A short review on qudit quantum machine learning

TL;DR

This review explores how qudit quantum systems, with their larger state space, enhance quantum machine learning by enabling more efficient, expressive algorithms and discussing recent experimental progress and software tools.

Contribution

It provides a comprehensive overview of qudit-based quantum machine learning, highlighting recent experimental demonstrations and discussing current challenges and software ecosystem development.

Findings

Qudit systems can reduce circuit depth and parameter counts.

Recent experiments demonstrate practical advantages of qudits.

The software ecosystem for qudits is rapidly evolving.

Abstract

As quantum devices scale toward practical machine learning applications, the binary qubit paradigm faces expressivity and resource efficiency limitations. Multi-level quantum systems, or qudits, offer a promising alternative by harnessing a larger Hilbert space, enabling richer data embeddings, more compact variational circuits, and support for multi-valued problem structures. In this work, we review the role of qudits in quantum machine learning techniques, mainly variational quantum algorithms and quantum neural networks. Drawing on recent experimental demonstrations, including high-level superconducting transmons, qutrit-based combinatorial optimization, and single-qudit classifiers, we highlight how qudit architectures can reduce circuit depth and parameter counts while maintaining competitive fidelity. We further assess the evolving software ecosystem, from specialized simulators and differentiable-programming libraries to extensions of mainstream frameworks. We also identify key challenges in control complexity, noise management, and tooling maturity.