Design Considerations for Denoising Quantum Time Series Autoencoder

This paper explains the main design decisions in the development of variational quantum time series models and denoising quantum time series autoencoders. Although we cover a specific type of quantum model, the problems and solutions are generally applicable to many other methods of time series analysis. The paper highlights the benefits and weaknesses of alternative approaches to designing a model, its data encoding and decoding, ansatz and its parameters, measurements and their interpretation, and quantum model optimization. Practical issues in training and execution of quantum time series models on simulators, including those that are CPU and GPU based, as well as their deployment on quantum machines, are also explored. All experimental results are evaluated, and the final recommendations are provided for the developers of quantum models focused on time series analysis.