Cao, S; Zhang, W; Tilly, J; Agarwal, A; Bakr, M; Campanaro, G; D Fasciati, S; Wills, J; Shteynas, B; Chidambaram, V; Leek, P; Rungger, I (2024) Encoding optimization for quantum machine learning demonstrated on a superconducting transmon qutrit. Quantum Science and Technology, 9 (4). 045037. ISSN 2058-9565

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Abstract

A qutrit represents a three-level quantum system, so that one qutrit can encode more information than a qubit, which corresponds to a two-level quantum system. This work investigates the potential of qutrit circuits in machine learning classification applications. We propose and evaluate different data-encoding schemes for qutrits, and find that the classification accuracy varies significantly depending on the used encoding. We therefore propose a training method for encoding optimization that allows to consistently achieve high classification accuracy, and show that it can also improve the performance within a data re-uploading approach. Our theoretical analysis and numerical simulations indicate that the qutrit classifier can achieve high classification accuracy using fewer components than a comparable qubit system. We showcase the qutrit classification using the encoding optimization method on a superconducting transmon qutrit, demonstrating the practicality of the proposed method on noisy hardware. Our work demonstrates high-precision ternary classification using fewer circuit elements, establishing qutrit quantum circuits as a viable and efficient tool for quantum machine learning applications.

Item Type:	Article
Subjects:	Quantum Phenomena > Quantum Information Processing and Communication
Divisions:	Quantum Technologies
Identification number/DOI:	10.1088/2058-9565/ad7315
Last Modified:	09 Feb 2026 14:22
URI:	https://eprintspublications.npl.co.uk/id/eprint/10264