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

TL;DR

This paper demonstrates that qutrit-based quantum circuits can perform high-precision classification with fewer components than qubit systems, using optimized encoding schemes on superconducting transmon qutrits.

Contribution

It introduces a data-encoding optimization method for qutrit quantum circuits, enhancing classification accuracy and practicality on noisy hardware.

Findings

Qutrit classifiers achieve high accuracy with fewer components.

Optimized encoding schemes significantly improve classification performance.

Demonstration on superconducting transmon qutrits shows practical viability.

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.