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Quantum annealers, coherent Ising machines and digital Ising machines forsolving quantum-inspired optimization problems have been developing rapidly dueto their near-term applications. The numerical solvers of the digital Isingmachines are based on traditional computing devices. In this work, we propose afast and efficient solver for the Ising optimization problems. The algorithmconsists of a pruning method that exploits the graph information of the Isingmodel to reduce the computational complexity, and a domain selection methodwhich introduces significant acceleration by relaxing the discrete feasibledomain into a continuous one to incorporate the efficient gradient descentmethod. The experiment results show that our solver can be an order ofmagnitude faster than the classical solver, and at least two times faster thanthe quantum-inspired annealers including the simulated quantum annealing on thebenchmark problems. With more relaxed requirements on hardware and lower costthan quantum annealing, the proposed solver has the potential for near-termapplication in solving challenging optimization problems as well as serving asa benchmark for evaluating the advantage of quantum devices.

Fast Numerical Solver of Ising Optimization Problems via Pruning and Domain Selection