Metadata-Version: 2.4
Name: dsf-quantum-gps-sdk
Version: 2.0.1
Summary: SDK oficial en Python para la API de DSF Quantum GPS (Optimization y Amplitude Estimation)
Home-page: https://github.com/jaimeajl/dsf-quantum-gps-sdk
Author: Jaime Alexander Jimenez
Author-email: Jaime Alexander JImenez Lozano <contacto@dsfuptech.cloud>
License: Proprietary
Project-URL: Homepage, https://github.com/jaimeajl/dsf-quantum-gps-sdk
Project-URL: Bug Tracker, https://github.com/jaimeajl/dsf-quantum-gps-sdk/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: requests
Requires-Dist: qiskit>=1.0.0
Requires-Dist: qiskit-aer
Requires-Dist: qiskit-algorithms
Requires-Dist: numpy
Requires-Dist: scipy
Dynamic: author
Dynamic: home-page
Dynamic: requires-python

# 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.

---

## 🚀 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

---

## 📊 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

---

## 💼 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.*

---

## 🔧 Quick Start

```python
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']}")
```

---

## ⚙️ 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

---

## 📊 Return Values

```python
{
    '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                      
}
```

---

## 🎯 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

---

## 🔒 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

---

## 📞 Get Started

**Request Technical Documentation:**  
Full API specifications under NDA  
[Contact: Technical Docs](mailto:contacto@dsfuptech.cloud?subject=GPS%20Technical%20Docs)

**Schedule Enterprise Demo:**  
30-minute consultation with your optimization problem  
[Contact: Demo](mailto:contacto@dsfuptech.cloud?subject=GPS%20Demo)

**Pilot Program:**  
60-day pilot for qualified organizations

---

## 📚 Resources

- [Case Studies](mailto:contacto@dsfuptech.cloud?subject=Case%20Studies)
- [Integration Guide](mailto:contacto@dsfuptech.cloud?subject=Integration%20Guide) (requires NDA)
- [Benchmark Studies](mailto:contacto@dsfuptech.cloud?subject=Benchmarks)
- [Optimization Best Practices](mailto:contacto@dsfuptech.cloud?subject=Best%20Practices)

---

## 🏢 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: contacto@dsfuptech.cloud

---

## 📋 Credits

**Technology Architect:** Jaime Alexander Jimenez

---

© 2025 DSF UpTech. All rights reserved.
