# Level Generation with Quantum Reservoir Computing

arXiv: https://arxiv.org/abs/2505.13287 · HTML: https://mlsanity.com/paper/2505.13287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 19 May 2025.

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

- [Challenges and Opportunities in Quantum Optimization](https://mlsanity.com/paper/2312.02279/) (2023)
- [Bootstrapping Conditional GANs for Video Game Level Generation](https://mlsanity.com/paper/1910.01603/) (2019)
- [Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder](https://mlsanity.com/paper/2102.12463/) (2021)
- [Quantum Natural Language Generation on Near-Term Devices](https://mlsanity.com/paper/2211.00727/) (2022)
- [A quantum procedure for map generation](https://mlsanity.com/paper/2005.10327/) (2020)
- [Reservoir Computing based on Quenched Chaos](https://mlsanity.com/paper/1909.01571/) (2019)
- [Defining Quantum Games](https://mlsanity.com/paper/2206.00089/) (2022)
- [Level Generation with Constrained Expressive Range](https://mlsanity.com/paper/2504.05334/) (2025)

## Abstract (arXiv)

Reservoir computing is a form of machine learning particularly suited for time series analysis, including forecasting predictions. We take an implementation of quantum reservoir computing that was initially designed to generate variants of musical scores and adapt it to create levels of Super Mario Bros. Motivated by our analysis of these levels, we develop a new Roblox textitobby where the courses can be generated in real time on superconducting qubit hardware, and investigate some of the constraints placed by such real-time generation.