dsf-quantum-oracle-sdk 2.0.1

SDK oficial en Python para la API de DSF Quantum Oracle (Grover's Search y Marcado de Estados)

DSF Quantum Oracle API

Hybrid Quantum-Classical Search & Filtering

Accelerate search and filtering in large datasets using quantum-inspired algorithms. Validated on real quantum hardware with classical fallback.

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🚀 Why Oracle?

Classical search through large datasets requires linear examination. Oracle uses hybrid quantum-classical techniques to accelerate discovery of high-priority items in complex data spaces.

Key Benefits:

• Accelerated search in large datasets
• Multi-criteria filtering
• Real-time anomaly detection
• API-first integration

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📊 Use Cases

Legal Technology

• Document retrieval in large case databases
• Precedent search across millions of cases
• Contract clause discovery
• Due diligence acceleration

E-commerce & Marketing

• Customer segmentation at scale
• High-value customer identification
• Product recommendation filtering
• Campaign audience optimization

Financial Services

• Portfolio screening
• Risk factor identification
• Fraud pattern detection
• Asset discovery

Data Analytics

• Anomaly detection in time-series
• Feature selection from high-dimensional data
• Pattern matching at scale
• Outlier identification

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💼 Pricing Tiers

Tier	Searches/Hour	Support	Price
Community	10	Email	Contact
Professional	50	Email	Contact
Enterprise	Custom	SLA + Dedicated	Contact

Enterprise tier includes custom volume limits and execution strategies.

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🔧 Quick Start

from dsf_quantum_orc_sdk import QuantumOracle

oracle = QuantumOracle(
    api_key="your_api_key",
    license_key="your_license_key",
    tier="professional"
)


result = oracle.search(
    values=[0.82, 0.61, 0.74, 0.55, 0.92],  
    parameters=[0.5, 0.3, 0.2, 0.4, 0.6],
    threshold=0.75
)

print(f"Found Items: {result['found_indices']}")
print(f"Success Rate: {result['success_probability']}")

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⚙️ Input Requirements

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

Dimensionality Limits:

• Community: Up to 100 items
• Professional: Up to 1,000 items
• Enterprise: Up to 10,000 items (custom limits available)

Performance Characteristics:
Results subject to quantum noise when executing on real hardware. Classical fallback ensures consistent availability.

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🎯 Integration Patterns

Pattern 1: RAG Document Filtering

Pre-filter LLM document retrievals for faster, more relevant results

Pattern 2: Batch Analytics

Overnight processing of large datasets with quantum-enhanced filtering

Pattern 3: Real-Time Scoring

Stream processing with hybrid execution paths

Pattern 4: Feature Selection

Pre-processing for ML pipelines to reduce dimensionality

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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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📊 Return Values

{
    'found_indices': [0, 2, 4],           
    'success_probability': 0.87,          
    'execution_backend': 'quantum',       
    'item_scores': [0.85, 0.62, 0.91], 
    'quantum_noise_level': 0.12           
}

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📞 Get Started

Request Technical Documentation:
Technical Access Form - Full API specs under NDA

Schedule Demo:
Book 30-min demo with your data

Pilot Program:
60-day pilot for qualified organizations

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📚 Resources

• RAG Integration Guide (requires NDA)
• Performance Benchmarks
• Case Studies: LegalTech

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🏢 Enterprise Features

• Custom search strategies
• Configurable execution backends
• On-premise deployment
• White-label options
• Custom SLAs

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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