Learning Lindblad Dynamics of a Superconducting Quantum Processor

TL;DR

LIMINAL is a data-driven framework that efficiently constructs minimal Lindblad models for quantum processors by testing assumptions against time-resolved data, improving understanding and calibration.

Contribution

It introduces a method to select adequate physical models for quantum processors using likelihood-ratio tests on nested Lindblad models.

Findings

Identified a minimal idling model with specific Hamiltonian and dissipation terms for a five-qubit superconducting processor.

Successfully reconstructed driven single-qubit Hamiltonians without assuming an analytic pulse model.

Demonstrated the framework's applicability to hidden-qubit extensions and coupler-mediated dynamics.

Abstract

Accurate models of quantum processors are essential for understanding, calibrating, and improving their performance. In practice, model construction must balance physical detail against the experimental and computational effort required to reliably learn parameters. Compact descriptions therefore often rely on assumptions about which interactions, noise processes, or hidden degrees of freedom are relevant. Here we introduce LIMINAL, a data-driven framework for testing such assumptions and selecting minimal adequate Lindblad models. LIMINAL fits nested candidate models to time-resolved tomographic data and uses likelihood-ratio tests to decide when added physical mechanisms are warranted. We apply LIMINAL to a five-qubit superconducting processor, identifying an idling model with three-local Hamiltonian terms and two-local dissipation, while finding no support for three-local dissipation. We further apply it to recover driven single-qubit Hamiltonians, reconstruct a shaped-pulse Hamiltonian without assuming an analytic pulse model, and test hidden-qubit extensions in coupler-mediated dynamics, demonstrating the applicability of the framework for a wide range of tasks.