Rigorous noise reduction with quantum autoencoders

TL;DR

This paper introduces a quantum autoencoder scheme that effectively reduces noise in quantum states, demonstrating perfect reconstruction in certain models and practical applications like cooling states and improving quantum distillation, with experimental validation.

Contribution

It presents a noise reduction method using a quantum autoencoder with rigorous guarantees, implementable with unitary operations and demonstrated experimentally.

Findings

Perfect reconstruction in specific noise models

Significant reduction in magic state distillation costs

Experimental noise reduction in photonic circuits

Abstract

Reducing noise in quantum systems is a major challenge towards the application of quantum technologies. Here, we propose and demonstrate a scheme to reduce noise using a quantum autoencoder with rigorous performance guarantees. The quantum autoencoder learns to compresses noisy quantum states into a latent subspace and removes noise via projective measurements. We find various noise models where we can perfectly reconstruct the original state even for high noise levels. We apply the autoencoder to cool thermal states to the ground state and reduce the cost of magic state distillation by several orders of magnitude. Our autoencoder can be implemented using only unitary transformations without ancillas, making it immediately compatible with the state of the art. We experimentally demonstrate our methods to reduce noise in a photonic integrated circuit. Our results can be directly applied to make quantum technologies more robust to noise.