quantum_benchmark_harness.py

# -*- 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