Variational Quantum Brushes

Fuente: arXiv
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Hauptverfasser: Lu, Jui-Ting, Ennes, Henrique, Huang, Chih-Kang, Abbassi, Ali
Format: Preprint
Veröffentlicht: 2025
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author Lu, Jui-Ting
Ennes, Henrique
Huang, Chih-Kang
Abbassi, Ali
author_facet Lu, Jui-Ting
Ennes, Henrique
Huang, Chih-Kang
Abbassi, Ali
contents Quantum brushes are computational arts software introduced by Ferreira et al (2025) that leverage quantum behavior to generate novel artistic effects. In this outreach paper, we introduce the mathematical framework and describe the implementation of two quantum brushes based on variational quantum algorithms, Steerable and Chemical. While Steerable uses quantum geometric control theory to merge two works of art, Chemical mimics variational eigensolvers for estimating molecular ground energies to evolve colors on an underlying canvas. The implementation of both brushes is available open-source at https://github.com/moth-quantum/QuantumBrush and is fully compatible with the original quantum brushes.
format Preprint
id arxiv_https___arxiv_org_abs_2512_24173
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Variational Quantum Brushes
Lu, Jui-Ting
Ennes, Henrique
Huang, Chih-Kang
Abbassi, Ali
Quantum Physics
Graphics
Machine Learning
Optimization and Control
Quantum brushes are computational arts software introduced by Ferreira et al (2025) that leverage quantum behavior to generate novel artistic effects. In this outreach paper, we introduce the mathematical framework and describe the implementation of two quantum brushes based on variational quantum algorithms, Steerable and Chemical. While Steerable uses quantum geometric control theory to merge two works of art, Chemical mimics variational eigensolvers for estimating molecular ground energies to evolve colors on an underlying canvas. The implementation of both brushes is available open-source at https://github.com/moth-quantum/QuantumBrush and is fully compatible with the original quantum brushes.
title Variational Quantum Brushes
topic Quantum Physics
Graphics
Machine Learning
Optimization and Control
url https://arxiv.org/abs/2512.24173