#!/usr/bin/env python3

"""
IBM Quantum Circuit Cutting Implementation

This module provides an implementation of quantum circuit cutting techniques,
allowing large circuits to be split into smaller subcircuits that can be
executed on quantum hardware with limited qubit counts.

Key features:
- Graph partitioning approaches for optimal circuit cutting
- Layer-based circuit partitioning as an alternative strategy
- Automatic subcircuit generation from partitioned circuits
- Compatible with both older Qiskit versions and Qiskit 1.0+

Qiskit 1.0+ Compatibility:
- Robust qubit index extraction that handles both `.index` (older versions) 
  and `._index` (Qiskit 1.0+)
- Compatible gate distribution across subcircuits
- Optional METIS integration with fallback to spectral partitioning

Example usage:
    from ibm_quantum_circuit_cutting import QuantumCircuitCutter
    
    # Initialize the circuit cutter
    cutter = QuantumCircuitCutter(max_subcircuit_width=4)
    
    # Cut the circuit
    subcircuits = cutter.cut_circuit(circuit)

Author: Quantum-AI Team
"""

import os
import logging
import time
from typing import Dict, List, Tuple, Optional, Union, Any, Set
import numpy as np
from copy import deepcopy
import networkx as nx
from collections import defaultdict

# Optional imports
try:
    import metis
    HAS_METIS = True
except ImportError:
    HAS_METIS = False

# Import Qiskit
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.circuit import Instruction, Barrier
from qiskit.dagcircuit import DAGCircuit
from qiskit.converters import circuit_to_dag, dag_to_circuit
from qiskit.transpiler import CouplingMap, PassManager
from qiskit.transpiler.passes import Decompose

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger('ibm_quantum_circuit_cutting')

class CircuitCutInfo:
    """
    Information about a cut in the quantum circuit.
    """
    def __init__(self, 
                qubit_index: int, 
                gate_index: int, 
                subcircuit_index: int,
                neighbor_subcircuit: int):
        """
        Initialize a circuit cut information.
        
        Args:
            qubit_index: Index of the qubit where the cut is made
            gate_index: Index of the gate after which the cut is made
            subcircuit_index: Index of the subcircuit containing this side of the cut
            neighbor_subcircuit: Index of the subcircuit on the other side of the cut
        """
        self.qubit_index = qubit_index
        self.gate_index = gate_index
        self.subcircuit_index = subcircuit_index
        self.neighbor_subcircuit = neighbor_subcircuit

class Subcircuit:
    """
    Representation of a subcircuit created by cutting a larger circuit.
    """
    def __init__(self, 
                circuit: QuantumCircuit, 
                index: int, 
                original_qubit_mapping: Dict[int, int],
                cuts: Optional[List[CircuitCutInfo]] = None):
        """
        Initialize a subcircuit.
        
        Args:
            circuit: The QuantumCircuit object for this subcircuit
            index: Index of this subcircuit
            original_qubit_mapping: Mapping from subcircuit qubit indices to original circuit indices
            cuts: List of cut information for this subcircuit
        """
        self.circuit = circuit
        self.index = index
        self.original_qubit_mapping = original_qubit_mapping
        self.cuts = cuts or []
        self.neighbor_subcircuits = set()
        
        # Extract neighbor subcircuits from cuts
        for cut in self.cuts:
            self.neighbor_subcircuits.add(cut.neighbor_subcircuit)
        
        # Keep track of execution results
        self.results = None

class QuantumCircuitCutter:
    """
    Class to implement circuit cutting techniques for running large quantum circuits
    on hardware with limited qubit connectivity or count.
    """
    
    def __init__(self, max_subcircuit_width: int = 20, max_cuts: int = 5):
        """
        Initialize the quantum circuit cutter.
        
        Args:
            max_subcircuit_width: Maximum number of qubits in a subcircuit
            max_cuts: Maximum number of cuts to make
        """
        self.max_subcircuit_width = max_subcircuit_width
        self.max_cuts = max_cuts
        
    def cut_circuit(self, 
                   circuit: QuantumCircuit, 
                   method: str = 'graph_partition',
                   partition_kwargs: Optional[Dict[str, Any]] = None) -> List[Subcircuit]:
        """
        Cut a quantum circuit into smaller subcircuits.
        
        Args:
            circuit: The quantum circuit to cut
            method: The cutting method to use
            partition_kwargs: Additional parameters for the partitioning algorithm
            
        Returns:
            List of Subcircuit objects
        """
        logger.info(f"Cutting circuit of width {circuit.num_qubits} using method: {method}")
        
        if circuit.num_qubits <= self.max_subcircuit_width:
            logger.info(f"Circuit fits within max width {self.max_subcircuit_width}, no cutting needed")
            # Create a single subcircuit with all qubits
            mapping = {i: i for i in range(circuit.num_qubits)}
            subcircuit = Subcircuit(circuit.copy(), 0, mapping)
            return [subcircuit]
        
        if method == 'graph_partition':
            return self._cut_by_graph_partition(circuit, partition_kwargs or {})
        elif method == 'layer_partition':
            return self._cut_by_layer_partition(circuit, partition_kwargs or {})
        else:
            raise ValueError(f"Unknown cutting method: {method}")
    
    def _cut_by_graph_partition(self, 
                               circuit: QuantumCircuit, 
                               partition_kwargs: Dict[str, Any]) -> List[Subcircuit]:
        """
        Cut a circuit using graph partitioning techniques.
        
        Args:
            circuit: The quantum circuit to cut
            partition_kwargs: Parameters for graph partitioning
            
        Returns:
            List of Subcircuit objects
        """
        # Build a graph representation of the circuit
        G = self._build_circuit_graph(circuit)
        
        # Set default parameters
        n_parts = partition_kwargs.get('n_parts', max(2, circuit.num_qubits // self.max_subcircuit_width))
        cut_method = partition_kwargs.get('cut_method', 'metis' if HAS_METIS else 'spectral')
        weight_by_gates = partition_kwargs.get('weight_by_gates', True)
        
        logger.info(f"Partitioning circuit into {n_parts} parts using {cut_method}")
        
        # Partition the graph
        if cut_method == 'metis' and HAS_METIS:
            try:
                _, parts = metis.part_graph(G, n_parts)
            except Exception as e:
                logger.warning(f"METIS partitioning failed: {str(e)}. Falling back to spectral partitioning")
                parts = self._spectral_partition(G, n_parts)
        elif cut_method == 'spectral':
            parts = self._spectral_partition(G, n_parts)
        elif cut_method == 'greedy':
            parts = self._greedy_partition(G, n_parts)
        else:
            raise ValueError(f"Unknown graph partitioning method: {cut_method}")
        
        # Create subcircuits based on the partitioning
        return self._create_subcircuits_from_partition(circuit, parts)
    
    def _build_circuit_graph(self, circuit: QuantumCircuit) -> nx.Graph:
        """
        Build a graph representation of the circuit for partitioning.
        
        Args:
            circuit: The quantum circuit to represent as a graph
            
        Returns:
            Graph representation of the circuit
        """
        # Create a graph with a node for each qubit
        G = nx.Graph()
        for i in range(circuit.num_qubits):
            G.add_node(i)
        
        # Add edges between qubits that interact via multi-qubit gates
        for instruction, qargs, _ in circuit.data:
            # Skip single-qubit gates and barriers
            if len(qargs) <= 1 or isinstance(instruction, Barrier):
                continue
                
            # For multi-qubit gates, add edges between all pairs of qubits
            for i in range(len(qargs)):
                for j in range(i + 1, len(qargs)):
                    # Extract qubit indices - handle both older Qiskit (index) and Qiskit 1.0+ (_index)
                    try:
                        q1 = qargs[i].index if hasattr(qargs[i], 'index') else qargs[i]._index
                        q2 = qargs[j].index if hasattr(qargs[j], 'index') else qargs[j]._index
                    except AttributeError:
                        # As a fallback, try to get the integer index directly
                        try:
                            q1 = int(qargs[i])
                            q2 = int(qargs[j])
                        except (ValueError, TypeError):
                            logger.warning(f"Could not determine qubit indices for {qargs[i]} and {qargs[j]}")
                            continue
                    
                    # Add or increment edge weight
                    if G.has_edge(q1, q2):
                        G[q1][q2]['weight'] += 1
                    else:
                        G.add_edge(q1, q2, weight=1)
        
        return G
    
    def _spectral_partition(self, G: nx.Graph, n_parts: int) -> List[int]:
        """
        Partition a graph using spectral methods or METIS.
        
        Args:
            G: Graph to partition
            n_parts: Number of partitions
            
        Returns:
            List of partition assignments for each node
        """
        # Get the cut method to use
        cut_method = 'spectral'  # Default method
        
        # Check if we can use METIS, which is generally better
        if HAS_METIS:
            cut_method = 'metis'
        
        # Partition using the chosen method
        parts = [0] * G.number_of_nodes()
        
        if cut_method == 'metis':
            try:
                _, parts = metis.part_graph(G, n_parts)
            except Exception as e:
                logger.warning(f"METIS partitioning failed: {str(e)}. Falling back to spectral method.")
                cut_method = 'spectral'
        
        if cut_method == 'spectral':
            # Use spectral clustering from NetworkX
            try:
                import numpy as np
                
                # Get the Laplacian matrix
                L = nx.normalized_laplacian_matrix(G).toarray()
                
                # Get the eigenvalues and eigenvectors
                eigenvalues, eigenvectors = np.linalg.eigh(L)
                
                # Use the eigenvector of the second-smallest eigenvalue (Fiedler vector)
                # for the spectral bisection
                indices = np.argsort(eigenvalues)
                fiedler_vector = eigenvectors[:, indices[1]] if n_parts == 2 else eigenvectors[:, indices[1:n_parts]]
                
                # For simple bisection
                if n_parts == 2:
                    # Assign to partitions based on the sign of the Fiedler vector
                    parts = [0 if x >= 0 else 1 for x in fiedler_vector]
                else:
                    # For more than 2 partitions, use k-means clustering on the relevant eigenvectors
                    from sklearn.cluster import KMeans
                    kmeans = KMeans(n_clusters=n_parts, random_state=0).fit(fiedler_vector)
                    parts = kmeans.labels_.tolist()
                
            except Exception as e:
                logger.warning(f"Spectral partitioning failed: {str(e)}. Falling back to simple bisection.")
                # Fall back to simple bisection
                return self._bisection_partition(G, n_parts)
        
        return parts
    
    def _bisection_partition(self, G: nx.Graph, n_parts: int) -> List[int]:
        """
        Partition a graph using recursive bisection.
        
        Args:
            G: The graph to partition
            n_parts: Number of partitions
            
        Returns:
            List of partition assignments for each node
        """
        if n_parts == 1:
            return [0] * G.number_of_nodes()
        
        # Start with all nodes in one partition
        partitions = {0: list(G.nodes())}
        assignments = [0] * G.number_of_nodes()
        
        for i in range(1, n_parts):
            # Find the largest partition
            largest_part = max(partitions.keys(), key=lambda k: len(partitions[k]))
            nodes_in_part = partitions[largest_part]
            
            if len(nodes_in_part) <= 1:
                break
                
            # Create a subgraph
            subgraph = G.subgraph(nodes_in_part)
            
            # Use Fiedler vector to bisect
            try:
                laplacian = nx.laplacian_matrix(subgraph).toarray()
                eigenvalues, eigenvectors = np.linalg.eigh(laplacian)
                # Fiedler vector is the eigenvector corresponding to the second smallest eigenvalue
                fiedler_idx = np.argsort(eigenvalues)[1]
                fiedler_vector = eigenvectors[:, fiedler_idx]
                
                # Partition based on the sign of the Fiedler vector
                new_partition = []
                remaining = []
                
                for j, node in enumerate(nodes_in_part):
                    if fiedler_vector[j] >= 0:
                        new_partition.append(node)
                    else:
                        remaining.append(node)
            except:
                # Fallback to random bisection if eigendecomposition fails
                import random
                half_size = len(nodes_in_part) // 2
                random.shuffle(nodes_in_part)
                new_partition = nodes_in_part[:half_size]
                remaining = nodes_in_part[half_size:]
            
            # Update partitions
            partitions[largest_part] = remaining
            partitions[i] = new_partition
            
            # Update assignments
            for node in new_partition:
                assignments[node] = i
        
        return assignments
    
    def _greedy_partition(self, G: nx.Graph, n_parts: int) -> List[int]:
        """
        Partition a graph using a greedy algorithm.
        
        Args:
            G: The graph to partition
            n_parts: Number of partitions
            
        Returns:
            List of partition assignments for each node
        """
        assignments = [-1] * G.number_of_nodes()
        partition_sizes = [0] * n_parts
        
        # Sort nodes by degree (highest first)
        nodes_by_degree = sorted(G.nodes(), key=lambda n: G.degree(n), reverse=True)
        
        # Assign highest degree nodes to different partitions first
        for i in range(min(n_parts, len(nodes_by_degree))):
            assignments[nodes_by_degree[i]] = i
            partition_sizes[i] += 1
        
        # Assign remaining nodes
        for node in nodes_by_degree[n_parts:]:
            # Calculate "attraction" to each partition
            attraction = [0] * n_parts
            
            for neighbor in G.neighbors(node):
                if assignments[neighbor] != -1:
                    attraction[assignments[neighbor]] += G[node][neighbor].get('weight', 1)
            
            # Adjust by partition size (prefer smaller partitions)
            for i in range(n_parts):
                if partition_sizes[i] > 0:
                    attraction[i] = attraction[i] / partition_sizes[i]
            
            # Assign to most attractive partition
            best_partition = np.argmax(attraction)
            assignments[node] = best_partition
            partition_sizes[best_partition] += 1
        
        return assignments
    
    def _cut_by_layer_partition(self, 
                               circuit: QuantumCircuit, 
                               partition_kwargs: Dict[str, Any]) -> List[Subcircuit]:
        """
        Cut a circuit by layers.
        
        Args:
            circuit: The quantum circuit to cut
            partition_kwargs: Parameters for layer partitioning
            
        Returns:
            List of Subcircuit objects
        """
        # Convert to DAG for easier layer analysis
        dag = circuit_to_dag(circuit)
        
        # Get the layers
        layers = list(dag.layers())
        
        # Determine where to make the cuts
        max_layer_qubits = partition_kwargs.get('max_layer_qubits', self.max_subcircuit_width)
        
        # Group layers into subcircuits
        subcircuit_layers = []
        current_subcircuit = []
        active_qubits = set()
        
        for layer in layers:
            # Get qubits in this layer
            layer_qubits = set()
            for node in layer['graph'].op_nodes():
                for qarg in node.qargs:
                    layer_qubits.add(qarg.index)
            
            # Check if adding this layer would exceed the max width
            new_active_qubits = active_qubits.union(layer_qubits)
            
            if len(new_active_qubits) > max_layer_qubits and current_subcircuit:
                # Start a new subcircuit
                subcircuit_layers.append(current_subcircuit)
                current_subcircuit = [layer]
                active_qubits = layer_qubits
            else:
                # Add to current subcircuit
                current_subcircuit.append(layer)
                active_qubits = new_active_qubits
        
        # Add the last subcircuit
        if current_subcircuit:
            subcircuit_layers.append(current_subcircuit)
        
        # Create subcircuits from the layer groups
        subcircuits = []
        for idx, layers in enumerate(subcircuit_layers):
            subcircuit = self._create_subcircuit_from_layers(circuit, layers, idx)
            subcircuits.append(subcircuit)
        
        # Identify cuts between subcircuits
        self._identify_layer_cuts(subcircuits)
        
        return subcircuits
    
    def _create_subcircuit_from_layers(self, 
                                      original_circuit: QuantumCircuit,
                                      layers: List[Dict],
                                      subcircuit_index: int) -> Subcircuit:
        """
        Create a subcircuit from a group of layers.
        
        Args:
            original_circuit: The original quantum circuit
            layers: List of layers to include in this subcircuit
            subcircuit_index: Index of this subcircuit
            
        Returns:
            Subcircuit object
        """
        # Identify qubits used in these layers
        used_qubits = set()
        for layer in layers:
            for node in layer['graph'].op_nodes():
                for qarg in node.qargs:
                    used_qubits.add(qarg.index)
        
        # Create a mapping from original to subcircuit qubits
        orig_to_sub_map = {}
        sub_to_orig_map = {}
        
        for i, q in enumerate(sorted(used_qubits)):
            orig_to_sub_map[q] = i
            sub_to_orig_map[i] = q
        
        # Create the subcircuit
        n_qubits = len(used_qubits)
        subcircuit = QuantumCircuit(n_qubits)
        
        # Add gates from these layers
        flatten_layers = []
        for layer in layers:
            for node in layer['graph'].op_nodes():
                qargs = [orig_to_sub_map[qarg.index] for qarg in node.qargs]
                cargs = []  # We'll handle classical bits separately
                subcircuit.append(node.op, qargs, cargs)
        
        return Subcircuit(subcircuit, subcircuit_index, sub_to_orig_map)
    
    def _identify_layer_cuts(self, subcircuits: List[Subcircuit]):
        """
        Identify cuts between layer-based subcircuits.
        
        Args:
            subcircuits: List of subcircuits
            
        Modifies subcircuits in place to add cut information.
        """
        for i in range(len(subcircuits) - 1):
            current = subcircuits[i]
            next_subcircuit = subcircuits[i + 1]
            
            # Find qubits that are in both subcircuits
            current_qubits = set(current.original_qubit_mapping.values())
            next_qubits = set(next_subcircuit.original_qubit_mapping.values())
            shared_qubits = current_qubits.intersection(next_qubits)
            
            # Create cuts for these shared qubits
            for orig_qubit in shared_qubits:
                # Find the subcircuit indices
                for sub_q1, orig_q1 in current.original_qubit_mapping.items():
                    if orig_q1 == orig_qubit:
                        q1 = sub_q1
                        break
                
                for sub_q2, orig_q2 in next_subcircuit.original_qubit_mapping.items():
                    if orig_q2 == orig_qubit:
                        q2 = sub_q2
                        break
                
                # Create cut information
                cut1 = CircuitCutInfo(q1, current.circuit.num_qubits - 1, i, i + 1)
                cut2 = CircuitCutInfo(q2, 0, i + 1, i)
                
                # Add to subcircuits
                current.cuts.append(cut1)
                next_subcircuit.cuts.append(cut2)
                
                # Update neighbor information
                current.neighbor_subcircuits.add(i + 1)
                next_subcircuit.neighbor_subcircuits.add(i)
    
    def _create_subcircuits_from_partition(self, 
                                         circuit: QuantumCircuit, 
                                         partitioning: List[int]) -> List[Subcircuit]:
        """
        Create subcircuits based on a graph partitioning.
        
        Args:
            circuit: The original quantum circuit
            partitioning: List of partition assignments for each qubit
            
        Returns:
            List of subcircuits
        """
        # Count the number of partitions
        n_partitions = max(partitioning) + 1
        
        # Create empty subcircuits
        subcircuits = []
        for i in range(n_partitions):
            # Get qubits in this partition
            partition_qubits = [q for q, p in enumerate(partitioning) if p == i]
            
            # Create mapping from original to subcircuit qubits
            orig_to_sub_map = {}
            sub_to_orig_map = {}
            
            for j, q in enumerate(partition_qubits):
                orig_to_sub_map[q] = j
                sub_to_orig_map[j] = q
            
            # Create empty quantum circuit with appropriate number of qubits
            n_qubits = len(partition_qubits)
            subcircuit = QuantumCircuit(n_qubits)
            
            # Add to list
            subcircuits.append(Subcircuit(subcircuit, i, sub_to_orig_map))
        
        # Analyze the circuit to find cuts
        dag = circuit_to_dag(circuit)
        
        # For each multi-qubit gate that spans partitions, create cuts
        cut_points = []
        
        for node in dag.op_nodes():
            if len(node.qargs) > 1:
                # Check if this gate spans partitions
                # Extract qubit indices - handle both older Qiskit (index) and Qiskit 1.0+ (_index)
                qubits = []
                for qarg in node.qargs:
                    try:
                        q_idx = qarg.index if hasattr(qarg, 'index') else qarg._index
                        qubits.append(q_idx)
                    except AttributeError:
                        # As a fallback, try to get the integer index directly
                        try:
                            q_idx = int(qarg)
                            qubits.append(q_idx)
                        except (ValueError, TypeError):
                            logger.warning(f"Could not determine qubit index for {qarg}")
                            continue
                
                partitions = [partitioning[q] for q in qubits]
                
                if len(set(partitions)) > 1:
                    # This gate spans partitions, create a cut
                    for i in range(len(qubits)):
                        for j in range(i + 1, len(qubits)):
                            if partitions[i] != partitions[j]:
                                cut_points.append((qubits[i], qubits[j], partitions[i], partitions[j]))
        
        # Create cut information
        for q1, q2, p1, p2 in cut_points:
            # Map to subcircuit qubits
            sub_q1 = orig_to_sub_map.get(q1)
            sub_q2 = orig_to_sub_map.get(q2)
            
            if sub_q1 is not None and sub_q2 is not None:
                # Create cut information objects
                cut1 = CircuitCutInfo(sub_q1, 0, p1, p2)  # Simplified - in reality, need to find exact gate index
                cut2 = CircuitCutInfo(sub_q2, 0, p2, p1)  # Simplified
                
                # Add to subcircuits
                subcircuits[p1].cuts.append(cut1)
                subcircuits[p2].cuts.append(cut2)
                
                # Update neighbor information
                subcircuits[p1].neighbor_subcircuits.add(p2)
                subcircuits[p2].neighbor_subcircuits.add(p1)
        
        # Add gates to subcircuits
        self._distribute_gates_to_subcircuits(circuit, subcircuits, partitioning, orig_to_sub_map)
        
        return subcircuits
    
    def _distribute_gates_to_subcircuits(self, 
                                        circuit: QuantumCircuit,
                                        subcircuits: List[Subcircuit],
                                        partitioning: List[int],
                                        orig_to_sub_map: Dict[int, int]):
        """
        Distribute gates from the original circuit to the appropriate subcircuits.
        
        Args:
            circuit: The original quantum circuit
            subcircuits: List of subcircuits
            partitioning: List of partition assignments for each qubit
            orig_to_sub_map: Mapping from original to subcircuit qubit indices
            
        Modifies subcircuits in place to add gates.
        """
        # Process one instruction at a time
        for instruction, qargs, cargs in circuit.data:
            # Get qubit indices
            qubits = []
            for qarg in qargs:
                try:
                    q_idx = qarg.index if hasattr(qarg, 'index') else qarg._index
                    qubits.append(q_idx)
                except AttributeError:
                    # As a fallback, try to get the integer index directly
                    try:
                        q_idx = int(qarg)
                        qubits.append(q_idx)
                    except (ValueError, TypeError):
                        logger.warning(f"Could not determine qubit index for {qarg}")
                        continue
            
            # Get partitions for these qubits
            qubit_partitions = [partitioning[q] for q in qubits]
            
            # If all qubits are in the same partition, add the gate to that partition
            if len(set(qubit_partitions)) == 1:
                partition = qubit_partitions[0]
                
                # Map original qubits to subcircuit qubits
                sub_qubits = []
                for q in qubits:
                    # Find the subcircuit qubit index
                    for sub_q, orig_q in subcircuits[partition].original_qubit_mapping.items():
                        if orig_q == q:
                            sub_qubits.append(sub_q)
                            break
                
                # Add the gate to the subcircuit
                if len(sub_qubits) == len(qubits):  # Make sure all qubits were mapped
                    subcircuits[partition].circuit.append(instruction, sub_qubits, [])
            
            # If qubits span partitions, handle with cut
            else:
                # For simplicity, we're not implementing the full cut protocol here
                # In a full implementation, we would replace multi-partition gates with
                # the appropriate cutting protocol (e.g., Bell measurements, teleportation)
                logger.warning(f"Gate {instruction.name} spans partitions {qubit_partitions} - implementing cut")
                
                # For now, we'll add a barrier to each affected partition to mark the cut point
                for p in set(qubit_partitions):
                    # Get qubits in this partition
                    p_qubits = [q for i, q in enumerate(qubits) if qubit_partitions[i] == p]
                    
                    # Map to subcircuit qubits
                    sub_qubits = []
                    for q in p_qubits:
                        # Find the subcircuit qubit index
                        for sub_q, orig_q in subcircuits[p].original_qubit_mapping.items():
                            if orig_q == q:
                                sub_qubits.append(sub_q)
                                break
                    
                    # Add a barrier
                    if sub_qubits:
                        subcircuits[p].circuit.barrier(sub_qubits)
    
    def execute_subcircuits(self, 
                           subcircuits: List[Subcircuit],
                           backend: Optional[Any] = None,
                           **execution_options) -> List[Subcircuit]:
        """
        Execute the subcircuits and store results.
        
        Args:
            subcircuits: List of subcircuits to execute
            backend: Backend to use for execution
            **execution_options: Additional options for execution
            
        Returns:
            List of subcircuits with results
        """
        # This method would connect to IBM Quantum and execute the subcircuits
        # For this implementation, we'll just provide a framework
        
        logger.info(f"Executing {len(subcircuits)} subcircuits")
        
        for i, subcircuit in enumerate(subcircuits):
            logger.info(f"Preparing subcircuit {i} with {subcircuit.circuit.num_qubits} qubits")
            
            # Here we would actually execute the circuit on IBM Quantum hardware
            # For now, we'll just log the execution
            logger.info(f"Executing subcircuit {i}")
            
            # Store fake results for demonstration
            subcircuit.results = {"success": True}
        
        return subcircuits
    
    def reconstruct_results(self, subcircuits: List[Subcircuit]) -> Dict[str, Any]:
        """
        Reconstruct the full results from subcircuit results.
        
        Args:
            subcircuits: List of executed subcircuits
            
        Returns:
            Dictionary with reconstructed results
        """
        # This would implement the algorithm to recombine results
        # This is a simplified placeholder
        
        logger.info("Reconstructing results from subcircuits")
        
        # Check that all subcircuits have results
        for subcircuit in subcircuits:
            if subcircuit.results is None:
                raise ValueError(f"Subcircuit {subcircuit.index} has no results")
        
        # In a full implementation, we would combine the results using
        # the cut information and appropriate recombination techniques
        
        return {
            "reconstructed": True,
            "subcircuit_count": len(subcircuits),
            "cuts": sum(len(s.cuts) for s in subcircuits) // 2  # Each cut is counted twice
        }


class CircuitCuttingDemo:
    """
    Demonstration class for circuit cutting techniques.
    """
    
    @staticmethod
    def create_large_circuit(n_qubits: int, depth: int) -> QuantumCircuit:
        """
        Create a large test circuit.
        
        Args:
            n_qubits: Number of qubits
            depth: Circuit depth
            
        Returns:
            QuantumCircuit: A large test circuit
        """
        circuit = QuantumCircuit(n_qubits)
        
        # Add some entanglement with CX gates in a brick pattern
        for d in range(depth):
            # Even layer: CX between qubits (0,1), (2,3), etc.
            if d % 2 == 0:
                for i in range(0, n_qubits - 1, 2):
                    circuit.h(i)
                    circuit.cx(i, i + 1)
            # Odd layer: CX between qubits (1,2), (3,4), etc.
            else:
                for i in range(1, n_qubits - 1, 2):
                    circuit.h(i)
                    circuit.cx(i, i + 1)
        
        # Add measurements
        circuit.measure_all()
        
        return circuit
    
    @staticmethod
    def run_demo():
        """Run a demonstration of circuit cutting."""
        print("=== Quantum Circuit Cutting Demonstration ===\n")
        
        # Create a large circuit
        n_qubits = 30
        depth = 5
        print(f"Creating a {n_qubits}-qubit circuit with depth {depth}")
        circuit = CircuitCuttingDemo.create_large_circuit(n_qubits, depth)
        
        print(f"\nCircuit details:")
        print(f"  Qubits: {circuit.num_qubits}")
        print(f"  Depth: {circuit.depth()}")
        print(f"  Gate count: {len(circuit.data)}")
        
        # Initialize the circuit cutter
        max_subcircuit_width = 5
        print(f"\nInitializing circuit cutter with max subcircuit width of {max_subcircuit_width}")
        cutter = QuantumCircuitCutter(max_subcircuit_width=max_subcircuit_width)
        
        # Cut the circuit
        print("\nCutting the circuit...")
        subcircuits = cutter.cut_circuit(circuit)
        
        print(f"\nCircuit was cut into {len(subcircuits)} subcircuits:")
        for i, subcircuit in enumerate(subcircuits):
            print(f"  Subcircuit {i}:")
            print(f"    Qubits: {subcircuit.circuit.num_qubits}")
            print(f"    Depth: {subcircuit.circuit.depth()}")
            print(f"    Gate count: {len(subcircuit.circuit.data)}")
            print(f"    Cuts: {len(subcircuit.cuts)}")
            print(f"    Neighbor subcircuits: {subcircuit.neighbor_subcircuits}")
        
        # Execute subcircuits
        print("\nExecuting subcircuits...")
        executed_subcircuits = cutter.execute_subcircuits(subcircuits)
        
        # Reconstruct results
        print("\nReconstructing results...")
        results = cutter.reconstruct_results(executed_subcircuits)
        
        print("\nReconstruction complete!")
        print(f"  Subcircuits used: {results['subcircuit_count']}")
        print(f"  Cuts made: {results['cuts']}")
        print("\nDemonstration complete!")


if __name__ == "__main__":
    """
    Run a demonstration of circuit cutting techniques.
    """
    CircuitCuttingDemo.run_demo() 