# Error mitigation of entangled states using brainbox quantum autoencoders

arXiv: https://arxiv.org/abs/2303.01134 · HTML: https://mlsanity.com/paper/2303.01134/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 67 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Generalization in quantum machine learning from few training data](https://mlsanity.com/paper/2111.05292/) (2021)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Theory of overparametrization in quantum neural networks](https://mlsanity.com/paper/2109.11676/) (2021)
- [Trainability of Dissipative Perceptron-Based Quantum Neural Networks](https://mlsanity.com/paper/2005.12458/) (2020)
- [Enabling Multi-programming Mechanism for Quantum Computing in the NISQ Era](https://mlsanity.com/paper/2102.05321/) (2021)
- [Experimental Implementation of a Quantum Autoencoder via Quantum Adders](https://mlsanity.com/paper/1807.10643/) (2018)
- [Denoising quantum states with Quantum Autoencoders -- Theory and Applications](https://mlsanity.com/paper/2012.14714/) (2020)
- [Bias-variance decomposition of overparameterized regression with random linear features](https://mlsanity.com/paper/2203.05443/) (2022)
- [Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks](https://mlsanity.com/paper/2301.08167/) (2023)

## Built on by (most cited first, 1 of 1)

- [Variational Denoising for Variational Quantum Eigensolver](https://mlsanity.com/paper/2304.00549/) (2023)

## Abstract (arXiv)

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 R'enyi entropy flow throughout the networks we demonstrate that the localization of entanglement plays a central role in denoising through learning.