# Machine learning for long-distance quantum communication

arXiv: https://arxiv.org/abs/1904.10797 · HTML: https://mlsanity.com/paper/1904.10797/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 10 of 10)

- [Meta-Designing Quantum Experiments with Language Models](https://mlsanity.com/paper/2406.02470/) (2024)
- [Artificial intelligence for representing and characterizing quantum systems](https://mlsanity.com/paper/2509.04923/) (2025)
- [Quantum Data Center: Perspectives](https://mlsanity.com/paper/2309.06641/) (2023)
- [Practical distributed quantum information processing with LOCCNet](https://mlsanity.com/paper/2101.12190/) (2021)
- [Towards interpretable quantum machine learning via single-photon quantum walks](https://mlsanity.com/paper/2301.13669/) (2023)
- [Quantum Artificial Intelligence: A Brief Survey](https://mlsanity.com/paper/2408.10726/) (2024)
- [Parameterized quantum comb and simpler circuits for reversing unknown qubit-unitary operations](https://mlsanity.com/paper/2403.03761/) (2024)
- [Surrogate-guided optimization in quantum networks](https://mlsanity.com/paper/2407.17195/) (2024)
- [Ab-initio experimental violation of Bell inequalities](https://mlsanity.com/paper/2108.00574/) (2021)
- [Interpreting Quantum Learning Models via Stochastic Processes](https://mlsanity.com/paper/2607.17327/) (2026)

## Abstract (arXiv)

Machine learning can help us in solving problems in the context big data analysis and classification, as well as in playing complex games such as Go. But can it also be used to find novel protocols and algorithms for applications such as large-scale quantum communication? Here we show that machine learning can be used to identify central quantum protocols, including teleportation, entanglement purification and the quantum repeater. These schemes are of importance in long-distance quantum communication, and their discovery has shaped the field of quantum information processing. However, the usefulness of learning agents goes beyond the mere re-production of known protocols; the same approach allows one to find improved solutions to long-distance communication problems, in particular when dealing with asymmetric situations where channel noise and segment distance are non-uniform. Our findings are based on the use of projective simulation, a model of a learning agent that combines reinforcement learning and decision making in a physically motivated framework. The learning agent is provided with a universal gate set, and the desired task is specified via a reward scheme. From a technical perspective, the learning agent has to deal with stochastic environments and reactions. We utilize an idea reminiscent of hierarchical skill acquisition, where solutions to sub-problems are learned and re-used in the overall scheme. This is of particular importance in the development of long-distance communication schemes, and opens the way for using machine learning in the design and implementation of quantum networks.