feat: Implement quantum mesh optimization for packet routing

- Add QuantumPacketOptimizer class with simulated quantum annealing
- Integrate quantum optimizer into MeshRouter for route finding
- Add multi-objective optimization for latency, bandwidth, and reliability
- Include fallback mechanisms for classical routing
- Update requirements.txt with scipy dependency
- Add comprehensive tests and performance benchmarks

This implementation provides quantum-inspired optimization for mesh network routing, offering potentially better performance than traditional algorithms through advanced optimization techniques.

Author: sorydima

ai/ai_quantum_core/__init__.py

@@ -0,0 +1,26 @@
+"""
+AI Quantum Core Module for SynapseSDK
+
+This module provides quantum-inspired AI capabilities for the REChain network,
+including graph neural networks for device relationship modeling and
+federated learning across mesh networks.
+"""
+
+__version__ = "1.0.0"
+__author__ = "REChain AI Solutions"
+
+from .device_graph import DeviceGraph
+from .quantum_optimizer import QuantumPacketOptimizer, RoutingConstraints, RoutingPath
+
+# Optional imports - only load if dependencies are available
+try:
+    from .graph_trainer import QuantumGraphTrainer
+    _HAS_TORCH = True
+except ImportError:
+    _HAS_TORCH = False
+    QuantumGraphTrainer = None
+
+__all__ = ['DeviceGraph', 'QuantumPacketOptimizer', 'RoutingConstraints', 'RoutingPath']
+
+if _HAS_TORCH:
+    __all__.append('QuantumGraphTrainer')