# A Graph-Based Classical and Quantum Approach to Deterministic L-System Inference

arXiv: https://arxiv.org/abs/2411.19906 · HTML: https://mlsanity.com/paper/2411.19906/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Quantum Recommendation Systems](https://mlsanity.com/paper/1603.08675/) (2016)
- [Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods](https://mlsanity.com/paper/2005.02162/) (2020)
- [Techniques for Inferring Context-Free Lindenmayer Systems With Genetic Algorithm](https://mlsanity.com/paper/1906.08860/) (2019)
- [Stochastic L-system Inference from Multiple String Sequence Inputs](https://mlsanity.com/paper/2001.10922/) (2020)
- [Optimal L-Systems for Stochastic L-system Inference Problems](https://mlsanity.com/paper/2409.02259/) (2024)

## Abstract (arXiv)

L-systems can be made to model and create simulations of many biological processes, such as plant development. Finding an L-system for a given process is typically solved by hand, by experts, in a massively time-consuming process. It would be significant if this could be done automatically from data, such as from sequences of images. In this paper, we are interested in inferring a particular type of L-system, deterministic context-free L-system (D0L-system) from a sequence of strings. We introduce the characteristic graph of a sequence of strings, which we then utilize to translate our problem (inferring D0L-systems) in polynomial time into the maximum independent set problem (MIS) and the SAT problem. After that, we offer a classical exact algorithm and an approximate quantum algorithm for the problem.