Model-based Optimization of Superconducting Qubit Readout

TL;DR

This paper presents a model-based optimization method for superconducting qubit readout that significantly reduces measurement errors and side-effects, enabling scalable high-fidelity quantum measurements for larger quantum systems.

Contribution

The authors develop a model-based readout optimization technique that achieves low error rates and minimal side-effects, scalable to hundreds of qubits.

Findings

1. Achieved 1.5% measurement error per qubit for 17 qubits.

2. End-to-end measurement duration of 500ns with minimal residual effects.

3. Suppressed measurement-induced state transitions limited only by natural heating.

Abstract

Measurement is an essential component of quantum algorithms, and for superconducting qubits it is often the most error prone. Here, we demonstrate model-based readout optimization achieving low measurement errors while avoiding detrimental side-effects. For simultaneous and mid-circuit measurements across 17 qubits, we observe 1.5% error per qubit with a 500ns end-to-end duration and minimal excess reset error from residual resonator photons. We also suppress measurement-induced state transitions achieving a leakage rate limited by natural heating. This technique can scale to hundreds of qubits and be used to enhance the performance of error-correcting codes and near-term applications.