<ul><li>Researchers have developed a quantum-inspired framework that compresses large Ising models to fit available quantum hardware.</li><li>The framework utilizes a physics-inspired GNN architecture to capture complex interactions in Ising models and predict alignments among neighboring qubits.</li><li>By progressively merging aligned qubits, the model size can be reduced while preserving the optimization structure.</li><li>Numerical studies have shown that the method effectively reduces instance size without compromising solution quality on quantum annealers.</li></ul>

Scalable Quantum-Inspired Optimization through Dynamic Qubit Compression

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