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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.13287 |
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| _version_ | 1866911451493433344 |
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| author | Ferreira, João S. Fromholz, Pierre Shaji, Hari Wootton, James R. |
| author_facet | Ferreira, João S. Fromholz, Pierre Shaji, Hari Wootton, James R. |
| contents | Reservoir computing is a form of machine learning particularly suited for time series analysis, including forecasting predictions. We take an implementation of \emph{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 \textit{obby} 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13287 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Level Generation with Quantum Reservoir Computing Ferreira, João S. Fromholz, Pierre Shaji, Hari Wootton, James R. Artificial Intelligence Quantum Physics Reservoir computing is a form of machine learning particularly suited for time series analysis, including forecasting predictions. We take an implementation of \emph{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 \textit{obby} 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. |
| title | Level Generation with Quantum Reservoir Computing |
| topic | Artificial Intelligence Quantum Physics |
| url | https://arxiv.org/abs/2505.13287 |