# Practical distributed quantum information processing with LOCCNet

arXiv: https://arxiv.org/abs/2101.12190 · HTML: https://mlsanity.com/paper/2101.12190/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 96 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [Universal Quantum Control through Deep Reinforcement Learning](https://mlsanity.com/paper/1803.01857/) (2018)
- [Machine learning for long-distance quantum communication](https://mlsanity.com/paper/1904.10797/) (2019)
- [Variational Quantum Algorithms for Trace Distance and Fidelity Estimation](https://mlsanity.com/paper/2012.05768/) (2020)
- [Quantum Codes from Neural Networks](https://mlsanity.com/paper/1806.08781/) (2018)

## Built on by (most cited first, 4 of 4)

- [Quantum Self-Attention Neural Networks for Text Classification](https://mlsanity.com/paper/2205.05625/) (2022)
- [Learning Quantum Entanglement Distillation with Noisy Classical Communications](https://mlsanity.com/paper/2205.08561/) (2022)
- [Parameterized quantum comb and simpler circuits for reversing unknown qubit-unitary operations](https://mlsanity.com/paper/2403.03761/) (2024)
- [Quantum Machine Learning for Distributed Quantum Protocols with Local Operations and Noisy Classical Communications](https://mlsanity.com/paper/2207.11354/) (2022)

## Abstract (arXiv)

Distributed quantum information processing is essential for building quantum networks and enabling more extensive quantum computations. In this regime, several spatially separated parties share a multipartite quantum system, and the most natural set of operations is Local Operations and Classical Communication (LOCC). As a pivotal part in quantum information theory and practice, LOCC has led to many vital protocols such as quantum teleportation. However, designing practical LOCC protocols is challenging due to LOCC's intractable structure and limitations set by near-term quantum devices. Here we introduce LOCCNet, a machine learning framework facilitating protocol design and optimization for distributed quantum information processing tasks. As applications, we explore various quantum information tasks such as entanglement distillation, quantum state discrimination, and quantum channel simulation. We discover protocols with evident improvements, in particular, for entanglement distillation with quantum states of interest in quantum information. Our approach opens up new opportunities for exploring entanglement and its applications with machine learning, which will potentially sharpen our understanding of the power and limitations of LOCC. An implementation of LOCCNet is available in Paddle Quantum, a quantum machine learning Python package based on PaddlePaddle deep learning platform.