Binary optimization by momentum annealing.

One of the vital roles of computing is to solve large-scale combinatorial optimization problems in a short time. In recent years, methods have been proposed that map optimization problems to ones of searching for the ground state of an Ising model by using a stochastic process. Simulated annealing (SA) is a representative algorithm. However, it is inherently difficult to perform a parallel search. Here we propose an algorithm called momentum annealing (MA), which, unlike SA, updates all spins of fully connected Ising models simultaneously and can be implemented on GPUs that are widely used for scientific computing. MA running in parallel on GPUs is 250 times faster than SA running on a modern CPU at solving problems involving 100 000 spin Ising models.

Uncertain behaviours of integrated circuits improve computational performance.

Improvements to the performance of conventional computers have mainly been achieved through semiconductor scaling; however, scaling is reaching its limitations. Natural phenomena, such as quantum superposition and stochastic resonance, have been introduced into new computing paradigms to improve performance beyond these limitations. Here, we explain that the uncertain behaviours of devices due to semiconductor scaling can improve the performance of computers. We prototyped an integrated circuit by performing a ground-state search of the Ising model. The bit errors of memory cell devices holding the current state of search occur probabilistically by inserting fluctuations into dynamic device characteristics, which will be actualised in the future to the chip. As a result, we observed more improvements in solution accuracy than that without fluctuations. Although the uncertain behaviours of devices had been intended to be eliminated in conventional devices, we demonstrate that uncertain behaviours has become the key to improving computational performance.