Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications

• URL: http://arxiv.org/abs/2504.09909v1
• Date: Mon, 14 Apr 2025 06:09:26 GMT
• Title: Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications
• Authors: Farha Nausheen, Khandakar Ahmed, M Imad Khan,
• Abstract summary: It is proposed to categorise QNLP models based on quantum computing principles, architecture, and computational approaches.<n>This paper attempts to provide a survey on how quantum meets language by mapping state-of-the-art in this area.
• Score: 0.34284444670464664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent developments, deep learning methodologies applied to Natural Language Processing (NLP) have revealed a paradox: They improve performance but demand considerable data and resources for their training. Alternatively, quantum computing exploits the principles of quantum mechanics to overcome the computational limitations of current methodologies, thereby establishing an emerging field known as quantum natural language processing (QNLP). This domain holds the potential to attain a quantum advantage in the processing of linguistic structures, surpassing classical models in both efficiency and accuracy. In this paper, it is proposed to categorise QNLP models based on quantum computing principles, architecture, and computational approaches. This paper attempts to provide a survey on how quantum meets language by mapping state-of-the-art in this area, embracing quantum encoding techniques for classical data, QNLP models for prevalent NLP tasks, and quantum optimisation techniques for hyper parameter tuning. The landscape of quantum computing approaches applied to various NLP tasks is summarised by showcasing the specific QNLP methods used, and the popularity of these methods is indicated by their count. From the findings, it is observed that QNLP approaches are still limited to small data sets, with only a few models explored extensively, and there is increasing interest in the application of quantum computing to natural language processing tasks.

Related papers

• Hamiltonian Dynamics Learning: A Scalable Approach to Quantum Process Characterization [6.741097425426473]
  We introduce an efficient quantum process learning method specifically designed for short-time Hamiltonian dynamics.<n>We demonstrate applications in quantum machine learning, where our protocol enables efficient training of variational quantum neural networks by directly learning unitary transformations.<n>This work establishes a new theoretical foundation for practical quantum dynamics learning, paving the way for scalable quantum process characterization in both near-term and fault-tolerant quantum computing.
  arXiv  Detail & Related papers  (2025-03-31T14:50:00Z)
• Comparative study of the ansätze in quantum language models [12.109572897953413]
  Quantum natural language processing (QNLP) methods and frameworks exist for text classification and generation.<n>We evaluate the performance of quantum natural language processing models based on these ans"atze at different levels in text classification tasks.
  arXiv  Detail & Related papers  (2025-02-28T05:49:38Z)
• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Multimodal Quantum Natural Language Processing: A Novel Framework for using Quantum Methods to Analyse Real Data [0.0]
  This thesis explores how quantum computational methods can enhance the compositional modeling of language. Specifically, it advances Multimodal Quantum Natural Language Processing (MQNLP) by applying the Lambeq toolkit. Results indicate that syntax-based models, particularly DisCoCat and TreeReader, excel in effectively capturing grammatical structures.
  arXiv  Detail & Related papers  (2024-10-29T19:03:43Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Exploring quantum localization with machine learning [39.58317527488534]
  We introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization. Our approach integrates a versatile quantum phase space parametrization leading to a custom 'quantum' NN, with the pattern recognition capabilities of a modified convolutional model.
  arXiv  Detail & Related papers  (2024-06-01T08:50:26Z)
• Separable Power of Classical and Quantum Learning Protocols Through the Lens of No-Free-Lunch Theorem [70.42372213666553]
  The No-Free-Lunch (NFL) theorem quantifies problem- and data-independent generalization errors regardless of the optimization process. We categorize a diverse array of quantum learning algorithms into three learning protocols designed for learning quantum dynamics under a specified observable. Our derived NFL theorems demonstrate quadratic reductions in sample complexity across CLC-LPs, ReQu-LPs, and Qu-LPs. We attribute this performance discrepancy to the unique capacity of quantum-related learning protocols to indirectly utilize information concerning the global phases of non-orthogonal quantum states.
  arXiv  Detail & Related papers  (2024-05-12T09:05:13Z)
• Adapting Pre-trained Language Models for Quantum Natural Language Processing [33.86835690434712]
  We show that pre-trained representation can bring 50% to 60% increases to the capacity of end-to-end quantum models. On quantum simulation experiments, the pre-trained representation can bring 50% to 60% increases to the capacity of end-to-end quantum models.
  arXiv  Detail & Related papers  (2023-02-24T14:59:02Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer [0.7194733565949804]
  We present results on the first NLP experiments conducted on Noisy Intermediate-Scale Quantum (NISQ) computers. We create representations for sentences that have a natural mapping to quantum circuits. We successfully train NLP models that solve simple sentence classification tasks on quantum hardware.
  arXiv  Detail & Related papers  (2021-02-25T13:37:33Z)
• Grammar-Aware Question-Answering on Quantum Computers [0.17205106391379021]
  We perform the first implementation of an NLP task on noisy intermediate-scale quantum (NISQ) hardware. We encode word-meanings in quantum states and we explicitly account for grammatical structure. Our novel QNLP model shows concrete promise for scalability as the quality of the quantum hardware improves.
  arXiv  Detail & Related papers  (2020-12-07T14:49:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.