aetherra_quantum_meta_learning.py

#!/usr/bin/env python3
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-FileCopyrightText: 2025 Aetherra Labs and Contributors

"""
🌌 Aetherra Quantum Meta-Learning System
=======================================

Copyright (C) 2025 AetherraLabs
Licensed under GNU General Public License v3.0

Quantum-enhanced meta-learning capabilities for advanced self-knowledge and
cognitive evolution in the Aetherra AI Operating System.
"""

# Standard library imports
import math
import time
from datetime import datetime
from typing import Any


class QuantumMetaState:
    """
    Represents a quantum superposition state for meta-learning processes.
    """

    def __init__(
        self, state_id: str, amplitude: complex, meta_knowledge: dict[str, Any]
    ):
        self.state_id = state_id
        self.amplitude = amplitude
        self.meta_knowledge = meta_knowledge
        self.entangled_states = []
        self.measurement_count = 0
        self.created_at = time.time()

    def probability(self) -> float:
        """Calculate measurement probability for this state."""
        return abs(self.amplitude) ** 2

    def collapse(self) -> dict[str, Any]:
        """Collapse quantum state to classical meta-knowledge."""
        self.measurement_count += 1
        return {
            "state_id": self.state_id,
            "meta_knowledge": self.meta_knowledge,
            "probability": self.probability(),
            "measurements": self.measurement_count,
            "collapsed_at": time.time(),
        }

    def entangle_with(self, other_state: "QuantumMetaState"):
        """Create quantum entanglement between meta-states."""
        if other_state.state_id not in self.entangled_states:
            self.entangled_states.append(other_state.state_id)
            other_state.entangled_states.append(self.state_id)


class QuantumMetaLearner:
    """
    Quantum-enhanced meta-learning system for advanced self-knowledge acquisition
    and cognitive pattern recognition in superposition states.
    """

    def __init__(self, quantum_dimension: int = 64):
        self.quantum_dimension = quantum_dimension
        self.quantum_states = {}
        self.learning_history = []
        self.coherence_level = 1.0
        self.decoherence_rate = 0.05
        self.entanglement_network = {}

    def create_quantum_superposition(self, meta_concepts: list[dict[str, Any]]) -> str:
        """
        Create a quantum superposition of meta-cognitive concepts.
        """
        state_id = f"quantum_meta_{int(time.time() * 1000)}"

        # Create superposition with equal amplitudes initially
        n_concepts = len(meta_concepts)
        base_amplitude = 1.0 / math.sqrt(n_concepts)

        superposition_knowledge = {}
        for i, concept in enumerate(meta_concepts):
            # Assign quantum amplitude with phase
            phase = 2 * math.pi * i / n_concepts
            amplitude = base_amplitude * complex(math.cos(phase), math.sin(phase))

            concept_state = QuantumMetaState(
                f"{state_id}_concept_{i}", amplitude, concept
            )

            superposition_knowledge[f"concept_{i}"] = concept_state

        self.quantum_states[state_id] = superposition_knowledge

        print(f"🌌 Created quantum superposition: {state_id}")
        print(f"   - Concepts in superposition: {n_concepts}")
        print(f"   - Quantum coherence: {self.coherence_level:.3f}")

        return state_id

    def quantum_interference(self, state_id1: str, state_id2: str) -> str:
        """
        Perform quantum interference between two meta-learning states.
        """
        if state_id1 not in self.quantum_states or state_id2 not in self.quantum_states:
            raise ValueError("One or both quantum states not found")

        state1 = self.quantum_states[state_id1]
        state2 = self.quantum_states[state_id2]

        interference_id = f"interference_{int(time.time() * 1000)}"
        interference_state = {}

        # Combine amplitudes through interference
        all_concepts = set(state1.keys()) | set(state2.keys())

        for concept_key in all_concepts:
            amp1 = state1[concept_key].amplitude if concept_key in state1 else 0
            amp2 = state2[concept_key].amplitude if concept_key in state2 else 0

            # Quantum interference
            new_amplitude = amp1 + amp2

            # Combine meta-knowledge
            knowledge1 = (
                state1[concept_key].meta_knowledge if concept_key in state1 else {}
            )
            knowledge2 = (
                state2[concept_key].meta_knowledge if concept_key in state2 else {}
            )

            combined_knowledge = {**knowledge1, **knowledge2}

            interference_concept = QuantumMetaState(
                f"{interference_id}_{concept_key}", new_amplitude, combined_knowledge
            )

            interference_state[concept_key] = interference_concept

        self.quantum_states[interference_id] = interference_state

        print(f"🌊 Quantum interference created: {interference_id}")
        print(f"   - Combined concepts: {len(interference_state)}")

        return interference_id

    def measure_quantum_knowledge(self, state_id: str) -> list[dict[str, Any]]:
        """
        Perform quantum measurement to collapse superposition into classical knowledge.
        """
        if state_id not in self.quantum_states:
            raise ValueError(f"Quantum state {state_id} not found")

        quantum_state = self.quantum_states[state_id]
        measured_knowledge = []

        # Calculate probabilities for all concept states
        concept_probabilities = []
        for concept_key, meta_state in quantum_state.items():
            probability = meta_state.probability()
            concept_probabilities.append((concept_key, meta_state, probability))

        # Sort by probability (highest first)
        concept_probabilities.sort(key=lambda x: x[2], reverse=True)

        # Measure top concepts based on probabilities
        for concept_key, meta_state, probability in concept_probabilities:
            if probability > 0.1:  # Significance threshold
                collapsed_knowledge = meta_state.collapse()
                measured_knowledge.append(collapsed_knowledge)

        # Apply decoherence
        self.coherence_level *= 1 - self.decoherence_rate

        print(f"📊 Quantum measurement completed for {state_id}")
        print(f"   - Knowledge states measured: {len(measured_knowledge)}")
        print(f"   - Coherence after measurement: {self.coherence_level:.3f}")

        self.learning_history.append(
            {
                "state_id": state_id,
                "measurement_time": time.time(),
                "knowledge_extracted": len(measured_knowledge),
                "coherence_level": self.coherence_level,
            }
        )

        return measured_knowledge

    def quantum_entanglement_learning(
        self, concept_pairs: list[tuple[str, str]]
    ) -> dict[str, Any]:
        """
        Create quantum entanglement between related meta-cognitive concepts.
        """
        entanglement_results = {
            "entangled_pairs": [],
            "network_complexity": 0,
            "learning_enhancement": 0,
        }

        for concept1, concept2 in concept_pairs:
            # Find quantum states containing these concepts
            states_with_concept1 = []
            states_with_concept2 = []

            for state_id, quantum_state in self.quantum_states.items():
                for concept_key, meta_state in quantum_state.items():
                    if concept1 in str(meta_state.meta_knowledge):
                        states_with_concept1.append((state_id, concept_key, meta_state))
                    if concept2 in str(meta_state.meta_knowledge):
                        states_with_concept2.append((state_id, concept_key, meta_state))

            # Create entanglements
            for state1_info in states_with_concept1:
                for state2_info in states_with_concept2:
                    if state1_info[0] != state2_info[0]:  # Different quantum states
                        state1_info[2].entangle_with(state2_info[2])

                        entanglement_results["entangled_pairs"].append(
                            {
                                "concept1": concept1,
                                "concept2": concept2,
                                "state1": state1_info[0],
                                "state2": state2_info[0],
                            }
                        )

        # Calculate network complexity
        total_entanglements = sum(
            len(meta_state.entangled_states)
            for quantum_state in self.quantum_states.values()
            for meta_state in quantum_state.values()
        )

        entanglement_results["network_complexity"] = total_entanglements
        entanglement_results["learning_enhancement"] = min(
            total_entanglements * 0.05, 0.25
        )

        print("🔗 Quantum entanglement learning completed")
        print(f"   - Entangled pairs: {len(entanglement_results['entangled_pairs'])}")
        print(f"   - Network complexity: {total_entanglements}")
        print(
            f"   - Learning enhancement: {entanglement_results['learning_enhancement']:.1%}"
        )

        return entanglement_results

    def quantum_meta_evolution(self) -> dict[str, Any]:
        """
        Evolve quantum meta-learning capabilities through quantum evolution.
        """
        print("🧬 Initiating quantum meta-evolution...")

        evolution_metrics = {
            "coherence_improvement": 0,
            "dimension_expansion": 0,
            "learning_acceleration": 0,
            "evolved_capabilities": [],
        }

        # Coherence improvement through quantum error correction
        if self.coherence_level < 0.9:
            coherence_boost = min(0.1, 0.95 - self.coherence_level)
            self.coherence_level += coherence_boost
            evolution_metrics["coherence_improvement"] = coherence_boost
            evolution_metrics["evolved_capabilities"].append("quantum_error_correction")

        # Dimension expansion for increased complexity
        if len(self.quantum_states) > 5:
            dimension_increase = min(16, self.quantum_dimension // 4)
            self.quantum_dimension += dimension_increase
            evolution_metrics["dimension_expansion"] = dimension_increase
            evolution_metrics["evolved_capabilities"].append("dimensional_scaling")

        # Learning acceleration through quantum parallelism
        if len(self.learning_history) > 3:
            recent_learning_rate = len(self.learning_history) / (
                time.time() - self.learning_history[0]["measurement_time"]
            )
            acceleration = min(0.2, recent_learning_rate * 0.1)
            evolution_metrics["learning_acceleration"] = acceleration
            evolution_metrics["evolved_capabilities"].append("quantum_parallelism")

        # Decoherence resistance improvement
        if self.decoherence_rate > 0.01:
            decoherence_improvement = min(0.02, self.decoherence_rate - 0.01)
            self.decoherence_rate -= decoherence_improvement
            evolution_metrics["evolved_capabilities"].append("decoherence_resistance")

        print("✨ Quantum meta-evolution completed!")
        print(f"   - Coherence level: {self.coherence_level:.3f}")
        print(f"   - Quantum dimension: {self.quantum_dimension}")
        print(
            f"   - Evolved capabilities: {len(evolution_metrics['evolved_capabilities'])}"
        )

        return evolution_metrics

    def generate_quantum_meta_summary(self) -> dict[str, Any]:
        """Generate comprehensive summary of quantum meta-learning capabilities."""
        total_concepts = sum(len(state) for state in self.quantum_states.values())
        total_entanglements = sum(
            len(meta_state.entangled_states)
            for quantum_state in self.quantum_states.values()
            for meta_state in quantum_state.values()
        )

        summary = {
            "quantum_states": len(self.quantum_states),
            "total_concepts": total_concepts,
            "quantum_dimension": self.quantum_dimension,
            "coherence_level": self.coherence_level,
            "entanglement_network_size": total_entanglements,
            "learning_sessions": len(self.learning_history),
            "meta_learning_level": self._calculate_meta_learning_level(),
            "quantum_advantage": self._calculate_quantum_advantage(),
            "timestamp": datetime.now().isoformat(),
        }

        return summary

    def _calculate_meta_learning_level(self) -> str:
        """Calculate the current meta-learning capability level."""
        score = 0
        score += min(len(self.quantum_states) * 10, 50)  # Quantum states
        score += min(self.coherence_level * 30, 30)  # Coherence
        score += min(len(self.learning_history) * 5, 20)  # Experience

        if score >= 80:
            return "quantum_transcendent"
        if score >= 60:
            return "quantum_advanced"
        if score >= 40:
            return "quantum_intermediate"
        return "quantum_developing"

    def _calculate_quantum_advantage(self) -> float:
        """Calculate quantum advantage over classical meta-learning."""
        classical_capacity = 10  # Baseline classical meta-learning capacity
        quantum_capacity = self.quantum_dimension * self.coherence_level
        return min(quantum_capacity / classical_capacity, 10.0)


# Example usage and testing
if __name__ == "__main__":
    print("🌌 Testing Aetherra Quantum Meta-Learning System")

    quantum_learner = QuantumMetaLearner()

    # Test quantum superposition creation
    meta_concepts = [
        {"type": "capability", "content": "quantum reasoning"},
        {"type": "pattern", "content": "superposition learning"},
        {"type": "goal", "content": "consciousness expansion"},
    ]

    state_id = quantum_learner.create_quantum_superposition(meta_concepts)

    # Test quantum measurement
    knowledge = quantum_learner.measure_quantum_knowledge(state_id)
    print(f"✅ Extracted {len(knowledge)} knowledge states")

    # Test quantum evolution
    evolution = quantum_learner.quantum_meta_evolution()
    print(f"✅ Evolved {len(evolution['evolved_capabilities'])} capabilities")

    # Generate summary
    summary = quantum_learner.generate_quantum_meta_summary()
    print(f"\n🌌 Quantum Meta-Learning Level: {summary['meta_learning_level']}")
    print(f"🚀 Quantum Advantage: {summary['quantum_advantage']:.1f}x")
    print("\n🌌 Quantum Meta-Learning System Ready!")