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Quantum Optimization for AI

Build a quantum annealing pipeline for combinatorial optimization — choose feature selection or graph partitioning, solve with simulated annealing, and optionally connect to D-Wave hardware.

"Select the optimal 8 features from 30 — annealing beats greedy by 5% accuracy"

6 Modules

Each module builds on the previous one. By the end, you have a complete production system.

  1. 1

    Foundations

    Brute-force QUBO solver + energy landscape

  2. 2

    QUBO Formulation

    Feature selection + graph partition QUBOs

  3. 3

    Use Case Lab

    Problem-specific QUBO (student choice)

  4. 4

    Annealing Solver

    SA solver with convergence analysis

  5. 5

    Pipeline App

    Interactive Next.js dashboard

  6. 6

    Production & D-Wave

    Benchmarks + portfolio piece

Production patterns you'll master

QUBO FormulationSimulated AnnealingBenchmarkingD-Wave IntegrationVisualization

Synthetic data included

  • Classification dataset (30 features)
  • Social network graph (50 nodes)
  • Pre-computed MI scores
  • Correlation matrices
  • Community ground truth

What you walk away with

Shareable portfolio

A public URL showing your module timeline, patterns mastered, and completion status.

All the code

Download everything as a ZIP — pipelines, guardrails, deployment configs. Yours forever.

Module walkthrough

Each module documented with deliverables and the production pattern you implemented.

Ready to build your quantum optimization for ai?

Free during the early adopter program. No credit card required.