Solving complex optimization problems is central to many modern technologies, from logistics and financial modeling to chip design, communications and artificial intelligence (AI). However, as these problems grow in size, conventional computers often require substantial time and energy to search for good solutions.
A research team led by Professor Yang Hyunsoo from the Department of Electrical and Computer Engineering in the College of Design and Engineering at the National University of Singapore (NUS) has developed new spintronic computing hardware that offers a promising route toward faster, more energy-efficient optimization. The team reported two recent advances in Nature Communications, demonstrating probabilistic computing systems based on magnetic tunnel junctions, nanoscale devices that can naturally generate tunable randomness.
Quantum computing has long been viewed as a potential breakthrough for optimization, but practical quantum advantage remains difficult to achieve in the near term. The NUS team's work shows that probabilistic computing, built using scalable spintronic hardware, could provide a more immediate, hardware-efficient path.
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