A team of engineers, physicists and quantum specialists at Google Research found that reducing noise to a certain level allows the company’s Sycamore quantum chip to beat classical computers running random circuit sampling (RCS) .
In their study, published in the journal Naturethe group modified the conditions under which its processor operated to reduce the amount of noise interference to a level that allowed it to outperform classical computers running RCS.
Computer scientists have been trying to build a truly useful quantum computer for several decades, but they have yet to build one that can deliver on its original promise: running algorithms that would take supercomputers hundreds, thousands, or even millions of years classics. Yet continued progress has been made as new technologies are introduced.
Researchers have developed algorithms that can run on either a traditional supercomputer or a quantum computer, to test their capabilities against each other. One such algorithm is RCS, which essentially just generates a series of random numbers.
One of the biggest obstacles researchers have faced is errors produced by ambient noise. This has led to much research into methods of correcting errors or, optimally, methods of preventing them from occurring. Such research has produced results suggesting that background noise in the environment can cause errors: this noise is natural or environmental in nature; this can come from temperature changes, magnetic fields or even space radiation.
In this new study, Google researchers worked to reduce background noise, which in part involved placing their chip in a chamber near absolute zero while it operated. They found that even small reductions in noise, for example from an error-free rate of 99.4% to 99.7%, led to dramatic changes in capabilities.
They also managed to reduce their chip’s error rate enough to allow it to gain a “quantum advantage” when performing RCS, suggesting that scientists are getting closer and closer to the dream of a truly useful quantum computer.
More information:
A. Morvan et al, Phase transitions in random circuit sampling, Nature (2024). DOI: 10.1038/s41586-024-07998-6
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