The Dawn of Quantum Natural Language Processing

• URL: http://arxiv.org/abs/2110.06510v1
• Date: Wed, 13 Oct 2021 05:46:57 GMT
• Title: The Dawn of Quantum Natural Language Processing
• Authors: Riccardo Di Sipio, Jia-Hong Huang, Samuel Yen-Chi Chen, Stefano Mangini, Marcel Worring
• Abstract summary: We train a quantum-enhanced Long Short-Term Memory network to perform the parts-of-speech tagging task. A quantum-enhanced Transformer is proposed to perform the sentiment analysis based on the existing dataset.
• Score: 13.482584048760485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing. We successfully train a quantum-enhanced Long Short-Term Memory network to perform the parts-of-speech tagging task via numerical simulations. Moreover, a quantum-enhanced Transformer is proposed to perform the sentiment analysis based on the existing dataset.

Related papers

• 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)
• End-to-End Quantum Vision Transformer: Towards Practical Quantum Speedup in Large-Scale Models [20.72342380227143]
  This paper introduces an end-to-end Quantum Vision Transformer (QViT), which incorporates an innovative quantum residual connection technique. Our thorough analysis of the QViT reveals a theoretically exponential complexity and empirically speedup, showcasing the model's efficiency and potential in quantum computing applications.
  arXiv  Detail & Related papers  (2024-02-29T08:12:02Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• 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)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• Quantum Neural Network Classifiers: A Tutorial [1.4567067583556714]
  We focus on quantum neural networks in the form of parameterized quantum circuits. We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks. benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language.
  arXiv  Detail & Related papers  (2022-06-06T18:00:01Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning [84.33745072274942]
  We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
  arXiv  Detail & Related papers  (2021-03-08T17:24:29Z)
• Variational learning for quantum artificial neural networks [0.0]
  We first review a series of recent works describing the implementation of artificial neurons and feed-forward neural networks on quantum processors. We then present an original realization of efficient individual quantum nodes based on variational unsampling protocols. While keeping full compatibility with the overall memory-efficient feed-forward architecture, our constructions effectively reduce the quantum circuit depth required to determine the activation probability of single neurons.
  arXiv  Detail & Related papers  (2021-03-03T16:10:15Z)
• Facial Expression Recognition on a Quantum Computer [68.8204255655161]
  We show a possible solution to facial expression recognition using a quantum machine learning approach. We define a quantum circuit that manipulates the graphs adjacency matrices encoded into the amplitudes of some appropriately defined quantum states.
  arXiv  Detail & Related papers  (2021-02-09T13:48:00Z)

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.