# Quantum Stream Learning

arXiv: https://arxiv.org/abs/2112.06628 · HTML: https://mlsanity.com/paper/2112.06628/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Universal Quantum Control through Deep Reinforcement Learning](https://mlsanity.com/paper/1803.01857/) (2018)
- [Fidelity-based Probabilistic Q-learning for Control of Quantum Systems](https://mlsanity.com/paper/1806.03145/) (2018)
- [Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving](https://mlsanity.com/paper/2010.03655/) (2020)
- [Approximation of quantum control correction scheme using deep neural networks](https://mlsanity.com/paper/1803.05193/) (2018)
- [Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach](https://mlsanity.com/paper/1511.08862/) (2015)

## Abstract (arXiv)

The exotic nature of quantum mechanics makes machine learning (ML) be different in the quantum realm compared to classical applications. ML can be used for knowledge discovery using information continuously extracted from a quantum system in a broad range of tasks. The model receives streaming quantum information for learning and decision-making, resulting in instant feedback on the quantum system. As a stream learning approach, we present a deep reinforcement learning on streaming data from a continuously measured qubit at the presence of detuning, dephasing, and relaxation. We also investigate how the agent adapts to another quantum noise pattern by transfer learning. Stream learning provides a better understanding of closed-loop quantum control, which may pave the way for advanced quantum technologies.