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A new study led by Dmitrii Dobrynin and collaborators, published in Physical Review E, offers insights into the energy landscapes of combinatorial optimization problems in Ising machines. The team uses advanced techniques to sample and visualize complex combinatorial energy landscape features, including energy barriers, degenerate local minima, and saddle regions. Their findings reveal structural challenges of hardware embedded problems that hinder Ising machine performance in navigating complex, non-convex energy landscapes in search of solutions. This work contributes to understanding and optimizing Ising machine design, vital for solving hard computational problems with greater speed and efficiency.
Read more in Phys. Rev. E.
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