Quantum-inspired algorithms represent an important direction in modern software information technologies that use heuristic methods and approaches of quantum science. This work presents a quantum approach for document search, retrieval, and ranking based on the Bell-like test, which is well-known in quantum physics. We propose quantum probability theory in the hyperspace analog to language (HAL) framework exploiting a Hilbert space for word and document vector specification. The quantum approach allows for accounting for specific user preferences in different contexts. To verify the algorithm proposed, we use a dataset of synthetic advertising text documents from travel agencies generated by the OpenAI GPT-4 model. We show that the “entanglement” in two-word document search and retrieval can be recognized as the frequent occurrence of two words in incompatible query contexts. We have found that the user preferences and word ordering in the query play a significant role in relatively small sizes of the HAL window. The comparison with the cosine similarity metrics demonstrates the key advantages of our approach based on the user-enforced contextual and semantic relationships between words and not just their superficial occurrence in texts. Our approach to retrieving and ranking documents allows for the creation of new information search engines that require no resource-intensive deep machine learning algorithms.

The article discusses the role of generative neural networks in the development and optimization of fonts which play a key role in creating aesthetically attractive and functional designs. The main attention is paid to licensing restrictions and insufficient availability of fonts for various world languages, which creates difficulties for designers and typographers in the process of creating text materials. The novelty of the approach lies in the use of the diffusion model as a generative neural network for automatic font creation, including missing glyphs for languages not supported by standard fonts. To solve the tasks set, a diffusion model has been developed which is an algorithm for generating fonts based on the analysis of patterns in the structure of symbols and the logic of their construction. The model is integrated into an application that automates the process of creating font layouts, allowing users to generate new glyphs and fonts tailored to specific language needs. This technique includes preliminary data preparation, network training, and subsequent character generation that mimic the style and composition of the original fonts. During the experiments, the diffusion model demonstrated a high ability to generate high-quality font characters visually similar to the original samples. Font sets with a limited set of characters were used as source data, which allowed us to evaluate the capabilities of the model to create missing glyphs for various languages. The results showed that the developed model successfully reproduces the stylistic features of the original font, which confirms its potential for application in the development of font solutions for global use. The proposed method of font generation is of interest to specialists working in the field of design, typography, and the creation of text materials for various language audiences. The results obtained can be useful when creating fonts intended for use in multilingual projects that require the presence of missing characters.