Learning to Utilize Correlated Auxiliary Noise: A Possible Quantum Advantage
This work addresses the problem of noise in data processing, potentially enabling quantum machines to overcome decoherence, though it appears incremental as it builds on existing noise exploitation concepts.
The paper demonstrates that neural networks can learn to exploit correlated auxiliary noise to decipher noisy input data, and shows that this approach could provide a scaling advantage for quantum machines by enabling machine-learned quantum error correction.
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.