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Uncovering the hidden “noise” that can kill qubits

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https://news.mit.edu/2019/non-gaussian-n...ubits-0916

INTRO: MIT and Dartmouth College researchers have demonstrated, for the first time, a tool that detects new characteristics of environmental “noise” that can destroy the fragile quantum state of qubits, the fundamental components of quantum computers. The advance may provide insights into microscopic noise mechanisms to help engineer new ways of protecting qubits.

Qubits can represent the two states corresponding to the classic binary bits, a 0 or 1. But, they can also maintain a “quantum superposition” of both states simultaneously, enabling quantum computers to solve complex problems that are practically impossible for classical computers. But a qubit’s quantum “coherence” — meaning its ability to maintain the superposition state — can fall apart due to noise coming from environment around the qubit. Noise can arise from control electronics, heat, or impurities in the qubit material itself, and can also can use serious computing errors that may be difficult to correct.

Researchers have developed statistics-based models to estimate the impact of unwanted noise sources surrounding qubits to create new ways to protect them, and to gain insights into the noise mechanisms themselves. But, those tools generally capture simplistic “Gaussian noise,” essentially the collection of random disruptions from a large number of sources. In short, it’s like white noise coming from the murmuring of a large crowd, where there’s no specific disruptive pattern that stands out, so the qubit isn’t particularly affected by any one particular source. In this type of model, the probability distribution of the noise would form a standard symmetrical bell curve, regardless of the statistical significance of individual contributors.

In a paper published today in the journal Nature Communications, the researchers describe a new tool that, for the first time, measures “non-Gaussian noise” affecting a qubit. This noise features distinctive patterns that generally stem from a few particularly strong noise sources.

The researchers designed techniques to separate that noise from the background Gaussian noise, and then used signal-processing techniques to reconstruct highly detailed information about those noise signals. Those reconstructions can help researchers build more realistic noise models, which may enable more robust methods to protect qubits from specific noise types. There is now a need for such tools, the researchers say: Qubits are being fabricated with fewer and fewer defects, which could increase the presence of non-Gaussian noise.

“It’s like being in a crowded room. If everyone speaks with the same volume, there is a lot of background noise, but I can still maintain my own conversation. However, if a few people are talking particularly loudly, I can’t help but lock on to their conversation. It can be very distracting,” says William Oliver, an associate professor of electrical engineering and computer science, professor of the practice of physics, MIT Lincoln Laboratory Fellow, and associate director of the Research Laboratory for Electronics (RLE). “For qubits with many defects, there is noise that decoheres, but we generally know how to handle that type of aggregate, usually Gaussian noise. However, as qubits improve and there are fewer defects, the individuals start to stand out, and the noise may no longer be simply of a Gaussian nature. We can find ways to handle that, too, but we first need to know the specific type of non-Gaussian noise and its statistics.”

“It is not common for theoretical physicists to be able to conceive of an idea and also find an experimental platform and experimental colleagues willing to invest in seeing it through,” says co-author Lorenza Viola, a professor of physics at Dartmouth. “It was great to be able to come to such an important result with the MIT team.” (MORE - details)
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