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AIResAgTeam/
Quantum-ScalingRL

Name: Quantum-ScalingRL
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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

Quantum Optimization - QAOA traversal, QSVM hallucination detection, QEC correction
RLHF Adaptation - KL-regularized PPO, backend selection learning
ScalingRL Budgeting - Batch sizing (∝ √model_size), reward shaping, compute tracking
Feedback Loop - Reflector, curator, RL retraining
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/

Backend Performance Comparison - IBM vs Russian backend analysis
Reward vs Batch Size Scaling - Validates batch_size ∝ √(model_size)
Cross-Lingual Backend Preference - Language-specific backend preferences
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

AIResAgTeam/Quantum-ScalingRL

Quantum-Scaling RL Hybrid Agent

Quick Start

Run Demo

Architecture: 5-Stage Pipeline

Key Features

Visualization Modules

Files

Core Implementation

Visualization Modules

Documentation

Demo Results

Performance Metrics

Quantum Metrics

RL Metrics

Scaling Metrics

Dependencies

Integration

With Quantum Modules

With RLHF System

With Scaling Laws

Usage with Visualizations

License

AIResAgTeam/
Quantum-ScalingRL