dynamic_compiler.py

from typing import List, Dict, Tuple, Optional, Callable
import numpy as np
from qiskit import QuantumCircuit, pulse
from qiskit.quantum_info import Operator
import networkx as nx
from dataclasses import dataclass
import matplotlib.pyplot as plt
from collections import defaultdict

@dataclass
class PulseParameters:
    """Parameters for quantum pulse control"""
    amplitude: float
    duration: int
    phase: float
    frequency: float
    shape: str  # 'gaussian', 'square', 'drag', etc.

@dataclass
class CompilationConstraints:
    """Constraints for circuit compilation"""
    max_depth: Optional[int] = None
    max_two_qubit_gates: Optional[int] = None
    preferred_basis_gates: List[str] = None
    connectivity_map: Dict[int, List[int]] = None
    timing_constraints: Dict[str, float] = None

class DynamicCircuitCompiler:
    def __init__(self, optimization_level: int = 2):
        self.optimization_level = optimization_level
        self.pulse_library = self._initialize_pulse_library()
        self.compilation_cache = {}
        self.runtime_statistics = []

    def compile_circuit(self, 
                       circuit: QuantumCircuit,
                       constraints: CompilationConstraints) -> QuantumCircuit:
        """Dynamically compile quantum circuit with runtime optimization"""
        # Check cache for similar circuits
        cache_key = self._generate_cache_key(circuit)
        if cache_key in self.compilation_cache:
            return self._adapt_cached_circuit(
                self.compilation_cache[cache_key], constraints
            )
        
        # Perform multi-stage compilation
        intermediate = circuit
        
        if self.optimization_level >= 1:
            intermediate = self._optimize_gate_sequence(intermediate, constraints)
        
        if self.optimization_level >= 2:
            intermediate = self._optimize_pulse_schedules(intermediate)
        
        if self.optimization_level >= 3:
            intermediate = self._apply_hardware_specific_optimizations(
                intermediate, constraints
            )
        
        # Cache the compiled circuit
        self.compilation_cache[cache_key] = intermediate
        
        return intermediate

    def _initialize_pulse_library(self) -> Dict[str, PulseParameters]:
        """Initialize library of optimal pulse parameters"""
        return {
            'x90': PulseParameters(
                amplitude=0.1,
                duration=160,
                phase=0.0,
                frequency=5.0e9,
                shape='gaussian'
            ),
            'cx': PulseParameters(
                amplitude=0.2,
                duration=320,
                phase=0.0,
                frequency=5.2e9,
                shape='gaussian_square'
            )
        }

    def _generate_cache_key(self, circuit: QuantumCircuit) -> str:
        """Generate a unique key for circuit caching"""
        # Create a hash based on circuit structure
        circuit_signature = []
        
        for instruction in circuit.data:
            gate_info = (
                instruction.operation.name,
                tuple(q.index for q in instruction.qubits)
            )
            if hasattr(instruction.operation, 'params'):
                gate_info += tuple(instruction.operation.params)
            circuit_signature.append(gate_info)
            
        return str(hash(tuple(circuit_signature)))

    def _adapt_cached_circuit(self, 
                            cached_circuit: QuantumCircuit,
                            constraints: CompilationConstraints) -> QuantumCircuit:
        """Adapt a cached circuit to new constraints"""
        adapted = cached_circuit.copy()
        
        # Adjust for new constraints
        if constraints.max_depth and adapted.depth() > constraints.max_depth:
            adapted = self._reduce_circuit_depth(adapted, constraints.max_depth)
        
        if constraints.connectivity_map:
            adapted = self._adapt_to_connectivity(
                adapted, constraints.connectivity_map
            )
            
        return adapted

    def _optimize_gate_sequence(self, 
                              circuit: QuantumCircuit,
                              constraints: CompilationConstraints) -> QuantumCircuit:
        """Optimize the sequence of quantum gates"""
        optimized = circuit.copy()
        
        # Build dependency graph
        dep_graph = self._build_dependency_graph(optimized)
        
        # Find commuting gates
        commuting_sets = self._find_commuting_gates(dep_graph)
        
        # Reorder gates for optimal execution
        optimized = self._reorder_gates(optimized, commuting_sets, constraints)
        
        # Merge adjacent gates when possible
        optimized = self._merge_adjacent_gates(optimized)
        
        return optimized

    def _optimize_pulse_schedules(self, circuit: QuantumCircuit) -> QuantumCircuit:
        """Optimize pulse-level implementation of gates"""
        optimized = circuit.copy()
        
        # Convert to pulse schedule
        schedule = self._circuit_to_pulse_schedule(optimized)
        
        # Optimize pulse sequences
        schedule = self._optimize_pulse_sequence(schedule)
        
        # Convert back to circuit
        return self._pulse_schedule_to_circuit(schedule)

    def _apply_hardware_specific_optimizations(self,
                                            circuit: QuantumCircuit,
                                            constraints: CompilationConstraints) -> QuantumCircuit:
        """Apply hardware-specific optimizations"""
        optimized = circuit.copy()
        
        # Optimize for specific hardware constraints
        if constraints.connectivity_map:
            optimized = self._optimize_qubit_mapping(
                optimized, constraints.connectivity_map
            )
        
        if constraints.timing_constraints:
            optimized = self._optimize_timing(
                optimized, constraints.timing_constraints
            )
        
        # Apply basis gate decomposition
        if constraints.preferred_basis_gates:
            optimized = self._decompose_to_basis(
                optimized, constraints.preferred_basis_gates
            )
            
        return optimized

    def _build_dependency_graph(self, circuit: QuantumCircuit) -> nx.DiGraph:
        """Build a graph representing gate dependencies"""
        G = nx.DiGraph()
        
        for i, inst1 in enumerate(circuit.data):
            G.add_node(i, instruction=inst1)
            
            # Add edges for dependencies
            for j in range(i + 1, len(circuit.data)):
                inst2 = circuit.data[j]
                if self._gates_dependent(inst1, inst2):
                    G.add_edge(i, j)
                    
        return G

    def _gates_dependent(self, gate1, gate2) -> bool:
        """Check if two gates have dependencies"""
        qubits1 = set(q.index for q in gate1.qubits)
        qubits2 = set(q.index for q in gate2.qubits)
        return bool(qubits1.intersection(qubits2))

    def _find_commuting_gates(self, dep_graph: nx.DiGraph) -> List[set]:
        """Find sets of commuting gates"""
        commuting_sets = []
        visited = set()
        
        for node in dep_graph.nodes():
            if node not in visited:
                # Find all gates that commute with this one
                commuting = {node}
                for other in dep_graph.nodes():
                    if other not in visited and not (
                        dep_graph.has_edge(node, other) or
                        dep_graph.has_edge(other, node)
                    ):
                        commuting.add(other)
                
                commuting_sets.append(commuting)
                visited.update(commuting)
                
        return commuting_sets

    def _reorder_gates(self, circuit: QuantumCircuit,
                      commuting_sets: List[set],
                      constraints: CompilationConstraints) -> QuantumCircuit:
        """Reorder gates based on commutation relations"""
        reordered = QuantumCircuit(circuit.num_qubits)
        used_gates = set()
        
        # Process each commuting set
        for gate_set in commuting_sets:
            # Filter out already used gates
            available = gate_set - used_gates
            
            # Add gates in optimal order
            for gate_idx in sorted(available):
                instruction = circuit.data[gate_idx]
                reordered.append(instruction.operation, instruction.qubits)
                used_gates.add(gate_idx)
        
        return reordered

    def _merge_adjacent_gates(self, circuit: QuantumCircuit) -> QuantumCircuit:
        """Merge adjacent gates when possible"""
        merged = QuantumCircuit(circuit.num_qubits)
        i = 0
        
        while i < len(circuit.data):
            if i + 1 < len(circuit.data):
                # Try to merge with next gate
                merged_gate = self._try_merge_gates(
                    circuit.data[i], circuit.data[i + 1]
                )
                if merged_gate:
                    merged.append(merged_gate.operation, merged_gate.qubits)
                    i += 2
                    continue
            
            # If no merge possible, add current gate
            merged.append(circuit.data[i].operation, circuit.data[i].qubits)
            i += 1
            
        return merged

    def _try_merge_gates(self, gate1, gate2):
        """Try to merge two adjacent gates"""
        # Check if gates can be merged
        if (gate1.operation.name == gate2.operation.name and
            gate1.qubits == gate2.qubits):
            # Merge identical gates
            if gate1.operation.name in ['x', 'z']:
                return None  # They cancel out
            elif hasattr(gate1.operation, 'params'):
                # Combine parameters
                new_params = [
                    p1 + p2 for p1, p2 in zip(
                        gate1.operation.params,
                        gate2.operation.params
                    )
                ]
                return QuantumCircuit.Instruction(
                    gate1.operation.__class__(*new_params),
                    gate1.qubits,
                    []
                )
        
        return None

    def visualize_compilation_results(self, 
                                    original: QuantumCircuit,
                                    compiled: QuantumCircuit):
        """Visualize the results of compilation"""
        fig, axes = plt.subplots(2, 2, figsize=(15, 10))
        
        # Plot circuit depth comparison
        axes[0, 0].bar(['Original', 'Compiled'], 
                      [original.depth(), compiled.depth()])
        axes[0, 0].set_title('Circuit Depth')
        
        # Plot gate count comparison
        axes[0, 1].bar(['Original', 'Compiled'],
                      [len(original.data), len(compiled.data)])
        axes[0, 1].set_title('Gate Count')
        
        # Plot pulse schedule
        schedule = self._circuit_to_pulse_schedule(compiled)
        times = [inst.start_time for inst in schedule.instructions]
        channels = [inst.channel.index for inst in schedule.instructions]
        axes[1, 0].scatter(times, channels)
        axes[1, 0].set_title('Pulse Schedule')
        
        # Plot compilation statistics
        stats = self.analyze_compilation(original, compiled)
        metrics = list(stats.keys())
        values = [stats[k] for k in metrics]
        axes[1, 1].bar(range(len(metrics)), values)
        axes[1, 1].set_xticks(range(len(metrics)))
        axes[1, 1].set_xticklabels(metrics, rotation=45)
        axes[1, 1].set_title('Compilation Metrics')
        
        plt.tight_layout()
        plt.savefig('compilation_results.png')
        plt.close()

    def analyze_compilation(self, 
                          original: QuantumCircuit,
                          compiled: QuantumCircuit) -> Dict:
        """Analyze the results of compilation"""
        analysis = {
            'depth_reduction': (
                original.depth() - compiled.depth()
            ) / original.depth(),
            'gate_count_reduction': (
                len(original.data) - len(compiled.data)
            ) / len(original.data),
            'execution_time_improvement': self._estimate_execution_time_improvement(
                original, compiled
            ),
            'pulse_optimization': self._analyze_pulse_optimization(compiled)
        }
        
        # Update runtime statistics
        self.runtime_statistics.append(analysis)
        
        return analysis

    def _estimate_execution_time_improvement(self,
                                          original: QuantumCircuit,
                                          compiled: QuantumCircuit) -> float:
        """Estimate improvement in execution time"""
        original_time = sum(
            self.pulse_library.get(
                inst.operation.name,
                self._default_pulse_parameters(inst)
            ).duration
            for inst in original.data
        )
        
        compiled_time = sum(
            self.pulse_library.get(
                inst.operation.name,
                self._default_pulse_parameters(inst)
            ).duration
            for inst in compiled.data
        )
        
        return (original_time - compiled_time) / original_time

    def _analyze_pulse_optimization(self, circuit: QuantumCircuit) -> Dict:
        """Analyze pulse-level optimizations"""
        schedule = self._circuit_to_pulse_schedule(circuit)
        
        return {
            'total_duration': schedule.duration,
            'pulse_count': len(schedule.instructions),
            'average_pulse_duration': np.mean([
                inst.pulse.duration for inst in schedule.instructions
            ])
        }

    def _default_pulse_parameters(self, instruction) -> PulseParameters:
        """Get default pulse parameters for an instruction"""
        if len(instruction.qubits) == 1:
            return PulseParameters(
                amplitude=0.1,
                duration=160,
                phase=0.0,
                frequency=5.0e9,
                shape='gaussian'
            )
        else:
            return PulseParameters(
                amplitude=0.2,
                duration=320,
                phase=0.0,
                frequency=5.2e9,
                shape='gaussian_square'
            )