sigma-c-framework 2.0.2

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

Sigma-C Framework v2.0.2 "Rigorous Control"

Universal Criticality Analysis & Active Control System

🚀 Overview

Sigma-C v2.0.2 is a rigorous active control system that detects, analyzes, and maintains critical points across quantum, GPU, financial, climate, and ML systems.

New in v2.0.2: Rigor Refinement - Enhanced numerical stability, AI safety constraints, and statistical significance testing.

✨ What's New in v2.0.2

Core Features

• Observable Discovery: Automatic identification of optimal order parameters
• Multi-Scale Analysis: Wavelet-based criticality detection across scales
• Active Control: PID controller for critical point maintenance
• Streaming Calculation: O(1) real-time susceptibility updates (Welford's Algorithm)

New Domains

• Climate: Mesoscale boundary detection
• Seismic: Gutenberg-Richter analysis with Significance Testing
• Magnetic: Critical exponents validation
• Edge Computing: Power efficiency optimization
• LLM Cost: Model selection via Pareto frontier with Safety Constraints

🔌 Universal Connectivity

• Quantum: Qiskit, PennyLane, Cirq, AWS Braket
• ML: PyTorch, JAX, TensorFlow
• Finance: QuantLib, Zipline
• DevOps: Kubernetes, GitHub Actions, Grafana
• Web: REST API, GraphQL, WebAssembly

📦 Installation

# Core framework
pip install sigma-c-framework

# With all integrations
pip install sigma-c-framework[all]

# Specific integrations
pip install sigma-c-framework[quantum]   # Qiskit, PennyLane
pip install sigma-c-framework[ml]        # PyTorch, JAX
pip install sigma-c-framework[devops]    # K8s, Grafana

🔧 Quick Start

Quantum (Qiskit)

from qiskit import QuantumCircuit
from sigma_c.connectors.qiskit import QiskitSigmaC

circuit = QuantumCircuit(3)
circuit.h(0)
circuit.cx(0, 1)

# Automatic criticality analysis
result = QiskitSigmaC.analyze(circuit)
print(f"σ_c = {result['sigma_c']:.4f}")

Machine Learning (PyTorch)

from sigma_c.ml.pytorch import CriticalModule, SigmaCLoss

class MyNet(CriticalModule):
    def forward(self, x):
        return self.critical_forward(x)  # Auto σ_c tracking

criterion = SigmaCLoss(lambda_critical=0.1)

Universal Bridge (Any Framework)

from sigma_c.connectors.bridge import SigmaCBridge

@SigmaCBridge.wrap_any_function
def my_function(x):
    return x ** 2

result = my_function(5)
print(result.__sigma_c__)  # Criticality metadata

DevOps (Kubernetes)

apiVersion: sigma-c.io/v1
kind: CriticalityMonitor
metadata:
  name: app-monitor
spec:
  target:
    app: my-app
  thresholds:
    cpu: 0.8
  actions:
    scale: true

📚 Documentation

• Integrations Guide - All 22+ integrations
• API Reference - Complete API docs
• Release Notes - What's new in v2.0.2
• Examples - Code examples

🎯 Use Cases

• Quantum Computing: Optimize circuits for NISQ devices
• GPU/HPC: Detect cache transitions, thermal throttling
• Finance: Predict market crashes, optimize portfolios
• ML: Train robust models, detect overfitting
• Climate: Identify mesoscale boundaries
• Edge/IoT: Optimize power efficiency

🛡️ License

Open Source: AGPL-3.0-or-later
Commercial: Contact [e-mail]

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