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Qode Engine: Production-Ready Quantum Solutions

Build. Tune. Deploy. Scale.

Qode Engine provides developers and data scientists with a robust Python SDK for seamlessly integrating quantum-enhanced workflows into enterprise architectures. While currently in Beta, Qode Engine’s underlying infrastructure is designed to scale—from early prototypes to production-grade solutions.

What Qode Engine Offers

6options

Advanced Quantum Algorithms

  • Optimization: Efficiently solve complex optimization problems using TetraOpt, TQoptimaX, ClearVu Analytics, QuEnc, and TQrouting.

  • Quantum Machine Learning: Enhance predictive accuracy and analytical depth with TQml.

SDKgettingstarted

Robust Python SDK & APIs

  • Comprehensive APIs and detailed documentation streamline quantum model development.

  • Optimize and iterate quantum models rapidly, fully integrated within your existing Python-based workflow.

hybrid options

Flexible, Hybrid Deployment

  • Combine CPUs, GPUs, and (soon) Quantum Processing Units (QPUs) for peak performance and cost-efficiency.

  • Deploy seamlessly on-cloud (AWS, Azure, Google Cloud) or on-premise, aligning with your existing infrastructure.

3steps

Evolving Secure, Compliant Architecture

  • Qode Engine is built on enterprise-grade architecture, ensuring compliance with security standards.

  • Customizable Role-based Access Control (RBAC) and Relationship-based Access Control (ReBAC), supporting OIDC/OAuth2 authentication.

Which Libraries Are Available in the SDK?

TQ42 Studio-SDK-libraries

How It Works

Data Ingestion


Manually prepare or retrieve data from your existing systems.

Model Development


Use the Python SDK to design, test, and tune quantum ML or optimization workflows.

Inference


Run solutions on HPC (CPUs, GPUs) or selected QPUs. Expanded QPU availability planned in future releases.
Ready to Scale Your Quantum Capabilities?
Bring quantum capabilities into your Python-driven ecosystem, leveraging powerful libraries and hybrid compute flexibility.