December 7, 2020

Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer

  • Amara Katabarwa
  • Alejandro Perdomo-Ortiz

Sonika Johri, Manuel Rudolph


This paper presents the first experimental implementation of a machine learning algorithm on an ion-trap quantum computer capable of generating high-resolution images of handwritten digits. The algorithm combines unique capabilities of gate-based quantum devices with the scalability of classical deep learning techniques to potentially learn real-life datasets.


Generating high-quality data (e.g. images or video) is one of the most exciting and challenging frontiers in unsupervised machine learning. Utilizing quantum computers in such tasks to potentially enhance conventional machine learning algorithms has emerged as a promising application, but poses big challenges due to the limited number of qubits and the level of gate noise in available devices. In this work, we provide the first practical and experimental implementation of a quantum-classical generative algorithm capable of generating high-resolution images of handwritten digits with state-of-the-art gate-based quantum computers. In our quantum-assisted machine learning framework, we implement a quantum-circuit based generative model to learn and sample the prior distribution of a Generative Adversarial Network. We introduce a multi-basis technique which leverages the unique possibility of measuring quantum states in different bases, hence enhancing the expressibility of the prior distribution. We train this hybrid algorithm on an ion-trap device based on 171Yb+ ion qubits to generate high-quality images and quantitatively outperform comparable classical Generative Adversarial Networks trained on the popular MNIST data set for handwritten digits.


Amara Katabarwa
Zapata Author

Amara Katabarwa , Ph.D.

Manager, Hardware Integration
Alejandro Perdomo-Ortiz
Zapata Author

Alejandro Perdomo-Ortiz , Ph.D.

Research Director, Quantum AI/ML