
        task_metadata: dict = None
    ):
        """
        Initialize program set task result.
        
        Args:
            results: List of individual program results
            task_metadata: Task execution metadata
        """
        self.results = results or []
        self.task_metadata = task_metadata or {}
    
    def get_result(self, index: int):
        """
        Get result for specific program in set.
        
        Args:
            index: Program index in set
            
        Returns:
            Individual program result
        """
        if 0 <= index < len(self.results):
            return self.results[index]
        raise IndexError(f"Program index {index} out of range")
    
    def get_all_results(self) -> list:
        """
        Get all program results.
        
        Returns:
            list: All program execution results
        """
        return self.results

def analyze_task_results(result) -> dict:
    """
    Analyze quantum task results for insights.
    
    Args:
        result: Quantum task result object
        
    Returns:
        dict: Comprehensive result analysis
    """
    analysis = {
        'result_type': type(result).__name__,
        'execution_info': result.task_metadata,
        'statistics': {}
    }
    
    if isinstance(result, GateModelQuantumTaskResult):
        # Gate model analysis
        counts = result.get_counts()
        total_shots = sum(counts.values())
        
        analysis['statistics'] = {
            'total_shots': total_shots,
            'unique_outcomes': len(counts),
            'most_probable': max(counts.items(), key=lambda x: x[1]),
            'entropy': calculate_shannon_entropy(counts),
            'uniformity': len(counts) / (2 ** len(list(counts.keys())[0]))
        }
        
    elif isinstance(result, AnnealingQuantumTaskResult):
        # Annealing analysis
        analysis['statistics'] = {
            'solution_count': len(result.record_array),
            'variable_count': result.variable_count,
            'unique_solutions': len(set(tuple(record) for record in result.record_array))
        }
    
    elif isinstance(result, ProgramSetQuantumTaskResult):
        # Program set analysis
        analysis['statistics'] = {
            'program_count': len(result.results),
            'total_executions': len(result.results),
            'batch_execution': True,
            'individual_results_available': True
        }
    
    return analysis

def calculate_shannon_entropy(counts: dict) -> float:
    """
    Calculate Shannon entropy of measurement distribution.
    
    Args:
        counts: Measurement counts dictionary
        
    Returns:
        float: Shannon entropy in bits
    """
    import math
    
    total_counts = sum(counts.values())
    if total_counts == 0:
        return 0.0
    
    entropy = 0.0
    for count in counts.values():
        if count > 0:
            prob = count / total_counts
            entropy -= prob * math.log2(prob)
    
    return entropy

def optimize_simulation_performance(circuits: list) -> dict:
    """
    Optimize quantum simulation performance for circuit collection.
    
    Args:
        circuits: List of quantum circuits to optimize
        
    Returns:
        dict: Optimization recommendations and performance estimates
    """
    optimization_report = {
        'circuit_analysis': [],
        'recommendations': [],
        'performance_estimates': {},
        'resource_requirements': {}
    }
    
    total_qubits = 0
    max_qubits = 0
    total_depth = 0
    
    for i, circuit in enumerate(circuits):
        circuit_info = {
            'index': i,
            'qubit_count': circuit.qubit_count,
            'depth': circuit.depth,
            'gate_count': len(circuit.instructions),
            'recommended_backend': 'braket_sv'
        }
        
        # Analyze circuit characteristics
        if circuit.qubit_count > 15:
            circuit_info['recommended_backend'] = 'braket_dm'
            optimization_report['recommendations'].append(
                f"Circuit {i}: Use density matrix simulator for {circuit.qubit_count} qubits"
            )
        
        # Check for noise
        has_noise = any(hasattr(inst.operator, 'noise') for inst in circuit.instructions)
        if has_noise:
            circuit_info['recommended_backend'] = 'braket_dm'
            optimization_report['recommendations'].append(
                f"Circuit {i}: Noise detected, using density matrix simulator"
            )
        
        optimization_report['circuit_analysis'].append(circuit_info)
        
        total_qubits += circuit.qubit_count
        max_qubits = max(max_qubits, circuit.qubit_count)
        total_depth += circuit.depth
    
    # Resource requirement estimates
    optimization_report['resource_requirements'] = {
        'max_memory_gb': estimate_memory_requirements(max_qubits, 'braket_sv'),
        'estimated_total_time_seconds': estimate_execution_time(circuits),
        'recommended_parallel_jobs': min(len(circuits), 8),
        'storage_requirements_mb': estimate_storage_requirements(circuits)
    }
    
    # Performance optimization recommendations
    optimization_report['recommendations'].extend([
        "Use parallel execution for independent circuits",
        "Consider circuit compression for repeated patterns",
        "Implement result caching for identical circuits",
        f"Batch similar circuits (max {max_qubits} qubits detected)"
    ])
    
    return optimization_report

def estimate_memory_requirements(qubit_count: int, backend: str) -> float:
    """
    Estimate memory requirements for quantum simulation.
    
    Args:
        qubit_count: Number of qubits in circuit
        backend: Simulation backend
        
    Returns:
        float: Estimated memory requirement in GB
    """
    if backend == 'braket_sv':
        # State vector: 2^n complex numbers, 16 bytes each
        return (2 ** qubit_count) * 16 / (1024 ** 3)
    elif backend == 'braket_dm':
        # Density matrix: 2^(2n) complex numbers, 16 bytes each
        return (2 ** (2 * qubit_count)) * 16 / (1024 ** 3)
    else:
        return 0.1  # Default estimate

def estimate_execution_time(circuits: list) -> float:
    """
    Estimate total execution time for circuit collection.
    
    Args:
        circuits: List of circuits to analyze
        
    Returns:
        float: Estimated execution time in seconds
    """
    total_time = 0.0
    
    for circuit in circuits:
        # Time estimation based on circuit complexity
        qubit_factor = 2 ** min(circuit.qubit_count, 20)
        depth_factor = circuit.depth
        gate_factor = len(circuit.instructions)
        
        # Empirical formula (would need benchmarking to calibrate)
        circuit_time = (qubit_factor * depth_factor * 0.001) + (gate_factor * 0.0001)
        total_time += circuit_time
    
    return total_time

def estimate_storage_requirements(circuits: list) -> float:
    """
    Estimate storage requirements for simulation results.
    
    Args:
        circuits: List of circuits
        
    Returns:
        float: Estimated storage in MB
    """
    storage_mb = 0.0
    
    for circuit in circuits:
        # Estimate result size based on measurement outcomes
        max_outcomes = 2 ** circuit.qubit_count
        # Assume JSON storage with reasonable overhead
        circuit_storage = max_outcomes * 50 / 1024 / 1024  # 50 bytes per outcome
        storage_mb += circuit_storage
    
    return storage_mb

def create_performance_optimized_simulator(circuit_characteristics: dict) -> LocalSimulator:
    """
    Create optimally configured simulator based on circuit characteristics.
    
    Args:
        circuit_characteristics: Analysis of circuits to be executed
        
    Returns:
        LocalSimulator: Optimally configured simulator
    """
    max_qubits = circuit_characteristics.get('max_qubits', 10)
    has_noise = circuit_characteristics.get('has_noise', False)
    total_circuits = circuit_characteristics.get('circuit_count', 1)
    
    # Select optimal backend
    if has_noise:
        backend = 'braket_dm'
    elif max_qubits > 18:
        backend = 'braket_dm'  # Memory efficiency for large systems
    else:
        backend = 'braket_sv'  # Accuracy for smaller systems
    
    # Configure shot limits based on workload
    if total_circuits > 100:
        max_shots = 10000  # Lower per-circuit limit for large batches
    else:
        max_shots = 100000  # Higher limit for smaller batches
    
    return LocalSimulator(
        backend=backend,
        max_shots=max_shots,
        seed=42  # Reproducible results
    )