Quantum Approach for Contextual Search, Retrieval, and Ranking of Classical Information.

ABSTRACT: 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 c

SUBMITTER: Alodjants AP 

PROVIDER: S-EPMC11508094 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature