Learning to Utilize Correlated Auxiliary Noise: A Possible Quantum Advantage

TL;DR

This paper shows neural networks can learn to leverage correlated auxiliary noise to decode noisy data, potentially enabling quantum advantage through machine-learned quantum error correction.

Contribution

It introduces a method where neural networks exploit correlated auxiliary noise, suggesting a pathway for quantum advantage via learned quantum error correction.

Findings

Neural networks can learn to use correlated auxiliary noise as an approximate key.

Scaling behavior indicates potential quantum advantage with future quantum machines.

Correlated auxiliary noise can be harnessed for quantum error correction.

Abstract

This paper has two messages. First, we demonstrate that neural networks that process noisy data can learn to exploit, when available, access to auxiliary noise that is correlated with the noise on the data. In effect, the network learns to use the correlated auxiliary noise as an approximate key to decipher its noisy input data. Second, we show that, for this task, the scaling behavior with increasing noise is such that future quantum machines could possess an advantage. In particular, decoherence generates correlated auxiliary noise in the environment. The new approach could, therefore, help enable future quantum machines by providing machine-learned quantum error correction.