sigma-c-framework 1.2.3

Critical Susceptibility Framework for Quantum, GPU, Financial, Climate, Seismic, and Magnetic analysis

Sigma-C Framework v1.2.3 "Universal Optimization"

The Universal Optimization Framework for Quantum, GPU, Financial, and ML Systems.

🚀 Overview

Sigma-C is a unified framework for optimizing complex systems by balancing Performance (Efficiency/Returns/Accuracy) against Stability (Resilience/Sigma_c).

It provides a consistent API to optimize:

• Quantum Circuits: Maximize fidelity while minimizing noise susceptibility
• GPU Kernels: Maximize throughput while maintaining thermal/memory stability
• Financial Strategies: Maximize returns while minimizing crash risk (sigma_c)
• ML Models: Maximize accuracy while ensuring adversarial robustness

✨ New in v1.2.3

• Machine Learning Optimizer: Optimize neural networks for robustness (BalancedMLOptimizer)
• Hardware-Aware Quantum: Native gate optimization for Rigetti, IQM, and IBM
• Enhanced Physics: Holevo bound, Roofline model, and No-Cloning theorem validation
• Extended Documentation: Comprehensive guides for hardware and domain extensions

📦 Installation

pip install sigma-c-framework

Or from source:

git clone https://github.com/forgottenforge/sigma-c-framework.git
cd sigma-c-framework
pip install -e .

🔧 Quick Start

1. Quantum Optimization

from sigma_c.adapters.quantum import QuantumAdapter
from sigma_c.optimization.quantum import BalancedQuantumOptimizer

# Initialize with hardware-aware compilation
adapter = QuantumAdapter(config={'device': 'rigetti', 'auto_compile': True})
optimizer = BalancedQuantumOptimizer(adapter)

# Optimize Grover's Algorithm
result = optimizer.optimize_circuit(
    circuit_factory=my_grover_circuit,
    param_space={'epsilon': [0.0, 0.01], 'idle_frac': [0.0, 0.1]}
)
print(f"Optimal Params: {result.optimal_params}")

2. ML Optimization (New!)

from sigma_c.optimization.ml import BalancedMLOptimizer

optimizer = BalancedMLOptimizer(performance_weight=0.7, stability_weight=0.3)

# Optimize Neural Network Hyperparameters
result = optimizer.optimize_model(
    model_factory=create_model,
    param_space={
        'learning_rate': [0.001, 0.01],
        'dropout': [0.1, 0.2, 0.3]
    }
)
print(f"Robust Accuracy: {result.score}")

3. Financial Optimization

from sigma_c.adapters.financial import FinancialAdapter
from sigma_c.optimization.financial import BalancedFinancialOptimizer

adapter = FinancialAdapter()
optimizer = BalancedFinancialOptimizer(adapter)

# Optimize Trading Strategy
result = optimizer.optimize_strategy(
    param_space={'lookback': [60, 126, 252], 'threshold': [0.01, 0.02]}
)
print(f"Stable Returns: {result.performance_after}")

📚 Documentation

• Full Documentation
• Hardware Compatibility
• Extending Domains
• Release Notes

🛡️ License

Open Source: AGPL-3.0-or-later
Commercial: Contact [e-mail] for commercial licensing options.

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