Skip to main content

Lightweight SDK for DSF Quantum Adaptive Scoring with IBM Quantum support

Project description

DSF Quantum API

Quantum-Enabled Scoring Engine for Enterprise Decision Workflows

Hybrid classical-quantum scoring platform validated on real IBM Quantum hardware. Accelerates multi-factor decision evaluation with quantum-enhanced evaluation.


🚀 Why Quantum-Enabled Scoring?

Classical scoring functions can miss non-linear patterns in complex decision spaces. DSF Quantum Scoring leverages hybrid quantum-classical techniques to:

  • Quantum-enhanced evaluation under realistic noise conditions
  • Compare quantum-enhanced vs classical evaluation pathways
  • Accelerate multi-factor decision workflows
  • Enable quantum-ready scoring infrastructure

Designed for: Enterprise innovation teams, advanced analytics, and decision intelligence pipelines.


📊 Use Cases

Financial Services

  • Credit evaluation workflows
  • Risk assessment enhancement
  • Portfolio quality analysis
  • Multi-factor decision acceleration

Insurance

  • Policy evaluation
  • Claims assessment workflows
  • Risk stratification
  • Underwriting support

Enterprise Risk

  • Vendor scoring
  • Third-party evaluation
  • Decision workflow optimization
  • Compliance-ready assessment

Healthcare & Life Sciences

  • Patient risk stratification
  • Care prioritization workflows
  • Resource allocation optimization
  • Clinical decision support

💼 Pricing Tiers

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

🔧 Quick Start

from dsf_quantum_scoring import QuantumScoring

scorer = QuantumScoring(
    api_key="your_api_key",
    license_key="your_license_key",
    tier="professional"
)


result = scorer.evaluate(
    factors=[0.85, 0.62, 0.71, 0.91],     
    weights=[1.2, 0.8, 1.5, 1.0],          
    impact_factors=[1.0, 1.3, 0.9, 1.1]    
)

print(f"Quantum-Enhanced Score: {result['quantum_score']}")
print(f"Classical Baseline: {result['classical_score']}")
print(f"Signal Quality: {result['degradation']}")

🎯 Platform Capabilities

Hybrid Quantum-Classical Architecture:
Validated scoring function with configurable backends

Real Hardware Validation:
Executes on IBM Quantum processors (ibm_brisbane, ibm_torino)

Enterprise Integration:
REST API designed for pipeline integration

Quantum-Enhanced Evaluation:
Non-linear pattern detection under realistic noise conditions

Benchmark-Ready:
Parallel classical evaluation for comparative analysis


🏢 Enterprise Features

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

Flexible Deployment:
Hardware backends configurable per use case

Enterprise Support:
Dedicated integration assistance available

Hybrid Workflows:
Classical fallback with quantum acceleration options

PoC-Ready:
Enterprise proof-of-concept programs available


🔒 Security

  • API key authentication
  • Encrypted data transport
  • License enforcement per tier
  • NDA available for technical specifications
  • Enterprise security review support

📊 Performance Characteristics

Validated on Real Quantum Hardware:
Tested on IBM Quantum production systems

Hybrid Evaluation:
Quantum-enhanced scoring with classical comparison

Configurable Backends:
Support for multiple quantum processors

Signal Quality Metrics:
Noise-aware evaluation with quality indicators


📞 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


📚 Resources


🔬 Built on Validated Research

Scoring engine validated on IBM Quantum hardware with documented experimental results. Suitable for:

  • Enterprise innovation 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


© 2025 DSF UpTech. All rights reserved.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dsf_quantum_sdk-2.0.0.tar.gz (14.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dsf_quantum_sdk-2.0.0-py3-none-any.whl (12.8 kB view details)

Uploaded Python 3

File details

Details for the file dsf_quantum_sdk-2.0.0.tar.gz.

File metadata

  • Download URL: dsf_quantum_sdk-2.0.0.tar.gz
  • Upload date:
  • Size: 14.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.9

File hashes

Hashes for dsf_quantum_sdk-2.0.0.tar.gz
Algorithm Hash digest
SHA256 cc206f3faa34eabeeb3850103cb4a913481fa40efcf62715ee9d8c0b2b896c31
MD5 57d75cc00de86d8d41762a1ad99f1753
BLAKE2b-256 4d8b05e996acd3e58c5cd8fa5536aa6eb8b037b013efe5801e28fc51f4d0ffee

See more details on using hashes here.

File details

Details for the file dsf_quantum_sdk-2.0.0-py3-none-any.whl.

File metadata

File hashes

Hashes for dsf_quantum_sdk-2.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 fcbe697d53c4968a6b331390427bb1cd6ac58261f5bdcf445414ce59d8caebad
MD5 780511076479e2d3876b66c2e4db3ceb
BLAKE2b-256 fb439f820d78460b39d8ea7843f8589717ef907e87c940428beed3fc4aa9f1c3

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page