dsf-quantum-kernel 1.0.2
Lightweight SDK for DSF quantum computing with IBM Quantum support
Requires Python: >=3.8
Keywords: quantum, quantum computing, quantum kernel, ibm quantum, qiskit, adaptive scoring, hierarchical evaluation, quantum amplitude estimation, qae, dsf, decision support, machine learning

# DSF Quantum Kernel

**Quantum-Enhanced Computing Engine for Enterprise Decision Workflows**

Hybrid classical-quantum platform with generalized primitives validated on IBM Quantum hardware. Accelerates optimization, search, and classification tasks with quantum-enhanced evaluation.

---

## 🚀 Why Quantum Kernel Computing?

Classical algorithms face scalability limits in complex decision spaces. DSF Quantum Kernel provides syscall-level primitives:

-GPS (Optimize): Quantum-enhanced multi-factor optimization
-Oracle (Search): Amplitude amplification for pattern detection
-QKM (Classify): Quantum kernel methods for classification
-Validated on real IBM Quantum hardware
-Hybrid quantum-classical workflows

Designed for: Enterprise innovation teams, advanced analytics, quantum-ready infrastructure.

**Designed for:** Enterprise innovation teams, advanced analytics, and decision intelligence pipelines.

---

## 📊 Use Cases

### Financial Services
Credit risk optimization
Portfolio rebalancing
Fraud pattern detection
Multi-factor decision workflows

### Insurance
Claims prioritization
Risk stratification
Policy optimization
Underwriting workflows

### Enterprise Risk
Resource allocation
Vendor selection optimization
Supply chain optimization
Compliance-ready workflows

### Healthcare & Life Sciences
Patient prioritization
Treatment pathway optimization
Resource allocation
Clinical decision support

---

## 💼 Pricing Tiers

|      Tier        | Evaluations/Hour | Support   |   Price |
|------------------|------------------|-----------|---------|
| **Community**    |        100       | Email     | Contact |
| **Professional** |      1,000       | Email     | Contact |
| **Enterprise**   |     Custom       | Dedicated | Custom  |

---

## 🔧 Quick Start

```python
from dsf_quantum_kernel import QuantumSDK, create_block, create_config

# Initialize SDK with your license
sdk = QuantumSDK(license_key="PRO-YYYY-MM-DD-QUANTUM-XXXX")

# Configure hierarchical blocks
blocks = [
    create_block("credit_risk", [0.85, 0.62], [1.0, 0.8]),
    create_block("payment_history", [0.71, 0.91], [1.2, 1.0])
]

config = create_config(blocks, global_adjustment=0.1)

# Submit job to quantum backend (async)
job_id = sdk.submit_async(
    data={
        "credit_risk": [0.85, 0.62],
        "payment_history": [0.71, 0.91]
    },
    config=config,
    backend="simulator"   # or "ibm_quantum"
)

# Wait for results
result = sdk.wait_for_result(job_id)

print(f"Quantum Score: {result.score}")
print(f"Block Scores: {result.blocks}")

```

---

## 🎯 Platform Capabilities

**Generalized Quantum Primitives:**  
GPS (optimize), Oracle (search), QKM (classify)

**Real Hardware Validation:**  
Executes on IBM Quantum processors (ibm_brisbane, ibm_torino)

**Hierarchical Architecture:**  
Multi-block configurations with weighted aggregation

**Enterprise Integration:**  
REST API designed for pipeline integration

**Hybrid Workflows:**  
Simulator for speed, quantum hardware for validation

**Production-Ready:**  
Priority queuing, quota management, tier enforcement

---

## 🏢 Enterprise Features

**Multi-Backend Support:**  
Qiskit Aer simulator + IBM Quantum hardware

**Flexible Deployment:**  
Hardware backends configurable per use case

**Scalable Architecture:**  
Priority-based job scheduling by tier

**PoC-Ready:**  
Proof-of-concept with your data

**Enterprise Support:**  
Dedicated integration assistance available

---

## 🔒 Security

License-based authentication 
Encrypted data transport (TLS)
Tier enforcement with daily quotas
NDA available for technical specifications
Enterprise security review support

---

## 📊 Performance Characteristics

**Validated on Real Quantum Hardware:**  
Tested on IBM Quantum production systems

**Hierarchical Evaluation:**  
Block-level + global aggregation

**Configurable Backends:**  
Simulator (seconds) vs Hardware (minutes)

**Noise-Aware Processing:**  
Entropy adjustment for signal quality


---

## 📞 Get Started

**Request Technical Documentation:**  
Full API specifications available under NDA  
[Contact: Technical Docs](mailto:contacto@dsfuptech.cloud?subject=Scoring%20Technical%20Docs)

**Schedule Enterprise Demo:**  
30-minute consultation with your data  
[Contact: Enterprise Demo](mailto:contacto@dsfuptech.cloud?subject=Enterprise%20Demo)

**Proof-of-Concept Program:**  
Enterprise PoC with integration support  
[Contact: PoC Program](mailto:contacto@dsfuptech.cloud?subject=PoC%20Program)

---

## 📚 Resources

- [Integration Guide](mailto:contacto@dsfuptech.cloud?subject=Integration%20Guide) (requires NDA)
- [Benchmark Studies](mailto:contacto@dsfuptech.cloud?subject=Benchmarks)
- [Hardware Validation Data](mailto:contacto@dsfuptech.cloud?subject=Validation)
- [Enterprise Case Studies](mailto:contacto@dsfuptech.cloud?subject=Case%20Studies)

---

## 🔬 Built on Validated Research

Quantum kernel validated on IBM Quantum hardware with documented experimental results. Suitable for:
- Enterprise quantum initiatives
- Advanced analytics workflows
- Decision intelligence enhancement
- Quantum-ready infrastructure development

Production integration available upon completion of client validation and model governance workflows.

---

## 📋 Credits

**Technology Architect:** Jaime Alexander Jimenez

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