Encoding optimization for quantum machine learning demonstrated on a superconducting transmon qutrit

Journal article

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.

Authors: Cao, S; Zhang, W; Tilly, J; Agarwal, A; Bakr, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IOP Publishing
• Journal: Quantum Science and Technology
• Volume: 9
• Issue: 4
• Article number: 045037
• Publication date: 2024-09-06
• Acceptance date: 2024-08-23
• DOI: 10.1088/2058-9565/ad7315
• EISSN: 2058-9565
• Language: English
• Keywords: quantum machine learning; qutrits; FFR; quantum encoding