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arXiv · 2207.07982

One-Step Time Series Forecasting Using Variational Quantum Circuits

Payal Kaushik·Sayantan Pramanik·M Girish Chandra·C V Sridhar
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Abstract

Time series forecasting has always been a thought-provoking topic in the field of machine learning. Machine learning scientists define a time series as a set of observations recorded over consistent time steps. And, time series forecasting is a way of analyzing the data and finding how variables change over time and hence, predicting the future value. Time is of great essence in this forecasting as it shows how the data coordinates over the dataset and the final result. It also requires a large dataset to ascertain the regularity and reliability. Quantum computers may prove to be a better option for perceiving the trends in the time series by exploiting quantum mechanical phenomena like superposition and entanglement. Here, we consider one-step time series forecasting using variational quantum circuits, and record observations for different datasets.

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Payal Kaushik, Sayantan Pramanik, M Girish Chandra, C V Sridhar. 2022-07-16. One-Step Time Series Forecasting Using Variational Quantum Circuits. https://arxiv.org/abs/2207.07982

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