Advanced Quantum Computing Features

# Analog Hamiltonian Simulation
from braket.ahs import (
    AnalogHamiltonianSimulation, AtomArrangement, AtomArrangementItem, SiteType,
    Canvas, DiscretizationProperties, DrivingField, Field, Hamiltonian,
    LocalDetuning, Pattern, ShiftingField
)

# Quantum Annealing
from braket.annealing import Problem, ProblemType

# Pulse Control
from braket.pulse import (
    Frame, Port, PulseSequence, ArbitraryWaveform, ConstantWaveform,
    DragGaussianWaveform, ErfSquareWaveform, GaussianWaveform
)

# Error Mitigation
from braket.error_mitigation import Debias, ErrorMitigation

# Experimental Capabilities
from braket.experimental_capabilities import EnableExperimentalCapability

from braket.ahs import AnalogHamiltonianSimulation, Hamiltonian, AtomArrangement
import numpy as np

class AnalogHamiltonianSimulation:
    """Main AHS program for neutral atom quantum computing."""
    
    def __init__(self):
        """Initialize empty AHS program."""
        self.hamiltonian = None
        self.atom_arrangement = None
        self.discretization_properties = None
    
    @classmethod
    def create(
        cls,
        atom_arrangement: 'AtomArrangement',
        hamiltonian: 'Hamiltonian',
        discretization_properties: 'DiscretizationProperties' = None
    ) -> 'AnalogHamiltonianSimulation':
        """
        Create AHS program with atom arrangement and Hamiltonian.
        
        Args:
            atom_arrangement: Spatial arrangement of neutral atoms
            hamiltonian: Time-dependent Hamiltonian specification
            discretization_properties: Time discretization settings
            
        Returns:
            AnalogHamiltonianSimulation: Complete AHS program
        """
        pass
    
    def to_ir(self) -> dict:
        """
        Convert to intermediate representation for device execution.
        
        Returns:
            dict: AHS program in device-compatible format
        """
        pass

class AtomArrangement:
    """Atom position arrangement for AHS."""
    
    def __init__(self):
        """Initialize empty atom arrangement."""
        self.sites = []
    
    def add(self, coordinate: tuple[float, float], site_type: 'SiteType' = None) -> 'AtomArrangement':
        """
        Add atom site to arrangement.
        
        Args:
            coordinate: (x, y) position in micrometers
            site_type: Type of site (filled or vacant)
            
        Returns:
            AtomArrangement: Self for method chaining
        """
        pass
    
    def add_item(self, item: 'AtomArrangementItem') -> 'AtomArrangement':
        """
        Add pre-configured arrangement item.
        
        Args:
            item: Atom arrangement item
            
        Returns:
            AtomArrangement: Self for method chaining
        """
        pass
    
    @property
    def coordinate_list(self) -> list[tuple[float, float]]:
        """Get list of atom coordinates."""
        pass
    
    def visualize(self) -> 'Canvas':
        """
        Create visualization of atom arrangement.
        
        Returns:
            Canvas: Visual representation of atom layout
        """
        pass

class AtomArrangementItem:
    """Individual atom arrangement item."""
    
    def __init__(self, coordinate: tuple[float, float], site_type: 'SiteType'):
        """
        Initialize atom arrangement item.
        
        Args:
            coordinate: (x, y) position in micrometers
            site_type: Type of site
        """
        self.coordinate = coordinate
        self.site_type = site_type

class SiteType:
    """Site type enumeration for atom arrangements."""
    
    FILLED = "filled"
    VACANT = "vacant"

# AHS program examples
def create_rydberg_blockade_program(lattice_spacing: float = 5.0) -> AnalogHamiltonianSimulation:
    """
    Create AHS program for Rydberg blockade physics.
    
    Args:
        lattice_spacing: Spacing between atoms in micrometers
        
    Returns:
        AnalogHamiltonianSimulation: Rydberg blockade AHS program
    """
    # Create square lattice of atoms
    arrangement = AtomArrangement()
    n_atoms_per_side = 3
    
    for i in range(n_atoms_per_side):
        for j in range(n_atoms_per_side):
            x = i * lattice_spacing
            y = j * lattice_spacing
            arrangement.add((x, y), SiteType.FILLED)
    
    # Create time-dependent Hamiltonian
    # H = Ω(t)/2 ∑ᵢ (σˣⁱ) - Δ(t) ∑ᵢ nᵢ + ∑ᵢⱼ V_ij nᵢnⱼ
    hamiltonian = create_rydberg_hamiltonian()
    
    # Time evolution settings
    discretization = DiscretizationProperties(
        time_step=1e-7,  # 0.1 microseconds
        time_series_precision=1e-9
    )
    
    return AnalogHamiltonianSimulation.create(
        arrangement, hamiltonian, discretization
    )

def create_quantum_annealing_ahs_program() -> AnalogHamiltonianSimulation:
    """
    Create AHS program for quantum annealing-like evolution.
    
    Returns:
        AnalogHamiltonianSimulation: Quantum annealing AHS program
    """
    # Linear chain for transverse-field Ising model
    arrangement = AtomArrangement()
    chain_length = 5
    spacing = 8.0  # micrometers
    
    for i in range(chain_length):
        arrangement.add((i * spacing, 0.0), SiteType.FILLED)
    
    # Create annealing schedule
    total_time = 2.0  # microseconds
    
    # Driving field (transverse field analog)
    driving_amplitude = create_annealing_schedule(
        initial_value=2*np.pi * 10,  # MHz
        final_value=0.0,
        total_time=total_time
    )
    
    # Detuning (longitudinal field)
    detuning = create_annealing_schedule(
        initial_value=0.0,
        final_value=2*np.pi * 5,  # MHz
        total_time=total_time
    )
    
    hamiltonian = Hamiltonian()
    hamiltonian.driving_field = DrivingField(
        amplitude=driving_amplitude,
        phase=Pattern.constant(0.0),
        detuning=detuning
    )
    
    return AnalogHamiltonianSimulation.create(arrangement, hamiltonian)

def analyze_ahs_program(ahs_program: AnalogHamiltonianSimulation) -> dict:
    """
    Analyze AHS program for physics insights and optimization.
    
    Args:
        ahs_program: AHS program to analyze
        
    Returns:
        dict: Comprehensive program analysis
    """
    analysis = {
        'atom_configuration': {},
        'hamiltonian_properties': {},
        'time_evolution': {},
        'physics_regime': {},
        'computational_complexity': {}
    }
    
    # Analyze atom arrangement
    coords = ahs_program.atom_arrangement.coordinate_list
    n_atoms = len(coords)
    
    analysis['atom_configuration'] = {
        'atom_count': n_atoms,
        'spatial_dimension': 2,  # 2D arrangements
        'lattice_spacing': calculate_average_spacing(coords),
        'geometry': identify_lattice_geometry(coords),
        'coordination_number': calculate_coordination_numbers(coords)
    }
    
    # Analyze Hamiltonian
    hamiltonian = ahs_program.hamiltonian
    analysis['hamiltonian_properties'] = {
        'has_driving_field': hamiltonian.driving_field is not None,
        'has_local_detuning': hasattr(hamiltonian, 'local_detuning') and hamiltonian.local_detuning is not None,
        'interaction_range': estimate_interaction_range(coords),
        'rydberg_blockade_radius': calculate_blockade_radius(coords)
    }
    
    # Physics regime analysis
    if analysis['hamiltonian_properties']['has_driving_field']:
        analysis['physics_regime']['type'] = 'Rydberg blockade'
        analysis['physics_regime']['phenomena'] = [
            'Quantum many-body scarring',
            'Rydberg crystallization',
            'Quantum phase transitions'
        ]
    else:
        analysis['physics_regime']['type'] = 'Ising-like'
        analysis['physics_regime']['phenomena'] = [
            'Ground state optimization',
            'Quantum annealing',
            'Combinatorial optimization'
        ]
    
    # Computational complexity
    hilbert_space_size = 2 ** n_atoms  # Each atom can be |0⟩ or |r⟩
    analysis['computational_complexity'] = {
        'hilbert_space_size': hilbert_space_size,
        'classical_simulation_feasible': n_atoms <= 20,
        'approximate_methods_needed': n_atoms > 15,
        'estimated_execution_time': estimate_ahs_execution_time(ahs_program)
    }
    
    return analysis

def calculate_average_spacing(coordinates: list[tuple[float, float]]) -> float:
    """Calculate average nearest-neighbor spacing."""
    if len(coordinates) < 2:
        return 0.0
    
    total_distance = 0.0
    count = 0
    
    for i, (x1, y1) in enumerate(coordinates):
        min_distance = float('inf')
        for j, (x2, y2) in enumerate(coordinates):
            if i != j:
                distance = np.sqrt((x2-x1)**2 + (y2-y1)**2)
                min_distance = min(min_distance, distance)
        
        if min_distance < float('inf'):
            total_distance += min_distance
            count += 1
    
    return total_distance / count if count > 0 else 0.0

from braket.ahs import Hamiltonian, DrivingField, LocalDetuning, Pattern
from braket.timings import TimeSeries

class Hamiltonian:
    """AHS Hamiltonian specification."""
    
    def __init__(self):
        """Initialize empty Hamiltonian."""
        self.driving_field = None
        self.local_detuning = None
    
    def add_driving_field(self, driving_field: 'DrivingField') -> 'Hamiltonian':
        """
        Add global driving field term.
        
        Args:
            driving_field: Global Rabi driving field
            
        Returns:
            Hamiltonian: Self for method chaining
        """
        self.driving_field = driving_field
        return self
    
    def add_local_detuning(self, local_detuning: 'LocalDetuning') -> 'Hamiltonian':
        """
        Add local detuning field.
        
        Args:
            local_detuning: Site-dependent detuning field
            
        Returns:
            Hamiltonian: Self for method chaining
        """
        self.local_detuning = local_detuning
        return self

class DrivingField(Field):
    """Hamiltonian driving field for Rabi oscillations."""
    
    def __init__(
        self,
        amplitude: 'Pattern',
        phase: 'Pattern',
        detuning: 'Pattern'
    ):
        """
        Initialize driving field.
        
        Args:
            amplitude: Time-dependent Rabi frequency Ω(t)
            phase: Time-dependent phase φ(t)
            detuning: Time-dependent detuning Δ(t)
        """
        self.amplitude = amplitude
        self.phase = phase
        self.detuning = detuning

class LocalDetuning(Field):
    """Local detuning field for site-dependent energy shifts."""
    
    def __init__(self, magnitude: 'Pattern'):
        """
        Initialize local detuning field.
        
        Args:
            magnitude: Spatially-dependent detuning magnitude
        """
        self.magnitude = magnitude

class Pattern:
    """Time-dependent pattern for AHS fields."""
    
    @staticmethod
    def constant(value: float) -> 'Pattern':
        """
        Create constant pattern.
        
        Args:
            value: Constant value
            
        Returns:
            Pattern: Constant pattern
        """
        pass
    
    @staticmethod
    def linear(start_value: float, end_value: float, duration: float) -> 'Pattern':
        """
        Create linear interpolation pattern.
        
        Args:
            start_value: Initial value
            end_value: Final value
            duration: Pattern duration
            
        Returns:
            Pattern: Linear interpolation pattern
        """
        pass
    
    @staticmethod
    def from_time_series(time_series: TimeSeries) -> 'Pattern':
        """
        Create pattern from time series data.
        
        Args:
            time_series: Time-dependent values
            
        Returns:
            Pattern: Pattern from time series
        """
        pass

def create_rydberg_hamiltonian() -> Hamiltonian:
    """
    Create typical Rydberg Hamiltonian for quantum simulation.
    
    Returns:
        Hamiltonian: Rydberg blockade Hamiltonian
    """
    hamiltonian = Hamiltonian()
    
    # Time-dependent Rabi frequency (adiabatic ramp)
    omega_max = 2*np.pi * 15  # 15 MHz
    ramp_time = 1.0  # microseconds
    hold_time = 0.5  # microseconds
    
    # Create piecewise linear amplitude
    amplitude_points = [
        (0.0, 0.0),              # Start at zero
        (ramp_time/4, omega_max), # Ramp up
        (3*ramp_time/4, omega_max), # Hold
        (ramp_time, 0.0)          # Ramp down
    ]
    
    amplitude_series = TimeSeries()
    for time, value in amplitude_points:
        amplitude_series.add_point(time, value)
    
    amplitude_pattern = Pattern.from_time_series(amplitude_series)
    
    # Constant phase and detuning
    phase_pattern = Pattern.constant(0.0)
    detuning_pattern = Pattern.constant(0.0)
    
    # Add driving field
    driving_field = DrivingField(
        amplitude=amplitude_pattern,
        phase=phase_pattern,
        detuning=detuning_pattern
    )
    
    hamiltonian.add_driving_field(driving_field)
    
    return hamiltonian

def create_annealing_schedule(initial_value: float, final_value: float, total_time: float) -> Pattern:
    """
    Create annealing schedule pattern for AHS.
    
    Args:
        initial_value: Starting parameter value
        final_value: Ending parameter value
        total_time: Total annealing time
        
    Returns:
        Pattern: Annealing schedule pattern
    """
    # Create smooth S-curve annealing schedule
    n_points = 100
    times = np.linspace(0, total_time, n_points)
    
    # S-curve using tanh function
    s_values = []
    for t in times:
        s = (np.tanh(4 * (t/total_time - 0.5)) + 1) / 2  # Sigmoid from 0 to 1
        value = initial_value + s * (final_value - initial_value)
        s_values.append(value)
    
    # Create time series
    time_series = TimeSeries()
    for t, v in zip(times, s_values):
        time_series.add_point(t, v)
    
    return Pattern.from_time_series(time_series)

from braket.annealing import Problem, ProblemType
from enum import Enum

class ProblemType(Enum):
    """Types of annealing problems."""
    QUBO = "QUBO"
    ISING = "ISING"

class Problem:
    """Annealing problem specification."""
    
    def __init__(self, problem_type: ProblemType):
        """
        Initialize annealing problem.
        
        Args:
            problem_type: Type of optimization problem (QUBO or Ising)
        """
        self.problem_type = problem_type
        self.linear = {}  # Linear terms
        self.quadratic = {}  # Quadratic terms
    
    def add_linear(self, variable: int, coefficient: float) -> 'Problem':
        """
        Add linear term to problem.
        
        Args:
            variable: Variable index
            coefficient: Linear coefficient
            
        Returns:
            Problem: Self for method chaining
        """
        self.linear[variable] = coefficient
        return self
    
    def add_quadratic(self, var1: int, var2: int, coefficient: float) -> 'Problem':
        """
        Add quadratic term to problem.
        
        Args:
            var1: First variable index
            var2: Second variable index  
            coefficient: Quadratic coefficient
            
        Returns:
            Problem: Self for method chaining
        """
        self.quadratic[(var1, var2)] = coefficient
        return self
    
    def to_dict(self) -> dict:
        """
        Convert problem to dictionary representation.
        
        Returns:
            dict: Problem in dictionary format
        """
        return {
            'type': self.problem_type.value,
            'linear': self.linear,
            'quadratic': self.quadratic
        }

# Annealing examples
def create_max_cut_problem(graph_edges: list[tuple[int, int]]) -> Problem:
    """
    Create Max-Cut problem for quantum annealing.
    
    Args:
        graph_edges: List of graph edges as (node1, node2) pairs
        
    Returns:
        Problem: Max-Cut QUBO formulation
    """
    problem = Problem(ProblemType.QUBO)
    
    # Max-Cut QUBO formulation: maximize ∑ᵢⱼ wᵢⱼ xᵢ(1-xⱼ) + wᵢⱼ(1-xᵢ)xⱼ
    # Equivalent to: minimize -∑ᵢⱼ wᵢⱼ(xᵢ + xⱼ - 2xᵢxⱼ)
    
    # Assume unit weights for simplicity
    nodes = set()
    for i, j in graph_edges:
        nodes.add(i)
        nodes.add(j)
    
    # Linear terms: -wᵢⱼ for each edge (i,j)
    linear_coeffs = {}
    for i, j in graph_edges:
        linear_coeffs[i] = linear_coeffs.get(i, 0) - 1
        linear_coeffs[j] = linear_coeffs.get(j, 0) - 1
    
    for node, coeff in linear_coeffs.items():
        problem.add_linear(node, coeff)
    
    # Quadratic terms: +2wᵢⱼ for each edge (i,j)
    for i, j in graph_edges:
        problem.add_quadratic(i, j, 2.0)
    
    return problem

def create_number_partitioning_problem(numbers: list[int]) -> Problem:
    """
    Create number partitioning problem for quantum annealing.
    
    Args:
        numbers: List of numbers to partition into equal-sum sets
        
    Returns:
        Problem: Number partitioning QUBO formulation
    """
    problem = Problem(ProblemType.QUBO)
    
    # Number partitioning: minimize (∑ᵢ aᵢxᵢ - S/2)²
    # where S = ∑ᵢ aᵢ is total sum
    S = sum(numbers)
    target = S // 2  # Target sum for each partition
    
    # Expand: (∑ᵢ aᵢxᵢ - target)² = ∑ᵢ aᵢ²xᵢ + 2∑ᵢⱼ aᵢaⱼxᵢxⱼ - 2target∑ᵢ aᵢxᵢ + target²
    # Ignore constant target² term
    
    # Linear terms: aᵢ² - 2target·aᵢ
    for i, a in enumerate(numbers):
        linear_coeff = a*a - 2*target*a
        problem.add_linear(i, linear_coeff)
    
    # Quadratic terms: 2aᵢaⱼ for i < j
    for i in range(len(numbers)):
        for j in range(i+1, len(numbers)):
            quadratic_coeff = 2 * numbers[i] * numbers[j]
            problem.add_quadratic(i, j, quadratic_coeff)
    
    return problem

def create_portfolio_optimization_problem(returns: list[float], risks: list[list[float]], risk_penalty: float = 1.0) -> Problem:
    """
    Create portfolio optimization problem for quantum annealing.
    
    Args:
        returns: Expected returns for each asset
        risks: Risk covariance matrix
        risk_penalty: Risk penalty parameter λ
        
    Returns:
        Problem: Portfolio optimization QUBO formulation
    """
    problem = Problem(ProblemType.QUBO)
    n_assets = len(returns)
    
    # Portfolio optimization: maximize ∑ᵢ rᵢxᵢ - λ ∑ᵢⱼ σᵢⱼxᵢxⱼ
    # Convert to minimization: minimize -∑ᵢ rᵢxᵢ + λ ∑ᵢⱼ σᵢⱼxᵢxⱼ
    
    # Linear terms: -rᵢ (negative expected returns)
    for i, r in enumerate(returns):
        problem.add_linear(i, -r)
    
    # Quadratic terms: λσᵢⱼ (risk penalties)
    for i in range(n_assets):
        for j in range(i, n_assets):
            if i == j:
                # Diagonal terms (variance)
                risk_coeff = risk_penalty * risks[i][j]
                problem.add_linear(i, risk_coeff)  # Add to diagonal
            else:
                # Off-diagonal terms (covariance)
                risk_coeff = risk_penalty * risks[i][j]
                problem.add_quadratic(i, j, 2 * risk_coeff)  # Factor of 2 for symmetry
    
    return problem

def analyze_annealing_problem(problem: Problem) -> dict:
    """
    Analyze quantum annealing problem for optimization insights.
    
    Args:
        problem: Annealing problem to analyze
        
    Returns:
        dict: Problem analysis and optimization recommendations
    """
    analysis = {
        'problem_structure': {},
        'complexity_metrics': {},
        'annealing_parameters': {},
        'optimization_difficulty': {}
    }
    
    # Analyze problem structure
    n_variables = len(set(list(problem.linear.keys()) + 
                         [var for pair in problem.quadratic.keys() for var in pair]))
    n_linear_terms = len(problem.linear)
    n_quadratic_terms = len(problem.quadratic)
    
    analysis['problem_structure'] = {
        'problem_type': problem.problem_type.value,
        'variable_count': n_variables,
        'linear_terms': n_linear_terms,
        'quadratic_terms': n_quadratic_terms,
        'density': n_quadratic_terms / (n_variables * (n_variables - 1) / 2) if n_variables > 1 else 0
    }
    
    # Complexity analysis
    coefficient_magnitudes = (list(problem.linear.values()) + 
                            list(problem.quadratic.values()))
    
    analysis['complexity_metrics'] = {
        'coefficient_range': (min(coefficient_magnitudes), max(coefficient_magnitudes)),
        'coefficient_std': np.std(coefficient_magnitudes),
        'sparsity': 1 - (n_linear_terms + n_quadratic_terms) / (n_variables + n_variables**2/2),
        'conditioning': max(coefficient_magnitudes) / min(abs(c) for c in coefficient_magnitudes if c != 0)
    }
    
    # Annealing parameter recommendations
    if analysis['complexity_metrics']['conditioning'] > 100:
        recommended_annealing_time = 'Long (>100μs)'
        recommended_schedule = 'Slow linear or exponential'
    else:
        recommended_annealing_time = 'Standard (20-50μs)'
        recommended_schedule = 'Linear or fast exponential'
    
    analysis['annealing_parameters'] = {
        'recommended_annealing_time': recommended_annealing_time,
        'recommended_schedule': recommended_schedule,
        'suggested_repetitions': max(1000, 10 * n_variables),
        'post_processing': 'Consider classical refinement for large problems'
    }
    
    return analysis

from braket.pulse import PulseSequence, Frame, Port
import numpy as np

class PulseSequence:
    """Sequence of pulse operations for direct hardware control."""
    
    def __init__(self):
        """Initialize empty pulse sequence."""
        self.operations = []
        self.duration = 0.0
    
    def play(self, frame: 'Frame', waveform: 'Waveform') -> 'PulseSequence':
        """
        Play waveform on specified frame.
        
        Args:
            frame: Target frame for pulse
            waveform: Pulse waveform to play
            
        Returns:
            PulseSequence: Self for method chaining
        """
        pass
    
    def delay(self, frame: 'Frame', duration: float) -> 'PulseSequence':
        """
        Add delay on frame.
        
        Args:
            frame: Target frame
            duration: Delay duration in seconds
            
        Returns:
            PulseSequence: Self for method chaining
        """
        pass
    
    def shift_frequency(self, frame: 'Frame', frequency: float) -> 'PulseSequence':
        """
        Shift frame frequency.
        
        Args:
            frame: Target frame
            frequency: Frequency shift in Hz
            
        Returns:
            PulseSequence: Self for method chaining
        """
        pass
    
    def set_phase(self, frame: 'Frame', phase: float) -> 'PulseSequence':
        """
        Set frame phase.
        
        Args:
            frame: Target frame
            phase: Phase in radians
            
        Returns:
            PulseSequence: Self for method chaining
        """
        pass
    
    def barrier(self, frames: list['Frame']) -> 'PulseSequence':
        """
        Add synchronization barrier across frames.
        
        Args:
            frames: Frames to synchronize
            
        Returns:
            PulseSequence: Self for method chaining
        """
        pass

class Frame:
    """Pulse frame definition for frequency and phase tracking."""
    
    def __init__(
        self,
        frame_id: str,
        port: 'Port',
        frequency: float,
        phase: float = 0.0
    ):
        """
        Initialize pulse frame.
        
        Args:
            frame_id: Unique frame identifier
            port: Associated hardware port
            frequency: Frame frequency in Hz
            phase: Initial phase in radians
        """
        self.frame_id = frame_id
        self.port = port
        self.frequency = frequency
        self.phase = phase

class Port:
    """Hardware port specification."""
    
    def __init__(self, port_id: str, dt: float):
        """
        Initialize hardware port.
        
        Args:
            port_id: Unique port identifier
            dt: Port time resolution in seconds
        """
        self.port_id = port_id
        self.dt = dt

# Waveform definitions
class GaussianWaveform:
    """Gaussian pulse waveform."""
    
    def __init__(
        self,
        length: float,
        amplitude: float,
        sigma: float
    ):
        """
        Initialize Gaussian waveform.
        
        Args:
            length: Pulse length in seconds
            amplitude: Peak amplitude
            sigma: Gaussian width parameter
        """
        self.length = length
        self.amplitude = amplitude
        self.sigma = sigma

class DragGaussianWaveform:
    """DRAG (Derivative Removal by Adiabatic Gating) Gaussian waveform."""
    
    def __init__(
        self,
        length: float,
        amplitude: float,
        sigma: float,
        beta: float
    ):
        """
        Initialize DRAG Gaussian waveform.
        
        Args:
            length: Pulse length in seconds
            amplitude: Peak amplitude  
            sigma: Gaussian width parameter
            beta: DRAG correction parameter
        """
        self.length = length
        self.amplitude = amplitude
        self.sigma = sigma
        self.beta = beta

class ConstantWaveform:
    """Constant amplitude waveform."""
    
    def __init__(self, length: float, amplitude: float):
        """
        Initialize constant waveform.
        
        Args:
            length: Pulse length in seconds
            amplitude: Constant amplitude
        """
        self.length = length
        self.amplitude = amplitude

class ArbitraryWaveform:
    """Custom arbitrary waveform."""
    
    def __init__(self, amplitudes: list[complex]):
        """
        Initialize arbitrary waveform.
        
        Args:
            amplitudes: Time-sampled amplitude values
        """
        self.amplitudes = amplitudes
        self.length = len(amplitudes)

# Pulse programming examples
def create_single_qubit_pulse_gate(qubit_frequency: float, rabi_frequency: float, gate_type: str) -> PulseSequence:
    """
    Create pulse sequence for single-qubit gate.
    
    Args:
        qubit_frequency: Qubit transition frequency in Hz
        rabi_frequency: Rabi frequency for π-pulse in Hz
        gate_type: Type of gate ('X', 'Y', 'Z', 'H')
        
    Returns:
        PulseSequence: Pulse sequence implementing the gate
    """
    sequence = PulseSequence()
    
    # Define hardware components
    port = Port("drive_port", dt=1e-9)  # 1 ns resolution
    frame = Frame("qubit_frame", port, qubit_frequency)
    
    if gate_type == 'X':
        # π-pulse around X-axis
        pulse_length = 1 / (2 * rabi_frequency)  # π-pulse duration
        waveform = GaussianWaveform(
            length=pulse_length,
            amplitude=rabi_frequency * 2 * np.pi,
            sigma=pulse_length / 4
        )
        sequence.play(frame, waveform)
    
    elif gate_type == 'Y':
        # π-pulse around Y-axis (π/2 phase shift)
        pulse_length = 1 / (2 * rabi_frequency)
        sequence.set_phase(frame, np.pi/2)
        waveform = GaussianWaveform(
            length=pulse_length,
            amplitude=rabi_frequency * 2 * np.pi,
            sigma=pulse_length / 4
        )
        sequence.play(frame, waveform)
        sequence.set_phase(frame, 0)  # Reset phase
    
    elif gate_type == 'H':
        # Hadamard via Y(π/2) - X(π) - Y(π/2) decomposition
        pulse_length = 1 / (4 * rabi_frequency)  # π/2-pulse duration
        
        # First Y(π/2)
        sequence.set_phase(frame, np.pi/2)
        y_pulse = GaussianWaveform(
            length=pulse_length,
            amplitude=rabi_frequency * 2 * np.pi,
            sigma=pulse_length / 4
        )
        sequence.play(frame, y_pulse)
        
        # X(π)
        sequence.set_phase(frame, 0)
        x_pulse = GaussianWaveform(
            length=2*pulse_length,
            amplitude=rabi_frequency * 2 * np.pi,
            sigma=pulse_length / 2
        )
        sequence.play(frame, x_pulse)
        
        # Final Y(π/2)
        sequence.set_phase(frame, np.pi/2)
        sequence.play(frame, y_pulse)
        sequence.set_phase(frame, 0)
    
    return sequence

def create_two_qubit_cross_resonance_gate(
    control_freq: float, 
    target_freq: float, 
    cr_amplitude: float
) -> PulseSequence:
    """
    Create cross-resonance pulse sequence for two-qubit gate.
    
    Args:
        control_freq: Control qubit frequency in Hz
        target_freq: Target qubit frequency in Hz  
        cr_amplitude: Cross-resonance drive amplitude
        
    Returns:
        PulseSequence: Cross-resonance gate pulse sequence
    """
    sequence = PulseSequence()
    
    # Hardware setup
    control_port = Port("control_port", dt=1e-9)
    target_port = Port("target_port", dt=1e-9)
    
    control_frame = Frame("control_frame", control_port, control_freq)
    target_frame = Frame("target_frame", target_port, target_freq)
    
    # Cross-resonance drive on target frequency via control port
    cr_frame = Frame("cr_frame", control_port, target_freq)
    
    # Cross-resonance pulse (flat-top with Gaussian edges)
    cr_duration = 320e-9  # 320 ns typical CR gate time
    rise_time = 20e-9     # 20 ns rise/fall time
    
    # Rising edge
    rise_waveform = GaussianWaveform(
        length=rise_time,
        amplitude=cr_amplitude,
        sigma=rise_time / 4
    )
    
    # Flat top
    flat_duration = cr_duration - 2 * rise_time
    flat_waveform = ConstantWaveform(flat_duration, cr_amplitude)
    
    # Falling edge  
    fall_waveform = GaussianWaveform(
        length=rise_time,
        amplitude=-cr_amplitude,  # Negative for falling
        sigma=rise_time / 4
    )
    
    # Play CR pulse sequence
    sequence.play(cr_frame, rise_waveform)
    sequence.play(cr_frame, flat_waveform)
    sequence.play(cr_frame, fall_waveform)
    
    # Echo cancellation pulses on control qubit
    echo_pulse = GaussianWaveform(
        length=cr_duration,
        amplitude=cr_amplitude / 4,  # Reduced amplitude
        sigma=cr_duration / 8
    )
    sequence.play(control_frame, echo_pulse)
    
    # Synchronization barrier
    sequence.barrier([control_frame, target_frame, cr_frame])
    
    return sequence

def optimize_pulse_fidelity(
    target_unitary: np.ndarray,
    pulse_parameters: dict,
    constraints: dict
) -> dict:
    """
    Optimize pulse sequence for maximum gate fidelity.
    
    Args:
        target_unitary: Target gate unitary matrix
        pulse_parameters: Initial pulse parameter values
        constraints: Hardware and physics constraints
        
    Returns:
        dict: Optimized pulse parameters and fidelity analysis
    """
    optimization_result = {
        'optimized_parameters': {},
        'achieved_fidelity': 0.0,
        'optimization_history': [],
        'constraint_violations': {},
        'recommendations': []
    }
    
    # Extract constraints
    max_amplitude = constraints.get('max_amplitude', 1e8)  # Hz
    min_duration = constraints.get('min_duration', 1e-9)   # s
    max_duration = constraints.get('max_duration', 1e-6)   # s
    
    # Simplified optimization (would use gradient-based methods)
    initial_amplitude = pulse_parameters.get('amplitude', max_amplitude / 2)
    initial_duration = pulse_parameters.get('duration', 50e-9)
    initial_sigma = pulse_parameters.get('sigma', initial_duration / 4)
    
    # Grid search over parameter space (simplified)
    best_fidelity = 0.0
    best_params = pulse_parameters.copy()
    
    amplitude_range = np.linspace(max_amplitude/10, max_amplitude, 10)
    duration_range = np.linspace(min_duration*10, max_duration/10, 10)
    
    for amp in amplitude_range:
        for dur in duration_range:
            # Simulate gate with these parameters
            simulated_fidelity = simulate_pulse_gate_fidelity(
                target_unitary, amp, dur, dur/4
            )
            
            if simulated_fidelity > best_fidelity:
                best_fidelity = simulated_fidelity
                best_params = {
                    'amplitude': amp,
                    'duration': dur,
                    'sigma': dur/4
                }
    
    optimization_result['optimized_parameters'] = best_params
    optimization_result['achieved_fidelity'] = best_fidelity
    
    # Generate recommendations
    if best_fidelity < 0.99:
        optimization_result['recommendations'].append(
            "Consider DRAG correction for improved fidelity"
        )
    
    if best_params['duration'] > max_duration / 2:
        optimization_result['recommendations'].append(
            "Long pulse duration may increase decoherence"
        )
    
    return optimization_result

def simulate_pulse_gate_fidelity(target_unitary: np.ndarray, amplitude: float, duration: float, sigma: float) -> float:
    """Simplified pulse gate fidelity simulation."""
    # This would involve solving the Schrödinger equation
    # For now, return a mock fidelity based on parameter reasonableness
    
    # Assume optimal parameters give ~99% fidelity
    optimal_duration = 50e-9  # 50 ns
    optimal_amplitude = 1e7   # 10 MHz
    
    duration_factor = 1 - abs(duration - optimal_duration) / optimal_duration
    amplitude_factor = 1 - abs(amplitude - optimal_amplitude) / optimal_amplitude
    
    fidelity = 0.95 * duration_factor * amplitude_factor
    return max(0.5, min(0.999, fidelity))  # Clamp between 50% and 99.9%

from braket.error_mitigation import ErrorMitigation, Debias

class ErrorMitigation:
    """Base error mitigation interface."""
    
    def mitigate(self, task_results: list) -> list:
        """
        Apply error mitigation to task results.
        
        Args:
            task_results: Raw quantum task results
            
        Returns:
            list: Mitigated results
        """
        pass

class Debias(ErrorMitigation):
    """Debiasing error mitigation technique."""
    
    def __init__(self, noise_model=None):
        """
        Initialize debiasing error mitigation.
        
        Args:
            noise_model: Optional noise model for correction
        """
        self.noise_model = noise_model
    
    def mitigate(self, task_results: list) -> list:
        """
        Apply debiasing to measurement results.
        
        Args:
            task_results: Task results to debias
            
        Returns:
            list: Debiased results
        """
        pass

# Error mitigation examples
def implement_zero_noise_extrapolation(
    circuit,
    device,
    noise_scaling_factors: list[float] = [1.0, 2.0, 3.0]
) -> dict:
    """
    Implement zero-noise extrapolation (ZNE) error mitigation.
    
    Args:
        circuit: Quantum circuit to execute with error mitigation
        device: Quantum device for execution
        noise_scaling_factors: Noise scaling factors for extrapolation
        
    Returns:
        dict: ZNE results and extrapolated values
    """
    zne_results = {
        'noise_factors': noise_scaling_factors,
        'measured_values': [],
        'extrapolated_value': 0.0,
        'extrapolation_error': 0.0,
        'mitigation_overhead': len(noise_scaling_factors)
    }
    
    # Execute circuit at different noise levels
    for noise_factor in noise_scaling_factors:
        # Create noise-scaled circuit (simplified - would need proper implementation)
        scaled_circuit = scale_circuit_noise(circuit, noise_factor)
        
        # Execute and measure expectation value
        task = device.run(scaled_circuit, shots=10000)
        result = task.result()
        
        if hasattr(result, 'values'):
            expectation_value = result.values[0]
        else:
            # Calculate expectation from measurement counts
            expectation_value = calculate_expectation_from_counts(
                result.measurement_counts
            )
        
        zne_results['measured_values'].append(expectation_value)
    
    # Extrapolate to zero noise
    extrapolated_value, error = extrapolate_to_zero_noise(
        noise_scaling_factors, zne_results['measured_values']
    )
    
    zne_results['extrapolated_value'] = extrapolated_value
    zne_results['extrapolation_error'] = error
    
    return zne_results

def implement_readout_error_mitigation(
    circuits: list,
    device,
    calibration_shots: int = 10000
) -> dict:
    """
    Implement readout error mitigation using confusion matrix.
    
    Args:
        circuits: Circuits to execute with readout error correction
        device: Quantum device
        calibration_shots: Shots for calibration measurements
        
    Returns:
        dict: Results with readout error correction applied
    """
    mitigation_results = {
        'confusion_matrix': None,
        'corrected_results': [],
        'improvement_metrics': {}
    }
    
    # Step 1: Calibrate readout errors
    n_qubits = max(circuit.qubit_count for circuit in circuits)
    confusion_matrix = calibrate_readout_errors(device, n_qubits, calibration_shots)
    mitigation_results['confusion_matrix'] = confusion_matrix
    
    # Step 2: Execute circuits and apply correction
    for circuit in circuits:
        # Execute circuit
        task = device.run(circuit, shots=10000)
        raw_counts = task.result().measurement_counts
        
        # Apply readout error correction
        corrected_counts = apply_readout_correction(
            raw_counts, confusion_matrix
        )
        
        mitigation_results['corrected_results'].append({
            'circuit_index': len(mitigation_results['corrected_results']),
            'raw_counts': raw_counts,
            'corrected_counts': corrected_counts
        })
    
    # Calculate improvement metrics
    mitigation_results['improvement_metrics'] = calculate_mitigation_improvement(
        mitigation_results['corrected_results']
    )
    
    return mitigation_results

def implement_virtual_distillation(
    circuit,
    device,
    num_copies: int = 5,
    shots_per_copy: int = 2000
) -> dict:
    """
    Implement virtual distillation error mitigation.
    
    Args:
        circuit: Circuit to execute with virtual distillation
        device: Quantum device
        num_copies: Number of virtual copies
        shots_per_copy: Shots per copy
        
    Returns:
        dict: Virtual distillation results
    """
    vd_results = {
        'num_copies': num_copies,
        'copy_results': [],
        'distilled_result': 0.0,
        'variance_reduction': 0.0
    }
    
    copy_values = []
    
    # Execute multiple copies of the circuit
    for copy_idx in range(num_copies):
        task = device.run(circuit, shots=shots_per_copy)
        result = task.result()
        
        # Calculate expectation value for this copy
        if hasattr(result, 'values'):
            expectation = result.values[0]
        else:
            expectation = calculate_expectation_from_counts(result.measurement_counts)
        
        copy_values.append(expectation)
        vd_results['copy_results'].append({
            'copy_index': copy_idx,
            'expectation_value': expectation,
            'measurement_counts': result.measurement_counts
        })
    
    # Apply virtual distillation formula
    # For odd number of copies: median
    # For even number: could use other combining strategies
    if num_copies % 2 == 1:
        distilled_value = np.median(copy_values)
    else:
        # Use weighted average with outlier removal
        copy_array = np.array(copy_values)
        q1, q3 = np.percentile(copy_array, [25, 75])
        iqr = q3 - q1
        lower_bound = q1 - 1.5 * iqr
        upper_bound = q3 + 1.5 * iqr
        
        # Filter outliers and average
        filtered_values = copy_array[
            (copy_array >= lower_bound) & (copy_array <= upper_bound)
        ]
        distilled_value = np.mean(filtered_values) if len(filtered_values) > 0 else np.mean(copy_array)
    
    vd_results['distilled_result'] = distilled_value
    
    # Calculate variance reduction
    raw_variance = np.var(copy_values)
    # Virtual distillation typically reduces variance by ~1/sqrt(N)
    expected_variance_reduction = 1 / np.sqrt(num_copies)
    vd_results['variance_reduction'] = expected_variance_reduction
    
    return vd_results

def scale_circuit_noise(circuit, noise_factor: float):
    """Scale noise in quantum circuit (simplified implementation)."""
    # This would involve adding noise gates or modifying existing noise
    # For now, return original circuit (real implementation would add noise)
    return circuit

def extrapolate_to_zero_noise(noise_factors: list[float], measured_values: list[float]) -> tuple[float, float]:
    """Extrapolate measured values to zero noise using polynomial fit."""
    # Linear extrapolation (could use higher-order polynomials)
    coeffs = np.polyfit(noise_factors, measured_values, deg=1)
    
    # Extrapolate to zero noise (x=0)
    extrapolated_value = coeffs[1]  # y-intercept
    
    # Estimate error from fit quality
    fit_values = np.polyval(coeffs, noise_factors)
    fit_error = np.sqrt(np.mean((np.array(measured_values) - fit_values)**2))
    
    return float(extrapolated_value), float(fit_error)

def calibrate_readout_errors(device, n_qubits: int, shots: int) -> np.ndarray:
    """Calibrate readout error confusion matrix."""
    from braket.circuits import Circuit
    
    confusion_matrix = np.zeros((2**n_qubits, 2**n_qubits))
    
    # Measure each computational basis state
    for state_idx in range(2**n_qubits):
        # Prepare computational basis state |state_idx⟩
        prep_circuit = Circuit()
        
        # Convert state index to binary and apply X gates
        binary_state = format(state_idx, f'0{n_qubits}b')
        for qubit, bit in enumerate(binary_state):
            if bit == '1':
                prep_circuit.x(qubit)
        
        # Measure
        prep_circuit.measure_all()
        
        # Execute calibration measurement
        task = device.run(prep_circuit, shots=shots)
        counts = task.result().measurement_counts
        
        # Fill confusion matrix row
        for measured_state, count in counts.items():
            measured_idx = int(measured_state, 2)
            confusion_matrix[state_idx, measured_idx] = count / shots
    
    return confusion_matrix

def apply_readout_correction(raw_counts: dict, confusion_matrix: np.ndarray) -> dict:
    """Apply readout error correction using confusion matrix inversion."""
    # Convert counts to probability vector
    total_shots = sum(raw_counts.values())
    n_states = len(confusion_matrix)
    
    measured_probs = np.zeros(n_states)
    for bitstring, count in raw_counts.items():
        state_idx = int(bitstring, 2)
        measured_probs[state_idx] = count / total_shots
    
    # Invert confusion matrix to get corrected probabilities
    try:
        inv_confusion = np.linalg.inv(confusion_matrix)
        corrected_probs = inv_confusion @ measured_probs
        
        # Ensure probabilities are non-negative (physical constraint)
        corrected_probs = np.maximum(corrected_probs, 0)
        corrected_probs /= np.sum(corrected_probs)  # Renormalize
        
    except np.linalg.LinAlgError:
        # If matrix is singular, use pseudo-inverse
        inv_confusion = np.linalg.pinv(confusion_matrix)
        corrected_probs = inv_confusion @ measured_probs
        corrected_probs = np.maximum(corrected_probs, 0)
        corrected_probs /= np.sum(corrected_probs)
    
    # Convert back to counts
    corrected_counts = {}
    for state_idx, prob in enumerate(corrected_probs):
        if prob > 0:
            bitstring = format(state_idx, f'0{int(np.log2(n_states))}b')
            corrected_counts[bitstring] = int(prob * total_shots)
    
    return corrected_counts

def calculate_expectation_from_counts(counts: dict) -> float:
    """Calculate expectation value ⟨Z⟩ from measurement counts."""
    total_shots = sum(counts.values())
    expectation = 0.0
    
    for bitstring, count in counts.items():
        # Calculate parity (even=+1, odd=-1)
        parity = (-1) ** sum(int(bit) for bit in bitstring)
        expectation += parity * count / total_shots
    
    return expectation

from braket.experimental_capabilities import EnableExperimentalCapability

class EnableExperimentalCapability:
    """Context manager for experimental features."""
    
    def __init__(self, capability_name: str):
        """
        Initialize experimental capability context.
        
        Args:
            capability_name: Name of experimental capability to enable
        """
        self.capability_name = capability_name
    
    def __enter__(self) -> 'EnableExperimentalCapability':
        """Enable experimental capability."""
        return self
    
    def __exit__(self, exc_type, exc_val, exc_tb) -> None:
        """Disable experimental capability."""
        pass

# Experimental features examples
def use_experimental_classical_control():
    """Example of using experimental classical control features."""
    
    with EnableExperimentalCapability("classical_control"):
        # Access experimental classical control capabilities
        from braket.experimental_capabilities import classical_control
        
        # This would enable features like:
        # - Mid-circuit measurements with classical feedback
        # - Conditional quantum operations
        # - Real-time classical processing during quantum execution
        
        experimental_features = {
            'mid_circuit_measurement': True,
            'classical_feedback': True,
            'conditional_gates': True,
            'real_time_processing': True
        }
        
        return experimental_features

def explore_advanced_noise_modeling():
    """Explore experimental advanced noise modeling capabilities."""
    
    with EnableExperimentalCapability("advanced_noise"):
        # Access experimental noise modeling features
        experimental_noise_features = {
            'correlated_noise': 'Multi-qubit correlated noise channels',
            'time_dependent_noise': 'Time-varying noise parameters',
            'device_specific_noise': 'Hardware-calibrated noise models',
            'non_markovian_effects': 'Memory effects in noise evolution'
        }
        
        return experimental_noise_features

def demonstrate_experimental_optimization():
    """Demonstrate experimental optimization capabilities."""
    
    with EnableExperimentalCapability("advanced_optimization"):
        
        optimization_features = {
            'adaptive_shot_allocation': 'Dynamic shot count optimization',
            'circuit_cutting': 'Large circuit decomposition',
            'error_extrapolation': 'Advanced error extrapolation methods',
            'hardware_aware_compilation': 'Device-specific circuit optimization'
        }
        
        # Example: Adaptive shot allocation
        def adaptive_shot_optimization(circuits: list, target_accuracy: float) -> dict:
            """
            Experimental adaptive shot allocation for circuit collection.
            
            Args:
                circuits: Circuits requiring optimization
                target_accuracy: Target measurement accuracy
                
            Returns:
                dict: Optimized shot allocation strategy
            """
            shot_allocation = {}
            
            for i, circuit in enumerate(circuits):
                # Estimate required shots based on circuit properties
                estimated_variance = estimate_measurement_variance(circuit)
                required_shots = int(estimated_variance / (target_accuracy**2))
                
                # Apply experimental optimization heuristics
                if circuit.depth > 50:
                    required_shots *= 2  # Deeper circuits need more shots
                
                if has_small_angle_rotations(circuit):
                    required_shots *= 1.5  # Small angles need higher precision
                
                shot_allocation[f'circuit_{i}'] = {
                    'required_shots': required_shots,
                    'estimated_variance': estimated_variance,
                    'optimization_factor': required_shots / 1000  # Compared to baseline
                }
            
            return shot_allocation
        
        return {
            'features': optimization_features,
            'adaptive_optimizer': adaptive_shot_optimization
        }

def estimate_measurement_variance(circuit) -> float:
    """Estimate measurement variance for shot optimization."""
    # Simplified variance estimation based on circuit properties
    base_variance = 0.25  # Maximum variance for uniform distribution
    
    # Adjust based on circuit depth (deeper = more noise = higher variance)
    depth_factor = min(2.0, 1 + circuit.depth / 100)
    
    # Adjust based on number of measured qubits
    qubit_factor = circuit.qubit_count / 10
    
    return base_variance * depth_factor * qubit_factor

def has_small_angle_rotations(circuit) -> bool:
    """Check if circuit contains small-angle rotations requiring high precision."""
    small_angle_threshold = 0.1  # radians
    
    for instruction in circuit.instructions:
        gate = instruction.operator
        if hasattr(gate, 'angle'):
            if abs(gate.angle) < small_angle_threshold:
                return True
    
    return False

def demonstrate_quantum_error_correction_experiments():
    """Demonstrate experimental quantum error correction capabilities."""
    
    with EnableExperimentalCapability("quantum_error_correction"):
        
        qec_experiments = {
            'surface_codes': {
                'description': 'Surface code implementation and testing',
                'min_qubits': 17,  # Smallest logical qubit
                'features': ['syndrome extraction', 'logical operations', 'error correction']
            },
            'color_codes': {
                'description': 'Color code quantum error correction',
                'min_qubits': 7,   # 7-qubit color code
                'features': ['triangular lattice', 'transversal gates', 'fault tolerance']
            },
            'repetition_codes': {
                'description': 'Simple repetition codes for bit-flip errors',
                'min_qubits': 3,   # 3-qubit repetition code
                'features': ['bit flip correction', 'syndrome measurement', 'majority voting']
            }
        }
        
        def create_simple_qec_circuit(code_type: str = 'repetition') -> dict:
            """Create simple quantum error correction demonstration circuit."""
            from braket.circuits import Circuit
            
            if code_type == 'repetition':
                # 3-qubit bit-flip repetition code
                circuit = Circuit()
                
                # Encode logical |0⟩ or |1⟩
                # |0⟩_L = |000⟩, |1⟩_L = |111⟩
                
                # For |+⟩_L state: (|000⟩ + |111⟩)/√2
                circuit.h(0)       # Create superposition on first qubit
                circuit.cnot(0, 1) # Entangle with second qubit  
                circuit.cnot(0, 2) # Entangle with third qubit
                
                # Syndrome measurement (detect single bit-flip errors)
                circuit.cnot(0, 3) # Ancilla 3: measures X₀X₁
                circuit.cnot(1, 3)
                
                circuit.cnot(1, 4) # Ancilla 4: measures X₁X₂  
                circuit.cnot(2, 4)
                
                # Measure syndrome qubits
                circuit.measure([3, 4])
                
                qec_info = {
                    'circuit': circuit,
                    'logical_qubits': 1,
                    'physical_qubits': 3,
                    'ancilla_qubits': 2,
                    'correctable_errors': ['single bit flip'],
                    'code_distance': 3
                }
                
            return qec_info
        
        return {
            'available_codes': qec_experiments,
            'circuit_generator': create_simple_qec_circuit
        }

from braket.emulation import Emulator, PassManager

class Emulator:
    """Quantum circuit emulator with compilation passes."""
    
    def __init__(
        self,
        pass_manager: PassManager = None,
        target_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
    ):
        """
        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):
        """
        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

# Emulation examples
def create_optimized_emulator() -> Emulator:
    """
    Create emulator with standard optimization passes.
    
    Returns:
        Emulator: Configured emulator with optimization
    """
    pass_manager = PassManager()
    
    # Add standard optimization passes
    # Note: Actual pass implementations would be more complex
    # pass_manager.add_pass(GateCommutationPass())
    # pass_manager.add_pass(RedundantGateEliminationPass())
    # pass_manager.add_pass(CircuitDepthOptimizationPass())
    
    emulator = Emulator(pass_manager=pass_manager)
    return emulator

def benchmark_emulation_performance(circuit) -> dict:
    """
    Benchmark emulation vs direct simulation performance.
    
    Args:
        circuit: Circuit to benchmark
        
    Returns:
        dict: Performance comparison results
    """
    import time
    from braket.devices import LocalSimulator
    
    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 = create_optimized_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 if hasattr(compiled_circuit, 'depth') else 'Unknown'
    }
    
    # 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 if hasattr(compiled_circuit, 'depth') else circuit.depth)) / circuit.depth if circuit.depth > 0 else 0
    }
    
    return benchmark_results

from braket.experimental_capabilities import EnableExperimentalCapability

class EnableExperimentalCapability:
    """Context manager for enabling experimental features."""
    
    def __init__(self, capability_name: str):
        """
        Initialize experimental capability context.
        
        Args:
            capability_name: Name of experimental capability to enable
        """
        self.capability_name = capability_name
    
    def __enter__(self):
        """Enter experimental capability context."""
        pass
    
    def __exit__(self, exc_type, exc_val, exc_tb):
        """Exit experimental capability context."""
        pass

# Experimental capabilities examples
def access_experimental_features() -> dict:
    """
    Demonstrate access to experimental quantum computing features.
    
    Returns:
        dict: Available experimental capabilities
    """
    experimental_features = {
        'available_capabilities': [
            'classical_control',
            'adaptive_circuits', 
            'real_time_feedback',
            'advanced_error_correction',
            'novel_gate_sets'
        ],
        'usage_examples': {},
        'stability_warnings': {
            'api_stability': 'Experimental APIs may change without notice',
            'result_reliability': 'Results may vary between SDK versions',
            'production_readiness': 'Not recommended for production use'
        }
    }
    
    # Classical control example (IQM-specific)
    def classical_control_example():
        """Demonstrate classical control features."""
        try:
            with EnableExperimentalCapability('classical_control'):
                from braket.experimental_capabilities.iqm import classical_control
                
                # This would enable classical control features
                # Actual implementation depends on device support
                control_config = {
                    'conditional_gates': True,
                    'real_time_feedback': True,
                    'adaptive_measurements': True
                }
                
                return {
                    'status': 'enabled',
                    'configuration': control_config,
                    'supported_devices': ['IQM Adonis', 'IQM Apollo']
                }
        except Exception as e:
            return {
                'status': 'unavailable',
                'error': str(e),
                'reason': 'Experimental feature not available in current environment'
            }
    
    experimental_features['usage_examples']['classical_control'] = classical_control_example()
    
    # Advanced error correction
    def advanced_error_correction_example():
        """Demonstrate advanced error correction features."""
        with EnableExperimentalCapability('advanced_error_correction'):
            # Experimental error correction protocols
            advanced_qec = {
                'surface_codes': {
                    'supported': True,
                    'min_qubits': 17,  # Smallest surface code
                    'features': ['arbitrary distance', 'custom boundary conditions']
                },
                'color_codes': {
                    'supported': True,
                    'min_qubits': 15,  # Color code triangle
                    'features': ['higher threshold', 'transversal gates']
                },
                'topological_codes': {
                    'supported': False,
                    'reason': 'Requires specific hardware topology'
                }
            }
            
            return advanced_qec
    
    experimental_features['usage_examples']['advanced_error_correction'] = advanced_error_correction_example()
    
    return experimental_features

def create_experimental_workflow() -> dict:
    """
    Create experimental quantum computing workflow.
    
    Returns:
        dict: Experimental workflow configuration
    """
    workflow = {
        'stages': [
            'feature_detection',
            'capability_enablement', 
            'experimental_execution',
            'result_validation',
            'fallback_handling'
        ],
        'implementation': {}
    }
    
    def feature_detection_stage():
        """Detect available experimental features."""
        available_features = []
        
        # Check for classical control
        try:
            with EnableExperimentalCapability('classical_control'):
                available_features.append('classical_control')
        except:
            pass
        
        # Check for advanced error correction
        try:
            with EnableExperimentalCapability('advanced_error_correction'):
                available_features.append('advanced_error_correction')
        except:
            pass
        
        return {
            'detected_features': available_features,
            'detection_method': 'capability_probing',
            'confidence': 'high' if available_features else 'low'
        }
    
    def experimental_execution_stage(circuit, experimental_config: dict):
        """Execute circuit with experimental features."""
        execution_plan = {
            'base_circuit': circuit,
            'experimental_modifications': [],
            'fallback_strategy': 'standard_execution'
        }
        
        # Apply experimental modifications based on config
        if 'classical_control' in experimental_config.get('enabled_features', []):
            execution_plan['experimental_modifications'].append({
                'type': 'classical_control',
                'description': 'Enable conditional gate execution',
                'impact': 'Allows adaptive circuit behavior'
            })
        
        if 'advanced_error_correction' in experimental_config.get('enabled_features', []):
            execution_plan['experimental_modifications'].append({
                'type': 'advanced_qec',
                'description': 'Apply experimental error correction',
                'impact': 'Improved fidelity with overhead'
            })
        
        return execution_plan
    
    workflow['implementation'] = {
        'feature_detection': feature_detection_stage,
        'experimental_execution': experimental_execution_stage
    }
    
    return workflow