Advancing Quantum Natural Language Processing: Novel Quantum Circuits and Modular Execution Techniques

Abstract

[EN] Quantum computing is widely regarded as one of the scientific fields with the greatest transformative potential. Its applications are already being explored in areas such as cybersecurity, chemistry, and machine learning. Within the domain of quantum machine learning, one emerging subfield is quantum natural language processing (QNLP), which seeks to leverage quantum algorithms to address complex language-related tasks more efficiently than classical counterparts. This thesis begins with a systematic literature review of the current landscape of quantum machine learning, focusing particularly on the design, development, and deployment of QNLP models on real quantum hardware. The SLR identifies 94 relevant articles that utilize quantum machine learning techniques and algorithms, highlighting their implementation via computational quantum circuits, or ansatzes. The main types of algorithms found include quantum adaptations of classical models such as support vector machines and k-nearest neighbors, as well as quantum neural networks, one of the most prominent applications being image classification. In addition, this work offers both quantitative and qualitative analyses, integrating a comprehensive set of experiments conducted on quantum simulators and real quantum devices. A significant contribution of this work lies in the design and evaluation of these new quantum circuit architectures, tailored to optimize expressibility, entanglement, and trainability. Empirical analysis shows that model accuracy tends to exhibit an inverse relationship with ansatz expressibility and a direct correlation with entanglement capability. In parallel, it contributes to the field by proposing novel ansatz architectures and applying circuit knitting techniques to overcome current hardware limitations. Ultimately, this thesis demonstrates the feasibility of hybrid quantum-classical approaches in QNLP and highlights their potential to be extended to support more complex and scalable real-world language applications.