Data compression for quantum machine learning

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

Abstract

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.

Citation

Dilip, R., Liu, Y. J., Smith, A., & Pollmann, F. (2022). Data compression for quantum machine learning. Physical Review Research, 4(4), Article 043007. https://doi.org/10.1103/PhysRevResearch.4.043007

• 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
• Electronic ISSN: 2643-1564
• Publisher: American Physical Society
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Issue: 4
• Article Number: 043007
• DOI: https://doi.org/10.1103/PhysRevResearch.4.043007
• Keywords: General Physics and Astronomy
• Public URL: https://nottingham-repository.worktribe.com/output/13173339
• Publisher URL: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.4.043007