# -*- coding: utf-8 -*- """ Stage 5: Visual Identity & Provenance → Quantum Traceability Classical provenance is linear. Quantum provenance allows branching, reversible trace paths using quantum hashing for model lineage and quantum fingerprints for visual identity. """ import numpy as np from typing import Dict, List, Tuple, Optional, Any, Union import hashlib import json import time from dataclasses import dataclass, asdict from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister from qiskit.quantum_info import Statevector, random_statevector from qiskit_aer import AerSimulator import logging logger = logging.getLogger(__name__) @dataclass class QuantumProvenanceRecord: """Data class for quantum provenance records.""" record_id: str parent_id: Optional[str] model_hash: str quantum_fingerprint: str visual_identity_hash: str operation_type: str parameters: Dict[str, Any] timestamp: float quantum_state: List[complex] entanglement_links: List[str] reversibility_key: str class QuantumProvenanceTracker: """ Quantum-enhanced provenance tracking for AI Research Agent. Uses quantum hashing for model lineage and encodes visual identity as quantum fingerprints with entangled logo states for traceability. """ def __init__(self, max_qubits: int = 20, hash_precision: int = 256): """Initialize quantum provenance tracker.""" self.max_qubits = max_qubits self.hash_precision = hash_precision self.simulator = AerSimulator() # Provenance state self.provenance_graph = {} self.quantum_fingerprints = {} self.visual_identities = {} self.entanglement_registry = {} self.reversibility_cache = {} logger.info(f"Initialized QuantumProvenanceTracker with {max_qubits} qubits, {hash_precision}-bit precision") def create_quantum_hash(self, data: Union[str, Dict, List], salt: str = None) -> str: """ Create quantum-enhanced hash for data integrity. Args: data: Data to hash salt: Optional salt for hashing Returns: Quantum hash string """ # Convert data to string representation if isinstance(data, (dict, list)): data_str = json.dumps(data, sort_keys=True) else: data_str = str(data) if salt: data_str = f"{data_str}:{salt}" # Classical hash as base classical_hash = hashlib.sha256(data_str.encode()).hexdigest() # Create quantum circuit for hash enhancement num_qubits = min(len(classical_hash) // 4, self.max_qubits) # 4 hex chars per qubit qreg = QuantumRegister(num_qubits, 'hash') circuit = QuantumCircuit(qreg) # Encode classical hash into quantum state for i, hex_char in enumerate(classical_hash[:num_qubits * 4:4]): hex_value = int(hex_char, 16) # Convert to rotation angle angle = (hex_value / 15.0) * np.pi circuit.ry(angle, qreg[i]) # Create quantum entanglement for hash integrity for i in range(num_qubits - 1): circuit.cx(qreg[i], qreg[i + 1]) # Add quantum randomness for i in range(num_qubits): circuit.rz(np.pi / (i + 1), qreg[i]) # Measure quantum state circuit.measure_all() job = self.simulator.run(circuit, shots=1) result = job.result() counts = result.get_counts() quantum_measurement = list(counts.keys())[0] # Combine classical and quantum hashes quantum_hash = f"q{classical_hash[:32]}{quantum_measurement}" logger.debug(f"Created quantum hash: {quantum_hash[:16]}...") return quantum_hash def generate_quantum_fingerprint(self, model_params: Dict[str, Any], visual_elements: Dict[str, Any] = None) -> str: """ Generate quantum fingerprint for model and visual identity. Args: model_params: Model parameters to fingerprint visual_elements: Visual identity elements (colors, logos, etc.) Returns: Quantum fingerprint string """ # Create quantum circuit for fingerprinting num_qubits = min(self.max_qubits, 16) # Limit for fingerprint qreg = QuantumRegister(num_qubits, 'fingerprint') circuit = QuantumCircuit(qreg) # Initialize superposition for i in range(num_qubits): circuit.h(qreg[i]) # Encode model parameters weights = model_params.get('weights', [1.0]) for i, weight in enumerate(weights[:num_qubits]): angle = weight * np.pi if abs(weight) <= 1 else np.pi circuit.ry(angle, qreg[i]) # Encode visual elements if provided if visual_elements: colors = visual_elements.get('colors', []) for i, color in enumerate(colors[:num_qubits]): if isinstance(color, str): # Convert color to numeric value color_value = sum(ord(c) for c in color) % 256 angle = (color_value / 255.0) * np.pi circuit.rz(angle, qreg[i]) # Create entanglement pattern for uniqueness for i in range(num_qubits - 1): circuit.cx(qreg[i], qreg[i + 1]) # Add model-specific phase model_id = model_params.get('id', 'default') model_phase = (hash(model_id) % 1000) / 1000 * 2 * np.pi circuit.rz(model_phase, qreg[0]) # Measure fingerprint circuit.measure_all() job = self.simulator.run(circuit, shots=1) result = job.result() counts = result.get_counts() fingerprint_bits = list(counts.keys())[0] # Convert to hex fingerprint fingerprint_int = int(fingerprint_bits, 2) fingerprint_hex = f"qf{fingerprint_int:0{num_qubits//4}x}" # Store fingerprint fingerprint_key = self.create_quantum_hash(model_params) self.quantum_fingerprints[fingerprint_key] = { 'fingerprint': fingerprint_hex, 'model_params': model_params, 'visual_elements': visual_elements, 'creation_time': time.time(), 'quantum_circuit': circuit } logger.info(f"Generated quantum fingerprint: {fingerprint_hex}") return fingerprint_hex def create_entangled_logo_states(self, logo_variants: List[Dict[str, Any]]) -> QuantumCircuit: """ Create entangled quantum states for logo variants. Args: logo_variants: List of logo variant specifications Returns: Quantum circuit with entangled logo states """ num_variants = min(len(logo_variants), self.max_qubits) qreg = QuantumRegister(num_variants, 'logo_variants') circuit = QuantumCircuit(qreg) # Create GHZ state for maximum entanglement circuit.h(qreg[0]) for i in range(1, num_variants): circuit.cx(qreg[0], qreg[i]) # Encode variant-specific features for i, variant in enumerate(logo_variants[:num_variants]): # Encode color scheme colors = variant.get('colors', ['#000000']) color_hash = hash(str(colors)) % 1000 color_phase = (color_hash / 1000) * 2 * np.pi circuit.rz(color_phase, qreg[i]) # Encode style elements style = variant.get('style', 'default') style_angle = (hash(style) % 100) / 100 * np.pi circuit.ry(style_angle, qreg[i]) # Store entangled logo circuit logo_key = self.create_quantum_hash(logo_variants) self.visual_identities[logo_key] = { 'variants': logo_variants, 'entangled_circuit': circuit, 'creation_time': time.time() } logger.info(f"Created entangled logo states for {num_variants} variants") return circuit def record_provenance(self, operation_type: str, model_params: Dict[str, Any], parent_record_id: str = None, visual_elements: Dict[str, Any] = None) -> str: """ Record quantum provenance for an operation. Args: operation_type: Type of operation (train, fine-tune, merge, etc.) model_params: Current model parameters parent_record_id: ID of parent record for lineage visual_elements: Visual identity elements Returns: Provenance record ID """ # Generate unique record ID record_id = self.create_quantum_hash({ 'operation': operation_type, 'params': model_params, 'timestamp': time.time() }) # Create quantum fingerprint fingerprint = self.generate_quantum_fingerprint(model_params, visual_elements) # Generate model hash model_hash = self.create_quantum_hash(model_params) # Create visual identity hash visual_hash = self.create_quantum_hash(visual_elements) if visual_elements else "none" # Create quantum state for this record num_qubits = min(self.max_qubits, 12) quantum_state = random_statevector(2**num_qubits) # Generate reversibility key for quantum operations reversibility_key = self.create_quantum_hash({ 'record_id': record_id, 'operation': operation_type, 'timestamp': time.time() }) # Create provenance record provenance_record = QuantumProvenanceRecord( record_id=record_id, parent_id=parent_record_id, model_hash=model_hash, quantum_fingerprint=fingerprint, visual_identity_hash=visual_hash, operation_type=operation_type, parameters=model_params, timestamp=time.time(), quantum_state=quantum_state.data.tolist(), entanglement_links=[], reversibility_key=reversibility_key ) # Store in provenance graph self.provenance_graph[record_id] = provenance_record # Create entanglement with parent if exists if parent_record_id and parent_record_id in self.provenance_graph: self._create_provenance_entanglement(parent_record_id, record_id) # Store reversibility information self.reversibility_cache[reversibility_key] = { 'record_id': record_id, 'inverse_operation': self._get_inverse_operation(operation_type), 'restoration_params': model_params.copy() } logger.info(f"Recorded quantum provenance: {record_id[:16]}... for {operation_type}") return record_id def _create_provenance_entanglement(self, parent_id: str, child_id: str): """Create quantum entanglement between provenance records.""" if parent_id not in self.provenance_graph or child_id not in self.provenance_graph: return # Update entanglement links self.provenance_graph[parent_id].entanglement_links.append(child_id) self.provenance_graph[child_id].entanglement_links.append(parent_id) # Store in entanglement registry entanglement_key = f"{parent_id}:{child_id}" self.entanglement_registry[entanglement_key] = { 'parent': parent_id, 'child': child_id, 'entanglement_strength': np.random.random(), # Quantum correlation strength 'creation_time': time.time() } logger.debug(f"Created provenance entanglement: {parent_id[:8]}...:{child_id[:8]}...") def trace_lineage(self, record_id: str, max_depth: int = 10) -> Dict[str, Any]: """ Trace quantum lineage for a provenance record. Args: record_id: Starting record ID max_depth: Maximum trace depth Returns: Lineage trace information """ if record_id not in self.provenance_graph: return {} lineage = { 'root_record': record_id, 'trace_path': [], 'quantum_correlations': [], 'branching_points': [], 'total_depth': 0 } # Breadth-first search through provenance graph visited = set() queue = [(record_id, 0)] while queue and len(lineage['trace_path']) < max_depth: current_id, depth = queue.pop(0) if current_id in visited: continue visited.add(current_id) record = self.provenance_graph[current_id] # Add to trace path lineage['trace_path'].append({ 'record_id': current_id, 'operation_type': record.operation_type, 'timestamp': record.timestamp, 'depth': depth, 'quantum_fingerprint': record.quantum_fingerprint }) # Check for branching (multiple children) children = [link for link in record.entanglement_links if link in self.provenance_graph and self.provenance_graph[link].parent_id == current_id] if len(children) > 1: lineage['branching_points'].append({ 'parent_id': current_id, 'children': children, 'branch_count': len(children) }) # Add quantum correlations for link in record.entanglement_links: if link in self.provenance_graph: entanglement_key = f"{current_id}:{link}" if entanglement_key in self.entanglement_registry: correlation = self.entanglement_registry[entanglement_key] lineage['quantum_correlations'].append(correlation) # Add parent to queue if record.parent_id and record.parent_id not in visited: queue.append((record.parent_id, depth + 1)) # Add children to queue for child in children: if child not in visited: queue.append((child, depth + 1)) lineage['total_depth'] = max([item['depth'] for item in lineage['trace_path']], default=0) logger.info(f"Traced lineage for {record_id[:16]}...: {len(lineage['trace_path'])} records") return lineage def verify_quantum_integrity(self, record_id: str) -> Dict[str, Any]: """ Verify quantum integrity of a provenance record. Args: record_id: Record ID to verify Returns: Integrity verification results """ if record_id not in self.provenance_graph: return {'valid': False, 'error': 'Record not found'} record = self.provenance_graph[record_id] # Verify quantum fingerprint regenerated_fingerprint = self.generate_quantum_fingerprint( record.parameters, self.visual_identities.get(record.visual_identity_hash, {}).get('variants') ) fingerprint_valid = regenerated_fingerprint == record.quantum_fingerprint # Verify model hash regenerated_model_hash = self.create_quantum_hash(record.parameters) model_hash_valid = regenerated_model_hash == record.model_hash # Verify quantum state integrity quantum_state = np.array(record.quantum_state) state_norm = np.linalg.norm(quantum_state) state_valid = abs(state_norm - 1.0) < 1e-6 # Valid quantum state should be normalized # Verify entanglement links entanglement_valid = all( link in self.provenance_graph for link in record.entanglement_links ) integrity_result = { 'record_id': record_id, 'valid': fingerprint_valid and model_hash_valid and state_valid and entanglement_valid, 'fingerprint_valid': fingerprint_valid, 'model_hash_valid': model_hash_valid, 'quantum_state_valid': state_valid, 'entanglement_valid': entanglement_valid, 'state_norm': float(state_norm), 'verification_time': time.time() } logger.info(f"Verified quantum integrity for {record_id[:16]}...: {'VALID' if integrity_result['valid'] else 'INVALID'}") return integrity_result def reverse_operation(self, reversibility_key: str) -> Dict[str, Any]: """ Reverse a quantum operation using reversibility key. Args: reversibility_key: Key for operation reversal Returns: Reversal operation results """ if reversibility_key not in self.reversibility_cache: return {'success': False, 'error': 'Reversibility key not found'} reversal_info = self.reversibility_cache[reversibility_key] record_id = reversal_info['record_id'] if record_id not in self.provenance_graph: return {'success': False, 'error': 'Original record not found'} original_record = self.provenance_graph[record_id] # Create reversed operation record reversed_params = reversal_info['restoration_params'] inverse_operation = reversal_info['inverse_operation'] # Record the reversal as new provenance entry reversal_record_id = self.record_provenance( operation_type=f"reverse_{inverse_operation}", model_params=reversed_params, parent_record_id=record_id ) reversal_result = { 'success': True, 'original_record_id': record_id, 'reversal_record_id': reversal_record_id, 'reversed_operation': inverse_operation, 'restored_parameters': reversed_params, 'reversal_time': time.time() } logger.info(f"Reversed operation {original_record.operation_type} -> {inverse_operation}") return reversal_result def _get_inverse_operation(self, operation_type: str) -> str: """Get inverse operation for reversibility.""" inverse_map = { 'train': 'untrain', 'fine_tune': 'restore_base', 'merge': 'split', 'quantize': 'dequantize', 'prune': 'restore_weights', 'distill': 'expand' } return inverse_map.get(operation_type, f"reverse_{operation_type}") def export_provenance_graph(self, filepath: str): """Export complete provenance graph to JSON file.""" export_data = { 'provenance_records': { record_id: { 'record_id': record.record_id, 'parent_id': record.parent_id, 'model_hash': record.model_hash, 'quantum_fingerprint': record.quantum_fingerprint, 'visual_identity_hash': record.visual_identity_hash, 'operation_type': record.operation_type, 'parameters': record.parameters, 'timestamp': record.timestamp, 'entanglement_links': record.entanglement_links, 'reversibility_key': record.reversibility_key } for record_id, record in self.provenance_graph.items() }, 'quantum_fingerprints': self.quantum_fingerprints, 'visual_identities': { key: { 'variants': value['variants'], 'creation_time': value['creation_time'] } for key, value in self.visual_identities.items() }, 'entanglement_registry': self.entanglement_registry, 'export_metadata': { 'total_records': len(self.provenance_graph), 'export_time': time.time(), 'quantum_precision': self.hash_precision } } with open(filepath, 'w') as f: json.dump(export_data, f, indent=2) logger.info(f"Exported provenance graph to {filepath}: {len(self.provenance_graph)} records") def get_quantum_provenance_metrics(self) -> Dict[str, Any]: """Get comprehensive metrics for quantum provenance tracking.""" metrics = { 'total_records': len(self.provenance_graph), 'quantum_fingerprints': len(self.quantum_fingerprints), 'visual_identities': len(self.visual_identities), 'entanglement_links': len(self.entanglement_registry), 'reversibility_keys': len(self.reversibility_cache), 'max_qubits': self.max_qubits, 'hash_precision': self.hash_precision } if self.provenance_graph: # Analyze provenance structure operations = [record.operation_type for record in self.provenance_graph.values()] operation_counts = {op: operations.count(op) for op in set(operations)} # Calculate graph depth depths = [] for record_id in self.provenance_graph: lineage = self.trace_lineage(record_id, max_depth=50) depths.append(lineage['total_depth']) metrics.update({ 'operation_distribution': operation_counts, 'average_lineage_depth': np.mean(depths) if depths else 0, 'max_lineage_depth': max(depths) if depths else 0, 'branching_factor': len(self.entanglement_registry) / len(self.provenance_graph) }) return metrics