Error mitigation of entangled states using brainbox quantum autoencoders
This work addresses noise issues in quantum computing for researchers and engineers, but it appears incremental as it builds on existing quantum autoencoder methods.
The paper tackled the problem of noise limiting access to multi-qubit entangled states in quantum hardware by proposing brainbox quantum autoencoders, which use complex bottleneck structures to denoise faster and handle stronger noise, with results showing improved error mitigation capabilities.
Current quantum hardware is subject to various sources of noise that limits the access to multi-qubit entangled states. Quantum autoencoder circuits with a single qubit bottleneck have shown capability to correct error in noisy entangled state. By introducing slightly more complex structures in the bottleneck, the so-called brainboxes, the denoising process can take place faster and for stronger noise channels. Choosing the most suitable brainbox for the bottleneck is the result of a trade-off between noise intensity on the hardware, and the training impedance. Finally, by studying Rényi entropy flow throughout the networks we demonstrate that the localization of entanglement plays a central role in denoising through learning.