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Quantum deep neural networks for time series analysis

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Abstract

Quantum machine learning (QML) has emerged as a promising domain offering significant computational advantages over classical counterparts. In recent times, researchers have directed their attention towards this field. The objective of this paper is to provide a thorough overview of the advancements in quantum machine learning, encompassing the state-of-the-art approaches. The machine learning field is itself quite diverse. Diversity of QML is broadened due to the respective roles the quantum information processing and machine learning play in it. The study focuses on analysing the predictive efficacy of deep learning models on time series data. After experimental evaluation, we have chosen deep learning models that have better performance on time series data. The paper illustrates how different quantum techniques such as quantum encoding, optimization, etc., are used in quantum-enhanced models and provides a comprehensive review and an experimental analysis of three state-of-the-art quantum-enhanced models. Mental health is a serious global public health concern that has permeated modern civilization. So, seven time series data related to mental health conditions were collected from SWELL-KW, Wesad and psykose. Based on the experimental findings from the current dataset, it is evident that the quantum LSTM model exhibits superior predictive performance compared to other state-of-the-art approaches.

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    Anupama Padha & Anita Sahoo

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Padha, A., Sahoo, A. Quantum deep neural networks for time series analysis. Quantum Inf Process 23, 205 (2024). https://doi.org/10.1007/s11128-024-04404-y

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