Target is under 10 qubits.
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Hardware-constrained learning for quantum computing and artificial intelligence
Simulation studio
A naive QUBO encoding starts above the available qubit budget and must be reduced through reformulation choices.
Variable merging, penalty absorption, and subgraph factoring move the learner across a quality-versus-qubit scatter chart with a goal of staying under 10 qubits at greater than 90 percent quality.
Module context
Module 2 simulations focus on the classical support machinery that keeps near-term quantum workflows tractable under sparse hardware and small qubit budgets.
Live lab
This studio route isolates a single simulation so the learner can focus on one model, one control surface, and one explanatory framing at a time.
Browser-playable lab
Apply reformulation moves until the QUBO fits the qubit budget while preserving acceptable solution quality.
Controls
Outputs
Target is under 10 qubits.
Target is above 90 percent solution quality.
Both constraints must hold at once.
Variable merging, penalty absorption, and subgraph factoring together show how much classical reformulation work is needed before a small quantum device becomes relevant.
Why this lab matters
Graph Shrinking Workshop sits inside Module 2to reinforce the module's core teaching objective through direct manipulation rather than summary-only reading.
Module 2 simulations focus on the classical support machinery that keeps near-term quantum workflows tractable under sparse hardware and small qubit budgets.
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