Efficient modeling of superconducting quantum circuits with tensor networks

TL;DR

This paper presents a tensor network toolbox for efficiently computing low-energy excitations in large superconducting quantum circuits, enabling detailed analysis of coherence times and noise effects in complex systems.

Contribution

The authors develop a novel tensor network algorithm that accurately models large-scale superconducting circuits, surpassing previous computational limitations.

Findings

Successfully benchmarked on fluxonium qubit with over 100 junctions

Computed coherence times considering charge noise and quantum phase slips

Demonstrated applicability to various circuit-QED systems

Abstract

We introduce an efficient tensor network toolbox to compute the low-energy excitations of large-scale superconducting quantum circuits up to a desired accuracy. We benchmark this algorithm on the fluxonium qubit, a superconducting quantum circuit based on a Josephson junction array with over a hundred junctions. As an example of the possibilities offered by this numerical tool, we compute the pure-dephasing coherence time of the fluxonium qubit due to charge noise and coherent quantum phase slips, taking into account the array degrees of freedom corresponding to a Hilbert space as large as$~15^{180}$. Our algorithm is applicable to the wide variety of circuit-QED systems and may be a useful tool for scaling up superconducting-qubit technologies.