QUANTUM-INSPIRED ALGORITHMS FOR ENHANCING CREATIVITY IN GENERATIVE DIGITAL ART MODELS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7463Keywords:
Quantum-Inspired Algorithms, Generative Digital Art, Computational Creativity, Gans, Diffusion Models, Quantum Annealing, Latent Space Exploration, Artificial Intelligence In ArtAbstract [English]
The current speed in generative artificial intelligence has brought major development to the creation of digital art, but the current models are in most cases incapable of producing true creativity because of their dependence on learned data distributions. This article introduces a new model, which includes quantum-inspired algorithms into the generative digital art models to foster creativity, diversity, and originality. Based on the ideas of superposition, probabilistic representation, and quantum-inspired optimization, the suggested solution refuses the latent space definition as a high-dimensional probabilistic landscape, which allows exploring various artistic options at the same time. The architecture has quantum-inspired encoding, annealing-like optimization and stochastic sampling sequences in traditional architectures of GANs and diffusion models. Test on experimental evaluation was done based on benchmark datasets such as WikiArt and large-scale art corpora. The suggested model was evaluated on the basis of not only quantitative measures like Fréchet Inception Distance (FID), Inception Score (IS), and a complex Creativity Index (CI), but also qualitative human evaluations. Findings prove that quantum-inspired model outcompares classical generative models in terms of lower FID scores, greater diversity, and much better novelty. User studies also conclude aesthetic appeal and originality of generated art works. The results suggest the prospect of quantum-inspired computation being a viable and realistic scalable method of further development of computational creativity. The research is valuable as it helps to develop a more expressive and innovative form of generative systems through the application of the ideas of quantum theory and artificial intelligence. In the future, it will be integrated with actual quantum hardware and multimodal creative uses.
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Copyright (c) 2026 Dr. Mary Gladence L, Shilpa Narula, Sahil Suri, Manoj I. Patel, Shanthi R, Dinesh Kumar R, Dr. Balkrishna K. Patil
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