# A walk through of time series analysis on quantum computers

arXiv: https://arxiv.org/abs/2205.00986 · HTML: https://mlsanity.com/paper/2205.00986/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 7 of 7)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks](https://mlsanity.com/paper/1901.06523/) (2019)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Continuous-variable quantum neural networks](https://mlsanity.com/paper/1806.06871/) (2018)
- [Machine learning & artificial intelligence in the quantum domain](https://mlsanity.com/paper/1709.02779/) (2017)
- [A review on distance based time series classification](https://mlsanity.com/paper/1806.04509/) (2018)
- [A Simple Quantum Neural Net with a Periodic Activation Function](https://mlsanity.com/paper/1804.07633/) (2018)

## Built on by (most cited first, 1 of 1)

- [Dimension reduction and redundancy removal through successive Schmidt decompositions](https://mlsanity.com/paper/2302.04801/) (2023)

## Abstract (arXiv)

Because of the rotational components on quantum circuits, some quantum neural networks based on variational circuits can be considered equivalent to the classical Fourier networks. In addition, they can be used to predict the Fourier coefficients of continuous functions. Time series data indicates a state of a variable in time. Since some time series data can be also considered as continuous functions, we can expect quantum machine learning models to do many data analysis tasks successfully on time series data. Therefore, it is important to investigate new quantum logics for temporal data processing and analyze intrinsic relationships of data on quantum computers. In this paper, we go through the quantum analogues of classical data preprocessing and forecasting with ARIMA models by using simple quantum operators requiring a few number of quantum gates. Then we discuss future directions and some of the tools/algorithms that can be used for temporal data analysis on quantum computers.