Article  Biggest probabilistic computer turns noise into answers

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https://spectrum.ieee.org/biggest-probab...tent=hero4

INTRO: Probabilistic computers might one day tackle certain problems well beyond standard computers while at the same time avoiding the many hardware challenges that currently vex quantum computing. Now scientists reveal they have created the largest probabilistic computer yet, one with 1 million “probabilistic bits,” a new study they suggest reveals the way forward to building even bigger machines.

Probabilistic bits, or p-bits, bridge the gap between the bits underlying regular computers and the qubits upon which quantum computers are based. Bits symbolize data as either a 0 or 1. Qubits, because of the bizarre nature of quantum physics, can exist in a state where they are either 0 or 1 or any state in between simultaneously. In contrast, p-bits flip between 0 or 1 with a tunable probability.

A bit that flips back and forth between 0 and 1 might seem useless—indeed, in a regular computer this would be too noisy to operate. However, when many such noisy bits operate together in a correlated fashion, they can be used to solve a whole class of problems—stochastic problems—that operate on probabilities rather than concrete numbers. This includes optimization problems to, for instance, find the shortest route with which one can deliver a set of packages.

There are other kinds of machines that are also designed to tackle stochastic problems, such as quadratic unconstrained binary optimization (QUBO) devices or Ising machines. However, unlike those devices, probabilistic computers are not hardwired for a single problem, but are instead programmable general-purpose machines, says Kerem Çamsarı, an associate professor of electrical and computer engineering at the University of California, Santa Barbara.

In a 2019 Nature study, scientists developed a probabilistic computer with eight p-bits. By 2023, researchers had built a machine with 7,200 p-bits. However, these devices were each confined to a single chip. Networking together multiple such chips is not as simple as it is for regular GPUs or CPUs: the machine functions on correlated fluctuations, and syncing up those fluctuations across a set of wires is no easy feat. This raised questions as to whether probabilistic computers could scale to larger sizes, and what problems they might face if they tried... (MORE - details)
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