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Cutting Through the Noise

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Noise robustness of the DNN classification method.

A team of scientists used a machine learning method called a deep neural network to discern the signal created by the spin orientation of electrons on quantum dots.

Credit: Yuta Matsumoto et al.

Researchers led by the Institute of Scientific and Industrial Research (SANKEN) at Osaka University have trained a deep neural network to correctly determine the output state of quantum bits, despite environmental noise. The team's novel approach may allow quantum computers to become much more widely used.

Modern computers are based on binary logic, in which each bit is constrained to be either a 1 or a 0. But thanks to the weird rules of quantum mechanics, new experimental systems can achieve increased computing power by allowing quantum bits, also called qubits, to be in "superpositions" of 1 and 0. For example, the spins of electrons confined to tiny islands called quantum dots can be oriented both up and down simultaneously. However, when the final state of a bit is read out, it reverts to the classical behavior of being one orientation or the other. To make reliable enough for consumer use, new systems will need to be created that can accurately record the output of each even if there is a lot of noise in the signal.

From Osaka University
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