dsf-quantum-gps-sdk 2.0.1

SDK oficial en Python para la API de DSF Quantum GPS (Optimization y Amplitude Estimation)

DSF Quantum GPS API

Hybrid Quantum-Classical Parameter Optimization

Optimize decision parameters using hybrid quantum-classical algorithms validated on real IBM Quantum hardware. No quantum expertise required.

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🚀 Why GPS?

Traditional parameter optimization relies on grid search or gradient descent that can get trapped in local minima. GPS leverages quantum-enhanced exploration to find optimal parameters more efficiently while your proprietary logic remains protected server-side.

Key Benefits:

• Quantum-enhanced parameter space exploration
• Reduced computational overhead
• API-first integration with existing workflows
• No quantum programming knowledge needed

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📊 Use Cases

Financial Services

• Portfolio weight optimization
• Risk model calibration
• Capital allocation strategies
• Trading algorithm tuning

Machine Learning & AI

• Feature importance optimization
• Ensemble model weighting
• Hyperparameter tuning
• Neural network architecture search

Business Analytics

• KPI weight optimization
• Multi-criteria decision analysis
• Balanced scorecard calibration
• Resource allocation optimization

Engineering & Operations

• Control system parameter tuning
• Process optimization
• Energy grid balancing
• Supply chain optimization

E-commerce & Marketing

• Recommendation system tuning
• Product ranking optimization
• Customer segmentation weights
• Campaign allocation

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💼 Pricing Tiers

Tier	Optimization Capacity	Support	Price
Community	Development & Testing	Email	Contact
Professional	Production Workloads	Email + SLA	Contact
Enterprise	Custom Volume	Dedicated Team	Contact

Capacity scales dynamically based on problem complexity and tier.

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🔧 Quick Start

from dsf_quantum_gps_sdk import QuantumGPS

gps = QuantumGPS(
    api_key="your_api_key",
    license_key="your_license_key",
    tier="professional"
)


result = gps.optimize(
    values=[0.82, 0.61, 0.74, 0.55],    
    priors=[1.0, 1.5, 2.0, 1.2],      
    config={'max_iterations': 100}
)

print(f"Optimized Parameters: {result['optimized_parameters']}")
print(f"Objective Score: {result['objective_value']}")
print(f"Convergence: {result['converged']}")

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⚙️ Input Requirements

Normalization Required:
All input values must be normalized to [0-1] range

Dimensionality Limits:

• Community: Up to 20 parameters
• Professional: Up to 100 parameters
• Enterprise: Up to 500 parameters (custom limits available)

Performance Characteristics:
Hybrid execution with quantum-enhanced exploration and classical fallback for reliability

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📊 Return Values

{
    'optimized_parameters': [0.23, 0.35, 0.42, ...],  
    'objective_value': 0.8542,                       
    'converged': True,                                
    'iterations': 47,                               
    'execution_time': 12.3,                        
    'execution_backend': 'quantum',                   
    'quantum_noise_level': 0.08                      
}

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🎯 Optimization Scenarios

Financial Portfolio Optimization

Multi-asset allocation with risk-return tradeoffs

ML Model Ensemble Weighting

Optimal combination of multiple prediction models

Multi-Criteria Decision Making

Balance competing objectives in complex decisions

Control System Tuning

PID controllers and feedback system optimization

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🔒 Security

• Transport: TLS 1.3 encryption
• Storage: AES-256 encryption at rest
• Authentication: Token-scoped API keys
• Compliance: SOC2-ready architecture (compliance program in progress)
• Data Residency: Configurable regional deployment
• Technical Docs: Available under NDA

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📞 Get Started

Request Technical Documentation:
Full API specifications under NDA
Contact: Technical Docs

Schedule Enterprise Demo:
30-minute consultation with your optimization problem
Contact: Demo

Pilot Program:
60-day pilot for qualified organizations

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📚 Resources

• Case Studies
• Integration Guide (requires NDA)
• Benchmark Studies
• Optimization Best Practices

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🏢 Enterprise Features

• Custom optimization strategies
• Configurable execution backends
• On-premise deployment options
• Custom integration assistance
• Priority feature requests
• White-label options

Production integration available upon completion of client validation and model governance workflows.

Contact: [e-mail]

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📋 Credits

Technology Architect: Jaime Alexander Jimenez

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