circuit_optimizer.py

import numpy as np
from typing import List, Dict, Tuple, Optional
from dataclasses import dataclass
import networkx as nx
from scipy.optimize import minimize
import cirq
from qiskit import QuantumCircuit
from qiskit.quantum_info import Operator, process_fidelity
from qiskit.circuit.library import (
    QFT, HGate, CXGate, PhaseGate,
    RZZGate, RXXGate, CZGate
)

@dataclass
class OptimizationTarget:
    """Target specifications for circuit optimization"""
    unitary: Optional[np.ndarray] = None
    state_vector: Optional[np.ndarray] = None
    algorithm_type: Optional[str] = None  # e.g., 'qft', 'grover', 'vqe'
    max_depth: Optional[int] = None
    max_two_qubit_gates: Optional[int] = None
    required_fidelity: float = 0.99
    optimization_level: int = 2  # 0: Basic, 1: Standard, 2: Aggressive

@dataclass
class CircuitTemplate:
    """Quantum circuit template for pattern matching"""
    pattern: List[Tuple[str, List[int]]]  # (gate_name, qubits)
    replacement: List[Tuple[str, List[int]]]
    cost_reduction: float
    fidelity_impact: float

class QuantumCircuitOptimizer:
    def __init__(self, num_qubits: int, hardware_constraints=None):
        self.num_qubits = num_qubits
        self.hardware_constraints = hardware_constraints
        self.templates = self._initialize_templates()
        self.equivalence_graph = self._build_equivalence_graph()

    def _initialize_templates(self) -> List[CircuitTemplate]:
        """Initialize common circuit transformation templates"""
        return [
            # CNOT-based templates
            CircuitTemplate(
                pattern=[('cx', [0, 1]), ('cx', [0, 1])],
                replacement=[],
                cost_reduction=2.0,
                fidelity_impact=1.0
            ),
            # Hadamard cancellation
            CircuitTemplate(
                pattern=[('h', [0]), ('h', [0])],
                replacement=[],
                cost_reduction=2.0,
                fidelity_impact=1.0
            ),
            # CNOT-Rotation merge
            CircuitTemplate(
                pattern=[('cx', [0, 1]), ('rz', [1]), ('cx', [0, 1])],
                replacement=[('rzz', [0, 1])],
                cost_reduction=1.0,
                fidelity_impact=0.95
            ),
            # Phase gate combination
            CircuitTemplate(
                pattern=[('p', [0]), ('p', [0])],
                replacement=[('p', [0])],  # Combined phase
                cost_reduction=1.0,
                fidelity_impact=1.0
            )
        ]

    def _build_equivalence_graph(self) -> nx.DiGraph:
        """Build a graph of quantum circuit equivalences"""
        G = nx.DiGraph()
        
        # Add basic gate sequences and their equivalents
        basic_equivalences = [
            (('h', 'cx', 'h'), ('cz',)),
            (('rx', 'rz'), ('u3',)),
            (('h', 'h'), ()),
            (('cx', 'cx'), ()),
            (('rzz', 'ryy'), ('fsim',))
        ]
        
        for original, equivalent in basic_equivalences:
            G.add_edge(original, equivalent, weight=len(equivalent))
            
        return G

    def optimize_circuit(self, circuit: QuantumCircuit,
                        target: OptimizationTarget) -> QuantumCircuit:
        """Optimize quantum circuit based on target specifications"""
        optimized = circuit.copy()
        
        # Apply optimization stages based on level
        if target.optimization_level >= 0:
            optimized = self._apply_basic_optimization(optimized)
        
        if target.optimization_level >= 1:
            optimized = self._apply_template_matching(optimized)
            optimized = self._apply_commutation_rules(optimized)
        
        if target.optimization_level >= 2:
            optimized = self._apply_quantum_shannon_decomposition(optimized)
            optimized = self._optimize_for_hardware(optimized)
        
        return optimized

    def _apply_basic_optimization(self, circuit: QuantumCircuit) -> QuantumCircuit:
        """Apply basic circuit optimization techniques"""
        optimized = circuit.copy()
        
        # Gate cancellation
        optimized = self._cancel_adjacent_gates(optimized)
        
        # Merge single-qubit gates
        optimized = self._merge_single_qubit_gates(optimized)
        
        # Remove redundant gates
        optimized = self._remove_redundant_gates(optimized)
        
        return optimized

    def _apply_template_matching(self, circuit: QuantumCircuit) -> QuantumCircuit:
        """Apply template-based optimization"""
        optimized = circuit.copy()
        modified = True
        
        while modified:
            modified = False
            for template in self.templates:
                matches = self._find_template_matches(optimized, template)
                if matches:
                    optimized = self._apply_template_replacement(
                        optimized, matches, template
                    )
                    modified = True
                    
        return optimized

    def _apply_commutation_rules(self, circuit: QuantumCircuit) -> QuantumCircuit:
        """Apply quantum gate commutation rules for optimization"""
        optimized = circuit.copy()
        
        # Build dependency graph
        dep_graph = self._build_dependency_graph(optimized)
        
        # Find independent gate sets
        independent_sets = self._find_independent_gates(dep_graph)
        
        # Reorder gates to maximize parallelization
        optimized = self._reorder_gates(optimized, independent_sets)
        
        return optimized

    def _apply_quantum_shannon_decomposition(self, 
                                           circuit: QuantumCircuit) -> QuantumCircuit:
        """Apply Quantum Shannon Decomposition for circuit optimization"""
        # Convert circuit to unitary
        unitary = Operator(circuit).data
        
        # Perform cosine-sine decomposition
        optimized = self._cosine_sine_decomposition(unitary)
        
        # Convert back to quantum circuit
        return self._unitary_to_circuit(optimized)

    def _optimize_for_hardware(self, circuit: QuantumCircuit) -> QuantumCircuit:
        """Optimize circuit for specific hardware constraints"""
        if not self.hardware_constraints:
            return circuit
            
        optimized = circuit.copy()
        
        # Map to hardware topology
        optimized = self._map_to_topology(optimized)
        
        # Optimize for gate fidelities
        optimized = self._optimize_gate_fidelities(optimized)
        
        # Balance depth vs. gate count
        optimized = self._balance_depth_gates(optimized)
        
        return optimized

    def estimate_resources(self, circuit: QuantumCircuit) -> Dict[str, float]:
        """Estimate quantum resources required for the circuit"""
        return {
            'depth': circuit.depth(),
            'gate_count': len(circuit.data),
            'two_qubit_gate_count': sum(
                1 for inst in circuit.data if len(inst.qubits) == 2
            ),
            'estimated_time': self._estimate_execution_time(circuit),
            'estimated_error_rate': self._estimate_error_rate(circuit),
            'qubit_connectivity_score': self._calculate_connectivity_score(circuit)
        }

    def verify_equivalence(self, circuit1: QuantumCircuit,
                          circuit2: QuantumCircuit,
                          tolerance: float = 1e-6) -> bool:
        """Verify if two circuits are functionally equivalent"""
        # Convert circuits to operators
        op1 = Operator(circuit1)
        op2 = Operator(circuit2)
        
        # Calculate process fidelity
        fidelity = process_fidelity(op1, op2)
        
        return fidelity >= (1 - tolerance)

    def _find_template_matches(self, circuit: QuantumCircuit,
                             template: CircuitTemplate) -> List[int]:
        """Find matches of template patterns in the circuit"""
        matches = []
        circuit_gates = [
            (inst.operation.name, [q.index for q in inst.qubits])
            for inst in circuit.data
        ]
        
        pattern = template.pattern
        pattern_len = len(pattern)
        
        for i in range(len(circuit_gates) - pattern_len + 1):
            if all(
                circuit_gates[i + j][0] == pattern[j][0] and
                circuit_gates[i + j][1] == pattern[j][1]
                for j in range(pattern_len)
            ):
                matches.append(i)
                
        return matches

    def _build_dependency_graph(self, circuit: QuantumCircuit) -> nx.Graph:
        """Build a graph representing gate dependencies"""
        G = nx.Graph()
        
        for i, inst1 in enumerate(circuit.data):
            G.add_node(i, gate=inst1)
            for j, inst2 in enumerate(circuit.data[i+1:], i+1):
                if self._gates_commute(inst1, inst2):
                    G.add_edge(i, j)
                    
        return G

    def _gates_commute(self, gate1, gate2) -> bool:
        """Check if two gates commute"""
        # Get qubits affected by each gate
        qubits1 = set(q.index for q in gate1.qubits)
        qubits2 = set(q.index for q in gate2.qubits)
        
        # Gates on different qubits always commute
        if not qubits1.intersection(qubits2):
            return True
            
        # Check known commutation rules
        if gate1.operation.name == gate2.operation.name:
            if gate1.operation.name in ['z', 'rz', 'phase']:
                return True
                
        return False

    def _estimate_execution_time(self, circuit: QuantumCircuit) -> float:
        """Estimate circuit execution time based on gate times"""
        if not self.hardware_constraints:
            return sum(1.0 for _ in circuit.data)
            
        total_time = 0.0
        for inst in circuit.data:
            gate_name = inst.operation.name
            total_time += self.hardware_constraints.gate_times.get(gate_name, 1.0)
            
        return total_time

    def _estimate_error_rate(self, circuit: QuantumCircuit) -> float:
        """Estimate overall circuit error rate"""
        if not self.hardware_constraints:
            return len(circuit.data) * 0.001  # Basic estimate
            
        error_rate = 0.0
        for inst in circuit.data:
            if len(inst.qubits) == 2:
                q1, q2 = inst.qubits
                error_rate += self.hardware_constraints.error_rates.get(
                    (q1.index, q2.index), 0.01
                )
            else:
                error_rate += 0.001  # Single-qubit gate error
                
        return error_rate

    def _calculate_connectivity_score(self, circuit: QuantumCircuit) -> float:
        """Calculate how well the circuit matches hardware connectivity"""
        if not self.hardware_constraints:
            return 1.0
            
        score = 0.0
        total_two_qubit_gates = 0
        
        for inst in circuit.data:
            if len(inst.qubits) == 2:
                total_two_qubit_gates += 1
                q1, q2 = inst.qubits
                if q2.index in self.hardware_constraints.connectivity_map.get(
                    q1.index, []
                ):
                    score += 1.0
                    
        return score / total_two_qubit_gates if total_two_qubit_gates > 0 else 1.0

    def decompose_algorithm(self, algorithm_type: str,
                          params: Dict) -> QuantumCircuit:
        """Generate optimized circuits for common quantum algorithms"""
        if algorithm_type == 'qft':
            return self._optimize_qft(params.get('n_qubits', self.num_qubits))
        elif algorithm_type == 'grover':
            return self._optimize_grover(params)
        elif algorithm_type == 'vqe':
            return self._optimize_vqe(params)
        else:
            raise ValueError(f"Unknown algorithm type: {algorithm_type}")

    def _optimize_qft(self, n_qubits: int) -> QuantumCircuit:
        """Generate optimized Quantum Fourier Transform circuit"""
        qft = QFT(n_qubits)
        return self.optimize_circuit(
            qft,
            OptimizationTarget(
                algorithm_type='qft',
                optimization_level=2
            )
        )

    def _optimize_grover(self, params: Dict) -> QuantumCircuit:
        """Generate optimized Grover's algorithm circuit"""
        oracle = params.get('oracle')
        n_iterations = params.get('iterations', 1)
        
        circuit = QuantumCircuit(self.num_qubits)
        
        # Initialize superposition
        for i in range(self.num_qubits):
            circuit.h(i)
            
        # Apply Grover iterations
        for _ in range(n_iterations):
            # Oracle
            circuit.compose(oracle, inplace=True)
            
            # Diffusion operator
            for i in range(self.num_qubits):
                circuit.h(i)
            for i in range(self.num_qubits):
                circuit.x(i)
            circuit.h(self.num_qubits - 1)
            circuit.mct(
                list(range(self.num_qubits - 1)),
                self.num_qubits - 1
            )
            circuit.h(self.num_qubits - 1)
            for i in range(self.num_qubits):
                circuit.x(i)
            for i in range(self.num_qubits):
                circuit.h(i)
                
        return self.optimize_circuit(
            circuit,
            OptimizationTarget(
                algorithm_type='grover',
                optimization_level=2
            )
        )

    def _optimize_vqe(self, params: Dict) -> QuantumCircuit:
        """Generate optimized Variational Quantum Eigensolver circuit"""
        hamiltonian = params.get('hamiltonian')
        depth = params.get('depth', 3)
        
        circuit = QuantumCircuit(self.num_qubits)
        
        # Initial state preparation
        for i in range(self.num_qubits):
            circuit.ry(np.pi/4, i)
            
        # Variational layers
        for _ in range(depth):
            # Entangling layer
            for i in range(self.num_qubits - 1):
                circuit.cx(i, i + 1)
            
            # Rotation layer
            for i in range(self.num_qubits):
                circuit.ry(np.pi/4, i)
                circuit.rz(np.pi/4, i)
                
        return self.optimize_circuit(
            circuit,
            OptimizationTarget(
                algorithm_type='vqe',
                optimization_level=2
            )
        )