Quantum Engineering of Qudits with Interpretable Machine Learning

TL;DR

This paper introduces a machine learning framework for controlling and understanding noise in high-dimensional quantum systems (qudits), improving fidelity and interpretability in quantum control under realistic conditions.

Contribution

It extends machine learning control methods to qudits of arbitrary dimension and introduces an interpretable noise modeling approach for complex quantum dynamics.

Findings

Achieved high-fidelity global and subspace gates on qudits.

Developed an interpretable noise model for complex quantum environments.

Demonstrated scalable control techniques for NISQ and future quantum devices.

Abstract

Higher-dimensional quantum systems (qudits) offer advantages in information encoding, error resilience, and compact gate implementations, and naturally arise in platforms such as superconducting and solid-state systems. However, realistic conditions such as non-Markovian noise, non-ideal pulses, and beyond rotating wave approximation (RWA) dynamics, pose significant challenges for controlling and characterizing qudits. In this work, we present a machine-learning-based graybox framework for the control and noise characterization of qudits with arbitrary dimension, extending recent methods developed for single-qubit systems. Additionally, we introduce a local analytic expansion that enables interpretable modelling of the noise dynamics, providing a structured and efficient way to simulate system behaviour and compare different noise models. This interpretability feature allows us to to understand the mechanisms underlying successful control strategies; and opens the way for developing methods for distinguishing noise sources with similar effects. We demonstrate high-fidelity implementations of both global unitary operations as well as two-level subspace gates. Our work establishes a foundation for scalable and interpretable quantum control techniques applicable to both NISQ devices and finite-dimensional quantum systems, enhancing the performance of next-generation quantum technologies.