<ul><li>Quantum or quantum-inspired Ising machines have shown promise in solving combinatorial optimization problems quickly.</li><li>Real-world applications require solving dynamically changing problems, posing challenges for Ising machines.</li><li>Researchers have developed a method using embedded Ising machines to solve diverse problems at high speed.</li><li>The approach involves customizing the algorithm, circuit architecture, and utilizing a machine learning model for parameter estimation.</li></ul>

Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems

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