A Survey of Classical And Quantum Sequence Models

• URL: http://arxiv.org/abs/2312.10242v1
• Date: Fri, 15 Dec 2023 22:21:26 GMT
• Title: A Survey of Classical And Quantum Sequence Models
• Authors: I-Chi Chen, Harshdeep Singh, V L Anukruti, Brian Quanz, Kavitha Yogaraj
• Abstract summary: This paper performs a comparative analysis of classical self-attention models and their quantum counterparts. We re-implement a key representative set of these existing methods, adapting an image classification approach using quantum self-attention to create a quantum hybrid transformer. We also explore different encoding techniques and introduce positional encoding into quantum self-attention neural networks leading to improved accuracy and faster convergence in text and image classification experiments.
• Score: 3.442372522693843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our primary objective is to conduct a brief survey of various classical and quantum neural net sequence models, which includes self-attention and recurrent neural networks, with a focus on recent quantum approaches proposed to work with near-term quantum devices, while exploring some basic enhancements for these quantum models. We re-implement a key representative set of these existing methods, adapting an image classification approach using quantum self-attention to create a quantum hybrid transformer that works for text and image classification, and applying quantum self-attention and quantum recurrent neural networks to natural language processing tasks. We also explore different encoding techniques and introduce positional encoding into quantum self-attention neural networks leading to improved accuracy and faster convergence in text and image classification experiments. This paper also performs a comparative analysis of classical self-attention models and their quantum counterparts, helping shed light on the differences in these models and their performance.

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