# Multiqubit and multilevel quantum reinforcement learning with quantum technologies

arXiv: https://arxiv.org/abs/1709.07848 · HTML: https://mlsanity.com/paper/1709.07848/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2017.

## Built on by (most cited first, 9 of 9)

- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [A Survey on Quantum Reinforcement Learning](https://mlsanity.com/paper/2211.03464/) (2022)
- [Deep Reinforcement Learning with Quantum-inspired Experience Replay](https://mlsanity.com/paper/2101.02034/) (2021)
- [Towards interpretable quantum machine learning via single-photon quantum walks](https://mlsanity.com/paper/2301.13669/) (2023)
- [Quantum Measurement Classification with Qudits](https://mlsanity.com/paper/2107.09781/) (2021)
- [Active Learning for the Optimal Design of Multinomial Classification in Physics](https://mlsanity.com/paper/2109.08612/) (2021)
- [Implications of Deep Circuits in Improving Quality of Quantum Question Answering](https://mlsanity.com/paper/2305.07374/) (2023)
- [A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning](https://mlsanity.com/paper/2309.16972/) (2023)
- [A quantum system control method based on enhanced reinforcement learning](https://mlsanity.com/paper/2310.03036/) (2023)

## Abstract (arXiv)

We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations.