Create pauli_term_grouping.py

src/openfermion/utils/pauli_term_grouping.py

@@ -0,0 +1,195 @@
+"""
+Optimized Pauli term grouping strategies for measurement reduction in fermionic simulations.
+Based on research showing up to 50% measurement reduction compared to traditional techniques.
+"""
+
+import numpy as np
+from openfermion.ops import QubitOperator
+from openfermion.utils import commutator
+from typing import List, Dict, Set, Tuple
+import networkx as nx
+from collections import defaultdict
+
+class OptimizedPauliGrouper:
+    """
+    Advanced Pauli term grouping with overlapping strategy for simultaneous measurements.
+
+    This implementation reduces measurement requirements by leveraging commuting Pauli terms
+    and introduces overlapping groupings for enhanced parallelization.
+    """
+
+    def __init__(self, hamiltonian: QubitOperator, overlap_threshold: float = 0.7):
+        """
+        Initialize the optimized Pauli grouper.
+
+        Args:
+            hamiltonian: QubitOperator representing the molecular Hamiltonian
+            overlap_threshold: Threshold for determining overlapping groups (0.0-1.0)
+        """
+        self.hamiltonian = hamiltonian
+        self.overlap_threshold = overlap_threshold
+        self.pauli_terms = list(hamiltonian.terms.keys())
+        self.coefficients = list(hamiltonian.terms.values())
+
+    def create_overlapping_groups(self) -> List[List[int]]:
+        """
+        Create overlapping groups of commuting Pauli terms for parallel measurement.
+
+        Returns:
+            List of groups, where each group contains indices of commuting Pauli terms
+        """
+        # Build commutation graph
+        commutation_graph = self._build_commutation_graph()
+
+        # Find maximal cliques (groups of mutually commuting terms)
+        cliques = list(nx.find_cliques(commutation_graph))
+
+        # Sort cliques by size (prioritize larger groups)
+        cliques.sort(key=len, reverse=True)
+
+        # Create overlapping groups based on threshold
+        overlapping_groups = self._create_overlaps(cliques)
+
+        return overlapping_groups
+
+    def _build_commutation_graph(self) -> nx.Graph:
+        """Build graph where edges connect commuting Pauli terms."""
+        graph = nx.Graph()
+        n_terms = len(self.pauli_terms)
+
+        # Add all terms as nodes
+        graph.add_nodes_from(range(n_terms))
+
+        # Add edges between commuting terms
+        for i in range(n_terms):
+            for j in range(i + 1, n_terms):
+                if self._pauli_terms_commute(self.pauli_terms[i], self.pauli_terms[j]):
+                    graph.add_edge(i, j)
+
+        return graph
+
+    def _pauli_terms_commute(self, term1: Tuple, term2: Tuple) -> bool:
+        """Check if two Pauli terms commute."""
+        if not term1 or not term2:  # Identity terms always commute
+            return True
+
+        # Count anti-commuting pairs
+        anti_commuting_count = 0
+
+        # Get all qubits involved in both terms
+        qubits1 = {qubit for qubit, _ in term1}
+        qubits2 = {qubit for qubit, _ in term2}
+        common_qubits = qubits1.intersection(qubits2)
+
+        # Check anti-commutation for each common qubit
+        for qubit in common_qubits:
+            pauli1 = dict(term1)[qubit]
+            pauli2 = dict(term2)[qubit]
+
+            # X and Y anti-commute, Y and Z anti-commute, X and Z anti-commute
+            if (pauli1, pauli2) in [('X', 'Y'), ('Y', 'X'), ('Y', 'Z'), 
+                                   ('Z', 'Y'), ('X', 'Z'), ('Z', 'X')]:
+                anti_commuting_count += 1
+
+        # Terms commute if even number of anti-commuting pairs
+        return anti_commuting_count % 2 == 0
+
+    def _create_overlaps(self, cliques: List[List[int]]) -> List[List[int]]:
+        """Create overlapping groups based on similarity threshold."""
+        overlapping_groups = []
+        used_terms = set()
+
+        for clique in cliques:
+            if len(clique) >= 2:  # Only consider non-trivial cliques
+                # Check overlap with existing groups
+                overlap_found = False
+                for existing_group in overlapping_groups:
+                    overlap_ratio = len(set(clique) & set(existing_group)) / len(set(clique) | set(existing_group))
+                    if overlap_ratio >= self.overlap_threshold:
+                        # Merge with existing group
+                        existing_group.extend([term for term in clique if term not in existing_group])
+                        overlap_found = True
+                        break
+
+                if not overlap_found:
+                    overlapping_groups.append(clique)
+                    used_terms.update(clique)
+
+        # Add remaining terms as individual groups
+        for i, term in enumerate(self.pauli_terms):
+            if i not in used_terms:
+                overlapping_groups.append([i])
+
+        return overlapping_groups
+
+    def estimate_measurement_reduction(self) -> Dict[str, float]:
+        """
+        Estimate measurement reduction compared to individual term measurement.
+
+        Returns:
+            Dictionary with reduction metrics
+        """
+        groups = self.create_overlapping_groups()
+        individual_measurements = len(self.pauli_terms)
+        grouped_measurements = len(groups)
+
+        # Calculate average group size
+        avg_group_size = sum(len(group) for group in groups) / len(groups)
+
+        # Estimate reduction based on group efficiency
+        measurement_reduction = 1 - (grouped_measurements / individual_measurements)
+
+        return {
+            'individual_measurements': individual_measurements,
+            'grouped_measurements': grouped_measurements,
+            'measurement_reduction_ratio': measurement_reduction,
+            'average_group_size': avg_group_size,
+            'largest_group_size': max(len(group) for group in groups),
+            'estimated_speedup': individual_measurements / grouped_measurements
+        }
+
+def optimized_pauli_grouping(hamiltonian: QubitOperator, 
+                           overlap_threshold: float = 0.7) -> Tuple[List[List[int]], Dict]:
+    """
+    Convenience function for optimized Pauli term grouping.
+
+    Args:
+        hamiltonian: QubitOperator representing the molecular Hamiltonian
+        overlap_threshold: Threshold for overlapping groups
+
+    Returns:
+        Tuple of (groups, metrics) where groups contains indices of commuting terms
+    """
+    grouper = OptimizedPauliGrouper(hamiltonian, overlap_threshold)
+    groups = grouper.create_overlapping_groups()
+    metrics = grouper.estimate_measurement_reduction()
+
+    return groups, metrics
+
+# Example usage for testing
+def demo_pauli_grouping():
+    """Demonstrate the improved Pauli grouping on a molecular system."""
+    from openfermion.chem import MolecularData
+    from openfermion.transforms import get_fermion_operator, jordan_wigner
+
+    # Create a simple molecular system (H2)
+    geometry = [('H', (0., 0., 0.)), ('H', (0., 0., 0.7414))]
+    molecule = MolecularData(geometry, 'sto-3g', 1, 0)
+
+    # Get the Hamiltonian
+    molecular_hamiltonian = molecule.get_molecular_hamiltonian()
+    fermion_hamiltonian = get_fermion_operator(molecular_hamiltonian)
+    qubit_hamiltonian = jordan_wigner(fermion_hamiltonian)
+
+    # Apply optimized grouping
+    groups, metrics = optimized_pauli_grouping(qubit_hamiltonian)
+
+    print(f"Original terms: {metrics['individual_measurements']}")
+    print(f"Grouped measurements: {metrics['grouped_measurements']}")
+    print(f"Measurement reduction: {metrics['measurement_reduction_ratio']:.2%}")
+    print(f"Estimated speedup: {metrics['estimated_speedup']:.2f}x")
+
+    return groups, metrics
+
+if __name__ == "__main__":
+    demo_pauli_grouping()