Encoding optimization for quantum machine learning demonstrated on a superconducting transmon qutrit
Abstract
Qutrits, three-level quantum systems, have the advantage of potentially requiring fewer components than the typically used two-level qubits to construct equivalent quantum circuits. This work investigates the potential of qutrit parametric 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. 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 optimized encoding method on superconducting transmon qutrits, demonstrating the practicality of the proposed method on noisy hardware. Our work demonstrates high-precision ternary classification using fewer circuit elements, establishing qutrit parametric quantum circuits as a viable and efficient tool for quantum machine learning applications.
Cite
@article{arxiv.2309.13036,
title = {Encoding optimization for quantum machine learning demonstrated on a superconducting transmon qutrit},
author = {Shuxiang Cao and Weixi Zhang and Jules Tilly and Abhishek Agarwal and Mustafa Bakr and Giulio Campanaro and Simone D Fasciati and James Wills and Boris Shteynas and Vivek Chidambaram and Peter Leek and Ivan Rungger},
journal= {arXiv preprint arXiv:2309.13036},
year = {2023}
}