A Graph-Based Classical and Quantum Approach to Deterministic L-System Inference
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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.
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References · 5
- 2016Quantum Recommendation Systemscited by 69
- 2020Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methodscited by 42
- 2019Techniques for Inferring Context-Free Lindenmayer Systems With Genetic Algorithmcited by 3
- 2020Stochastic L-system Inference from Multiple String Sequence Inputscited by 2
- 2024Optimal L-Systems for Stochastic L-system Inference Problemscited by 2
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