A quantum generative model for multi-dimensional time series using Hamiltonian learning

TL;DR

This paper introduces a quantum generative model leveraging Hamiltonian learning to generate multi-dimensional time series data, effectively capturing complex temporal correlations that classical methods struggle with.

Contribution

It proposes a novel quantum machine learning approach to model and generate time series data by learning quantum processes, demonstrating its effectiveness on an actual quantum device.

Findings

Successfully generated synthetic time series with preserved temporal dynamics

Captured complex features of the original time series

Demonstrated the algorithm on an 11-qubit quantum computer

Abstract

Synthetic data generation has proven to be a promising solution for addressing data availability issues in various domains. Even more challenging is the generation of synthetic time series data, where one has to preserve temporal dynamics, i.e., the generated time series must respect the original relationships between variables across time. Recently proposed techniques such as generative adversarial networks (GANs) and quantum-GANs lack the ability to attend to the time series specific temporal correlations adequately. We propose using the inherent nature of quantum computers to simulate quantum dynamics as a technique to encode such features. We start by assuming that a given time series can be generated by a quantum process, after which we proceed to learn that quantum process using quantum machine learning. We then use the learned model to generate out-of-sample time series and show that it captures unique and complex features of the learned time series. We also study the class of time series that can be modeled using this technique. Finally, we experimentally demonstrate the proposed algorithm on an 11-qubit trapped-ion quantum machine.