Recently, the mathematical framework of quantum mechanics has boosted interpretability and performance in a variety of NLP tasks. Since different linguistic units are explicitly weighted by this framework in density matrix, it is also often believed that the models inspired by quantum mechanics afford transparency. However, the relationship between weights of linguistic units and model outputs is uncertain. In this work we test whether this assumption holds by performing extensive experiments in question answering (QA) model to manipulating semantic weights and assessing the resulting differences in its output. Although we observe in some ways that higher weights do correlate with greater effect on model, but most of the data does not support it. i.e., the weight is independent of the prediction, and different weight distributions can produce equivalent result. Our results illustrate that standard quantum-inspired model has interpretable structure but does not accurately model feature importance and requires improvement in semantic representation and model interpretability.
Recently, the mathematical framework of quantum mechanics has boosted interpretability and performance in a variety of NLP tasks. Since different linguistic units are explicitly weighted by this framework in density matrix, it is also often believed that the models inspired by quantum mechanics afford transparency. However, the relationship between weights of linguistic units and model outputs is uncertain. In this work we test whether this assumption holds by performing extensive experiments in question answering (QA) model to manipulating semantic weights and assessing the resulting differences in its output. Although we observe in some ways that higher weights do correlate with greater effect on model, but most of the data does not support it. i.e., the weight is independent of the prediction, and different weight distributions can produce equivalent result. Our results illustrate that standard quantum-inspired model has interpretable structure but does not accurately model feature importance and requires improvement in semantic representation and model interpretability.
Multi-task learning has demonstrated remarkable success across a broad spectrum of natural language processing tasks, particularly with neural network-based methods. Despite these advances, a fundamental gap remains in explaining the relationship between task-relatedness and model effectiveness. To address this issue, we propose a novel approach for implicitly modeling task-relatedness by leveraging a quantum physical mathematical framework. In this paper, we introduce a complex-valued neural network designed to encapsulate and analyze task-relatedness. Within this framework, sentences originating from diverse tasks are encoded as mixed quantum systems, represented on a meticulously defined Semantic Hilbert Space. This allows the network to interpret inter-task relationships through the explicit physical semantics of well-constrained components grounded in quantum probability theory. By adhering to these rigorous principles, our model not only establishes a robust method for quantifying task-relatedness but also fosters a deeper, self-explanatory understanding of the underlying processes. To validate the efficacy of our approach, we conducted extensive experiments across five benchmark text classification tasks. The results demonstrate both the superior performance and the interpretability of the proposed model, highlighting its potential as a self-explanatory system for multi-task learning in NLP.
In the field of Natural Language Processing (NLP), especially for many specific tasks, representing and modeling semantic of text plays essential role. Generally speaking, by extracting feature of words, we can find the internal relationship between words and model text semantics. Further more, there have been achievements in using quantum concepts to construct language models, which suggests the effectiveness of applying quantum theory to semantic learning and representation. However, in the current research of quantum-inspired language model, semantic of the text is often directly modeled by quantum concept (e.g., density matrix), which blurs the original meaning of the word itself. In this study, we propose the composite semantic system (CSS) hypothesis and construct a quantum-inspired semantic representation network (QSRN). We apply QSRN to four different text classification tasks, the experimental results show the effectiveness of the algorithm and verify the hypothesis we proposed.
Language Modeling (LM) is a fundamental research topic in a range of areas. Recently, inspired by quantum theory, a novel Quantum Language Model (QLM) has been proposed for Information Retrieval (IR). In this paper, we aim to broaden the theoretical and practical basis of QLM. We develop a Neural Network based Quantum-like Language Model (NNQLM) and apply it to Question Answering. Specifically, based on word embeddings, we design a new density matrix, which represents a sentence (e.g., a question or an answer) and encodes a mixture of semantic subspaces. Such a density matrix, together with a joint representation of the question and the answer, can be integrated into neural network architectures (e.g., 2-dimensional convolutional neural networks). Experiments on the TREC-QA and WIKIQA datasets have verified the effectiveness of our proposed models.
The rapid advancement of Artificial Intelligence (AI) has heightened concerns about the opacity of decision-making processes in complex models, particularly deep neural networks. This research explores a novel paradigm that integrates quantum computing principles into Explainable AI (XAI) to enhance interpretability without compromising predictive accuracy. The study introduces a Quantum-inspired Interpretable Model (QIIM) framework that leverages Hilbert space embeddings, unitary transformations, and operator-based learning to represent knowledge in a mathematically transparent form. The proposed model computes interpretability through the expectation values of observable operators, enabling the decomposition of decisions into quantifiable, human-understandable components. To capture hierarchical relationships, a Tensor Network Interpretable Model (TNIM) is further developed, offering scalable insights into complex dependencies among features. Experimental evaluations—performed on benchmark datasets for decision-making tasks—demonstrate that the quantum-inspired models achieve competitive accuracy while significantly improving local and global interpretability metrics compared to classical XAI techniques. The findings underscore the potential of quantum formalism as a new foundation for transparent AI systems, bridging the gap between computational efficiency and explainability. This study contributes to both theoretical understanding and practical advancement in interpretable machine learning, paving the way for ethically aligned, transparent, and human-trustworthy AI-driven decision support systems.
Quantum language models (QLMs) in which words are modeled as a quantum superposition of sememes have demonstrated a high level of model transparency and good post-hoc interpretability. Nevertheless, in the current literature, word sequences are basically modeled as a classical mixture of word states, which cannot fully exploit the potential of a quantum probabilistic description. A quantum-inspired neural network (NN) module is yet to be developed to explicitly capture the nonclassical correlations within the word sequences. We propose a NN model with a novel entanglement embedding (EE) module, whose function is to transform the word sequence into an entangled pure state representation. Strong quantum entanglement, which is the central concept of quantum information and an indication of parallelized correlations among the words, is observed within the word sequences. The proposed QLM with EE (QLM-EE) is proposed to implement on classical computing devices with a quantum-inspired NN structure, and numerical experiments show that QLM-EE achieves superior performance compared with the classical deep NN models and other QLMs on question answering (QA) datasets. In addition, the post-hoc interpretability of the model can be improved by quantifying the degree of entanglement among the word states.
Quantum-Inspired Language Models (QLMs) model sentences through the superposition of semantic features and have achieved certain progress in various tasks. The defining characteristic of this approach lies in its use of the mathematical framework of quantum mechanics to model textual data. However, existing QLMs lack mature iterative update mechanisms for sentence representations. This paper proposes a Quantum-Inspired Unitary Transformation and Transformer for Question-Answering (QUTT-QA) model, which enhances the semantic expressiveness of word vectors via pretraining and leverages quantum-inspired operations to strengthen the model's capability in processing complex sequential data. Experimental results on QA tasks show that the proposed QUTT-QA model outperforms most traditional QLMs in accuracy and recall. Additionally, ablation studies on key quantum modules validate the effectiveness of the QUTT-QA model.