Skip to main content

Research Repository

Advanced Search

Data compression for quantum machine learning

Dilip, Rohit; Liu, Yu Jie; Smith, Adam; Pollmann, Frank


Rohit Dilip

Yu Jie Liu

Profile Image

Assistant Professor

Frank Pollmann


The advent of noisy-intermediate scale quantum computers has introduced the exciting possibility of achieving quantum speedups in machine learning tasks. These devices, however, are composed of a small number of qubits and can faithfully run only short circuits. This puts many proposed approaches for quantum machine learning beyond currently available devices. We address the problem of compressing classical data into efficient representations on quantum devices. Our proposed methods allow both the required number of qubits and depth of the quantum circuit to be tuned. We achieve this by using a correspondence between matrix-product states and quantum circuits and further propose a hardware-efficient quantum circuit approach, which we benchmark on the Fashion-MNIST dataset. Finally, we demonstrate that a quantum circuit-based classifier can achieve competitive accuracy with current tensor learning methods using only 11 qubits.


Dilip, R., Liu, Y. J., Smith, A., & Pollmann, F. (2022). Data compression for quantum machine learning. Physical Review Research, 4(4), Article 043007.

Journal Article Type Article
Acceptance Date Aug 24, 2022
Online Publication Date Oct 4, 2022
Publication Date Oct 1, 2022
Deposit Date Nov 10, 2022
Publicly Available Date Nov 10, 2022
Journal Physical Review Research
Publisher American Physical Society
Peer Reviewed Peer Reviewed
Volume 4
Issue 4
Article Number 043007
Keywords General Physics and Astronomy
Public URL
Publisher URL


You might also like

Downloadable Citations