Engineers are working to design computers capable of handling a difficult class of tasks known as combinatorial optimization problems. These challenges are central to many everyday applications, including telecommunications planning, scheduling, and route optimization for travel.
Current computing technologies face physical limits on how much processing power can be built into a chip, and the energy required to train artificial intelligence models is enormous.
A collaborative team from UCLA and UC Riverside has introduced a new strategy to address these limitations and tackle some of the hardest optimization problems. Instead of representing all information digitally, their system processes data through a network of oscillators — components that shift back and forth at defined frequencies. This architecture, called an Ising machine, excels at parallel computing, enabling many calculations to run at the same time. The solution to the problem is reached when the oscillators fall into synchrony.
In their report published in Physical Review Applied, the researchers described a device that relies on quantum properties connecting electrical activity with vibrations inside a material. Unlike most existing quantum computing approaches, which must be cooled to extremely low temperatures to preserve their quantum state, this device can function at room temperature.
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