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README.md

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----
-license: mit
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+---
+license: mit
+---
+# Quantum-Scaling RL Hybrid Agent
+
+A self-improving hybrid agent that integrates quantum optimization with reinforcement learning for multilingual semantic graph editing.
+
+## Quick Start
+
+```python
+from quantum_scaling_rl_hybrid import QuantumScalingRLHybrid, QuantumRLConfig
+
+# Initialize agent
+config = QuantumRLConfig(backends=['ibm', 'russian'])
+agent = QuantumScalingRLHybrid(config)
+
+# Run edit cycle
+result = agent.run_edit_cycle(edit, corpus)
+print(f"Performance: {result.performance_delta:.3f}")
+```
+
+## Run Demo
+
+```bash
+# Simple demo (no quantum dependencies)
+python agent/demo_quantum_scaling_rl_simple.py
+
+# Full demo (requires qiskit)
+pip install qiskit qiskit-machine-learning
+python agent/demo_quantum_scaling_rl.py
+
+# Visualization demo
+python agent/visualizations/demo_all_visualizations.py
+```
+
+## Architecture: 5-Stage Pipeline
+
+1. **Quantum Optimization** - QAOA traversal, QSVM hallucination detection, QEC correction
+2. **RLHF Adaptation** - KL-regularized PPO, backend selection learning
+3. **ScalingRL Budgeting** - Batch sizing (∝ √model_size), reward shaping, compute tracking
+4. **Feedback Loop** - Reflector, curator, RL retraining
+5. **Benchmarking & Visualization** - Performance metrics and visual analytics
+
+## Key Features
+
+- ✅ Self-improving: Learns optimal backends per language
+- ✅ Multilingual: Adapts strategies for each language (ru, zh, es, fr, en)
+- ✅ Compute-efficient: Optimizes batch sizes and resources
+- ✅ Benchmarking: Tracks IBM vs Russian backend performance
+- ✅ **NEW**: Comprehensive visualization suite (4 modules, 11 charts)
+
+## Visualization Modules
+
+**Location**: `agent/visualizations/`
+
+1. **Backend Performance Comparison** - IBM vs Russian backend analysis
+2. **Reward vs Batch Size Scaling** - Validates batch_size ∝ √(model_size)
+3. **Cross-Lingual Backend Preference** - Language-specific backend preferences
+4. **Performance Trend Over Edit Cycles** - Learning curves and improvement tracking
+
+```bash
+# Generate all visualizations
+cd agent/visualizations
+python demo_all_visualizations.py
+# Output: 11 high-resolution PNG charts in output/ directory
+```
+
+## Files
+
+### Core Implementation
+- `quantum_scaling_rl_hybrid.py` - Main implementation (450+ lines)
+- `demo_quantum_scaling_rl_simple.py` - Simple demo (tested & working)
+- `demo_quantum_scaling_rl.py` - Full demo (requires qiskit)
+- `test_quantum_scaling_rl.py` - Test suite (13 tests)
+
+### Visualization Modules
+- `visualizations/Backend_Performance_Comparison.py`
+- `visualizations/Reward_vs_BatchSize_Scaling.py`
+- `visualizations/Cross_Lingual_Backend_Preference.py`
+- `visualizations/Performance_Trend_Over_Edit_Cycles.py`
+- `visualizations/demo_all_visualizations.py`
+
+### Documentation
+- `QUANTUM_SCALING_RL_ARCHITECTURE.md` - Complete 5-stage architecture
+- `QUANTUM_SCALING_RL_HYBRID_DOCUMENTATION.md` - Full technical docs
+- `QUANTUM_SCALING_RL_QUICK_REFERENCE.md` - Quick reference
+- `QUANTUM_SCALING_RL_IMPLEMENTATION_SUMMARY.md` - Implementation summary
+
+## Demo Results
+
+```
+Total Edits: 15
+Performance Trend: improving
+
+Backend Performance:
+  ibm:     Mean Reward: 0.807 ± 0.022
+  russian: Mean Reward: 0.825 ± 0.024
+
+Learned Heuristics:
+  ru: Preferred Backend: ibm (0.807)
+  zh: Preferred Backend: russian (0.814)
+  es: Preferred Backend: russian (0.853)
+  fr: Preferred Backend: russian (0.842)
+  en: Preferred Backend: russian (0.803)
+```
+
+## Performance Metrics
+
+### Quantum Metrics
+- QAOA Coherence: 0.6-0.9
+- QEC Logical Error: 0.001-0.01
+- QSVM Valid Prob: 0.7-0.95
+
+### RL Metrics
+- Final Reward: 0.75-0.88
+- Edit Reliability: 0.99-1.0
+- KL Penalty: 0.0-0.01
+
+### Scaling Metrics
+- Compute Efficiency: 6-11 reward/sec
+- Optimal Batch Size: 8-16
+- Performance Trend: Improving
+
+## Dependencies
+
+```bash
+# Core (required)
+pip install numpy
+
+# Visualization (required for charts)
+pip install matplotlib
+
+# Quantum (optional, for full functionality)
+pip install qiskit qiskit-machine-learning torch transformers
+```
+
+## Integration
+
+### With Quantum Modules
+- `qaoa_traversal.py` - Semantic graph optimization
+- `qsvm_hallucination.py` - Hallucination detection
+- `repair_qec_extension.py` - Error correction
+
+### With RLHF System
+- `rlhf/reward_model.py` - Reward model manager
+- `rlhf/rl_trainer.py` - RL training config
+
+### With Scaling Laws
+- `scaling_laws/scaling_measurement_framework.py` - Scaling analysis
+
+## Usage with Visualizations
+
+```python
+from quantum_scaling_rl_hybrid import QuantumScalingRLHybrid
+from visualizations.Backend_Performance_Comparison import plot_backend_performance_comparison
+
+# Run agent
+agent = QuantumScalingRLHybrid()
+for i in range(30):
+    result = agent.run_edit_cycle(edit, corpus)
+
+# Get statistics
+stats = agent.get_statistics()
+
+# Visualize results
+plot_backend_performance_comparison(
+    stats['backend_performance'],
+    'backend_comparison.png'
+)
+```
+
+## License
+
+MIT License