dsf-quantum-kernel 1.0.2

Lightweight SDK for DSF quantum computing with IBM Quantum support

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.

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🚀 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.

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📊 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

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💼 Pricing Tiers

Tier	Evaluations/Hour	Support	Price
Community	100	Email	Contact
Professional	1,000	Email	Contact
Enterprise	Custom	Dedicated	Custom

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🔧 Quick Start

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}")

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🎯 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

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🏢 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

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🔒 Security

License-based authentication Encrypted data transport (TLS) Tier enforcement with daily quotas NDA available for technical specifications Enterprise security review support

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📊 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

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📞 Get Started

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

Schedule Enterprise Demo:
30-minute consultation with your data
Contact: Enterprise Demo

Proof-of-Concept Program:
Enterprise PoC with integration support
Contact: PoC Program

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📚 Resources

• Integration Guide (requires NDA)
• Benchmark Studies
• Hardware Validation Data
• Enterprise Case Studies

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🔬 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.

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📋 Credits

Technology Architect: Jaime Alexander Jimenez

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