# -*- coding: utf-8 -*- """ Stage 4: Evaluation Harness → Quantum Benchmarking Classical benchmarks are static and sequential. Quantum benchmarking allows probabilistic, multi-dimensional scoring with parallel evaluation across languages and styles using quantum circuits. """ import numpy as np from typing import Dict, List, Tuple, Optional, Any, Callable import json import time from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister from qiskit.quantum_info import Statevector, random_statevector from qiskit_aer import AerSimulator import pennylane as qml from pennylane import numpy as pnp import logging from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass logger = logging.getLogger(__name__) @dataclass class QuantumBenchmarkResult: """Data class for quantum benchmark results.""" agent_id: str language: str alignment_loss: float diversity_score: float semantic_coverage: float quantum_coherence: float entanglement_measure: float overall_score: float measurement_counts: Dict[str, int] execution_time: float class QuantumBenchmarkHarness: """ Quantum-enhanced benchmarking harness for LIMIT-Graph evaluation. Simulates agent behavior across languages and styles using quantum circuits, scoring alignment loss, diversity, and semantic coverage in parallel. """ def __init__(self, max_qubits: int = 24, languages: List[str] = None): """Initialize quantum benchmark harness.""" self.max_qubits = max_qubits self.languages = languages or ['indonesian', 'arabic', 'spanish', 'english', 'chinese'] self.simulator = AerSimulator() # Benchmark state self.benchmark_circuits = {} self.evaluation_history = [] self.quantum_leaderboard = {} # PennyLane device for variational circuits self.dev = qml.device('default.qubit', wires=max_qubits) logger.info(f"Initialized QuantumBenchmarkHarness with {max_qubits} qubits for {len(self.languages)} languages") def create_quantum_benchmark_circuit(self, agent_params: Dict[str, Any], language: str, task_type: str) -> QuantumCircuit: """ Create quantum circuit for benchmarking agent performance. Args: agent_params: Agent parameters to benchmark language: Target language for evaluation task_type: Type of task (alignment, diversity, coverage) Returns: Quantum benchmark circuit """ # Determine circuit size based on agent complexity agent_weights = agent_params.get('weights', [1.0]) num_qubits = min(len(agent_weights), self.max_qubits) qreg = QuantumRegister(num_qubits, f'{task_type}_eval') creg = ClassicalRegister(num_qubits, 'measurements') circuit = QuantumCircuit(qreg, creg) # Initialize agent state for i, weight in enumerate(agent_weights[:num_qubits]): # Encode weight as rotation angle angle = weight * np.pi if abs(weight) <= 1 else np.pi circuit.ry(angle, qreg[i]) # Language-specific encoding with Chinese integration language_encodings = { 'indonesian': {'phase': np.pi/6, 'entangle_pattern': 'linear'}, 'arabic': {'phase': np.pi/4, 'entangle_pattern': 'circular'}, 'spanish': {'phase': np.pi/3, 'entangle_pattern': 'star'}, 'english': {'phase': np.pi/2, 'entangle_pattern': 'complete'}, 'chinese': {'phase': np.pi/5, 'entangle_pattern': 'hierarchical'} } lang_config = language_encodings.get(language, language_encodings['english']) # Apply language-specific phase for i in range(num_qubits): circuit.rz(lang_config['phase'], qreg[i]) # Create entanglement pattern if lang_config['entangle_pattern'] == 'linear': for i in range(num_qubits - 1): circuit.cx(qreg[i], qreg[i + 1]) elif lang_config['entangle_pattern'] == 'circular': for i in range(num_qubits - 1): circuit.cx(qreg[i], qreg[i + 1]) if num_qubits > 2: circuit.cx(qreg[num_qubits - 1], qreg[0]) elif lang_config['entangle_pattern'] == 'star': for i in range(1, num_qubits): circuit.cx(qreg[0], qreg[i]) elif lang_config['entangle_pattern'] == 'complete': for i in range(num_qubits): for j in range(i + 1, num_qubits): circuit.cx(qreg[i], qreg[j]) elif lang_config['entangle_pattern'] == 'hierarchical': # Chinese hierarchical pattern - tree-like structure for level in range(int(np.log2(num_qubits)) + 1): for i in range(0, num_qubits, 2**(level+1)): if i + 2**level < num_qubits: circuit.cx(qreg[i], qreg[i + 2**level]) # Task-specific operations if task_type == 'alignment': # Add alignment-specific gates for i in range(num_qubits): circuit.rx(np.pi/8, qreg[i]) elif task_type == 'diversity': # Add diversity-promoting gates for i in range(num_qubits): circuit.ry(np.pi/6, qreg[i]) elif task_type == 'coverage': # Add coverage-measuring gates for i in range(num_qubits): circuit.rz(np.pi/4, qreg[i]) circuit_key = f"{language}_{task_type}_{hash(str(agent_params))}" self.benchmark_circuits[circuit_key] = circuit logger.info(f"Created quantum benchmark circuit for {language} {task_type}: {num_qubits} qubits") return circuit def quantum_alignment_evaluation(self, agent_params: Dict[str, Any], reference_params: Dict[str, Any], language: str) -> float: """ Evaluate agent alignment using quantum interference. Args: agent_params: Agent parameters to evaluate reference_params: Reference/target parameters language: Evaluation language Returns: Quantum alignment score (0-1) """ # Create circuits for agent and reference agent_circuit = self.create_quantum_benchmark_circuit(agent_params, language, 'alignment') ref_circuit = self.create_quantum_benchmark_circuit(reference_params, language, 'alignment') # Create interference circuit num_qubits = min(agent_circuit.num_qubits, ref_circuit.num_qubits) qreg = QuantumRegister(num_qubits * 2, 'interference') circuit = QuantumCircuit(qreg) # Prepare agent state in first half for i in range(num_qubits): weights = agent_params.get('weights', [1.0]) if i < len(weights): angle = weights[i] * np.pi if abs(weights[i]) <= 1 else np.pi circuit.ry(angle, qreg[i]) # Prepare reference state in second half for i in range(num_qubits): ref_weights = reference_params.get('weights', [1.0]) if i < len(ref_weights): angle = ref_weights[i] * np.pi if abs(ref_weights[i]) <= 1 else np.pi circuit.ry(angle, qreg[i + num_qubits]) # Create interference through controlled operations for i in range(num_qubits): circuit.cx(qreg[i], qreg[i + num_qubits]) # Measure interference pattern circuit.measure_all() job = self.simulator.run(circuit, shots=1024) result = job.result() counts = result.get_counts() # Calculate alignment from interference pattern total_shots = sum(counts.values()) # Look for constructive interference (even parity states) constructive_counts = sum(count for state, count in counts.items() if state.count('1') % 2 == 0) alignment_score = constructive_counts / total_shots logger.info(f"Quantum alignment for {language}: {alignment_score:.4f}") return alignment_score def quantum_diversity_measurement(self, agent_params: Dict[str, Any], language: str, num_samples: int = 10) -> float: """ Measure agent diversity using quantum state sampling. Args: agent_params: Agent parameters language: Target language num_samples: Number of quantum samples Returns: Diversity score (0-1) """ circuit = self.create_quantum_benchmark_circuit(agent_params, language, 'diversity') # Sample multiple quantum states samples = [] for _ in range(num_samples): # Add random rotations for sampling sample_circuit = circuit.copy() for qubit in range(circuit.num_qubits): random_angle = np.random.uniform(0, np.pi/4) sample_circuit.ry(random_angle, qubit) sample_circuit.measure_all() job = self.simulator.run(sample_circuit, shots=100) result = job.result() counts = result.get_counts() # Get most probable state most_probable = max(counts.keys(), key=counts.get) samples.append(most_probable) # Calculate diversity as unique states ratio unique_samples = len(set(samples)) diversity_score = unique_samples / num_samples logger.info(f"Quantum diversity for {language}: {diversity_score:.4f}") return diversity_score def quantum_semantic_coverage(self, agent_params: Dict[str, Any], language: str, semantic_space_dim: int = 16) -> float: """ Measure semantic coverage using quantum state space exploration. Args: agent_params: Agent parameters language: Target language semantic_space_dim: Dimension of semantic space Returns: Coverage score (0-1) """ circuit = self.create_quantum_benchmark_circuit(agent_params, language, 'coverage') # Create semantic space exploration circuit num_qubits = min(semantic_space_dim, self.max_qubits) qreg = QuantumRegister(num_qubits, 'semantic_space') explore_circuit = QuantumCircuit(qreg) # Initialize uniform superposition for i in range(num_qubits): explore_circuit.h(qreg[i]) # Apply agent-specific transformations weights = agent_params.get('weights', [1.0]) for i, weight in enumerate(weights[:num_qubits]): angle = weight * np.pi if abs(weight) <= 1 else np.pi explore_circuit.ry(angle, qreg[i]) # Language-specific semantic modulation lang_phases = { 'indonesian': np.pi/6, 'arabic': np.pi/4, 'spanish': np.pi/3, 'english': np.pi/2, 'chinese': np.pi/5 } phase = lang_phases.get(language, np.pi/4) for i in range(num_qubits): explore_circuit.rz(phase, qreg[i]) # Measure coverage explore_circuit.measure_all() job = self.simulator.run(explore_circuit, shots=2048) result = job.result() counts = result.get_counts() # Calculate coverage as entropy of measurement distribution total_shots = sum(counts.values()) probabilities = np.array([count/total_shots for count in counts.values()]) # Normalized entropy as coverage measure max_entropy = np.log2(len(counts)) entropy = -np.sum(probabilities * np.log2(probabilities + 1e-10)) coverage_score = entropy / max_entropy if max_entropy > 0 else 0.0 logger.info(f"Quantum semantic coverage for {language}: {coverage_score:.4f}") return coverage_score def parallel_quantum_evaluation(self, agent_params: Dict[str, Any], reference_params: Dict[str, Any] = None) -> Dict[str, QuantumBenchmarkResult]: """ Perform parallel quantum evaluation across all languages. Args: agent_params: Agent parameters to evaluate reference_params: Reference parameters for alignment Returns: Dictionary of benchmark results per language """ if reference_params is None: # Create default reference parameters reference_params = {'weights': [0.5] * len(agent_params.get('weights', [1.0]))} results = {} def evaluate_language(language: str) -> QuantumBenchmarkResult: start_time = time.time() # Parallel quantum evaluations alignment_loss = 1.0 - self.quantum_alignment_evaluation(agent_params, reference_params, language) diversity_score = self.quantum_diversity_measurement(agent_params, language) semantic_coverage = self.quantum_semantic_coverage(agent_params, language) # Quantum coherence measurement circuit = self.create_quantum_benchmark_circuit(agent_params, language, 'alignment') job = self.simulator.run(circuit, shots=1024) result = job.result() counts = result.get_counts() # Calculate quantum coherence total_shots = sum(counts.values()) probabilities = np.array([count/total_shots for count in counts.values()]) coherence = 1.0 - (-np.sum(probabilities * np.log2(probabilities + 1e-10)) / np.log2(len(counts))) # Entanglement measure (simplified) entanglement = min(1.0, len([s for s in counts.keys() if s.count('1') > 1]) / len(counts)) # Overall score (weighted combination) overall_score = ( 0.3 * (1.0 - alignment_loss) + 0.25 * diversity_score + 0.25 * semantic_coverage + 0.1 * coherence + 0.1 * entanglement ) execution_time = time.time() - start_time return QuantumBenchmarkResult( agent_id=agent_params.get('id', 'unknown'), language=language, alignment_loss=alignment_loss, diversity_score=diversity_score, semantic_coverage=semantic_coverage, quantum_coherence=coherence, entanglement_measure=entanglement, overall_score=overall_score, measurement_counts=counts, execution_time=execution_time ) # Parallel execution across languages with ThreadPoolExecutor(max_workers=len(self.languages)) as executor: future_to_lang = {executor.submit(evaluate_language, lang): lang for lang in self.languages} for future in future_to_lang: language = future_to_lang[future] try: result = future.result() results[language] = result except Exception as e: logger.error(f"Evaluation failed for {language}: {e}") # Create fallback result results[language] = QuantumBenchmarkResult( agent_id=agent_params.get('id', 'unknown'), language=language, alignment_loss=1.0, diversity_score=0.0, semantic_coverage=0.0, quantum_coherence=0.0, entanglement_measure=0.0, overall_score=0.0, measurement_counts={}, execution_time=0.0 ) # Store in evaluation history self.evaluation_history.append({ 'agent_params': agent_params, 'results': results, 'timestamp': time.time() }) logger.info(f"Parallel quantum evaluation completed for {len(results)} languages") return results def update_quantum_leaderboard(self, agent_id: str, results: Dict[str, QuantumBenchmarkResult]): """ Update quantum-aware leaderboard with new results. Args: agent_id: Agent identifier results: Benchmark results per language """ # Calculate aggregate scores overall_scores = [result.overall_score for result in results.values()] aggregate_score = np.mean(overall_scores) # Calculate quantum metrics coherence_scores = [result.quantum_coherence for result in results.values()] entanglement_scores = [result.entanglement_measure for result in results.values()] leaderboard_entry = { 'agent_id': agent_id, 'aggregate_score': aggregate_score, 'language_scores': {lang: result.overall_score for lang, result in results.items()}, 'quantum_coherence': np.mean(coherence_scores), 'quantum_entanglement': np.mean(entanglement_scores), 'alignment_performance': np.mean([1.0 - result.alignment_loss for result in results.values()]), 'diversity_performance': np.mean([result.diversity_score for result in results.values()]), 'coverage_performance': np.mean([result.semantic_coverage for result in results.values()]), 'total_execution_time': sum(result.execution_time for result in results.values()), 'languages_evaluated': list(results.keys()), 'timestamp': time.time() } self.quantum_leaderboard[agent_id] = leaderboard_entry logger.info(f"Updated quantum leaderboard for {agent_id}: score = {aggregate_score:.4f}") def get_quantum_leaderboard(self, top_k: int = 10) -> List[Dict[str, Any]]: """ Get top-k entries from quantum leaderboard. Args: top_k: Number of top entries to return Returns: Sorted leaderboard entries """ sorted_entries = sorted( self.quantum_leaderboard.values(), key=lambda x: x['aggregate_score'], reverse=True ) return sorted_entries[:top_k] def export_benchmark_results(self, filepath: str): """Export benchmark results to JSON file.""" export_data = { 'quantum_leaderboard': self.quantum_leaderboard, 'evaluation_history': [ { 'agent_params': entry['agent_params'], 'results': { lang: { 'agent_id': result.agent_id, 'language': result.language, 'alignment_loss': result.alignment_loss, 'diversity_score': result.diversity_score, 'semantic_coverage': result.semantic_coverage, 'quantum_coherence': result.quantum_coherence, 'entanglement_measure': result.entanglement_measure, 'overall_score': result.overall_score, 'execution_time': result.execution_time } for lang, result in entry['results'].items() }, 'timestamp': entry['timestamp'] } for entry in self.evaluation_history ], 'benchmark_config': { 'max_qubits': self.max_qubits, 'languages': self.languages, 'total_evaluations': len(self.evaluation_history) } } with open(filepath, 'w') as f: json.dump(export_data, f, indent=2) logger.info(f"Exported benchmark results to {filepath}") def get_quantum_benchmark_metrics(self) -> Dict[str, Any]: """Get comprehensive metrics for quantum benchmarking.""" metrics = { 'max_qubits': self.max_qubits, 'languages_supported': len(self.languages), 'total_evaluations': len(self.evaluation_history), 'benchmark_circuits_created': len(self.benchmark_circuits), 'leaderboard_entries': len(self.quantum_leaderboard), 'quantum_speedup_factor': len(self.languages) ** 2, # Parallel evaluation advantage } if self.evaluation_history: # Analyze evaluation performance execution_times = [] overall_scores = [] for entry in self.evaluation_history: for result in entry['results'].values(): execution_times.append(result.execution_time) overall_scores.append(result.overall_score) metrics.update({ 'average_execution_time': np.mean(execution_times), 'average_overall_score': np.mean(overall_scores), 'score_variance': np.var(overall_scores), 'evaluation_efficiency': len(self.languages) / np.mean(execution_times) if execution_times else 0 }) return metrics