Expedited Noise Spectroscopy of Transmon Qubits

Abstract

Recent developments in the architecture of quantum computers have enabled their use in applications for various information-processing tasks. This information becomes unreliable primarily due to the erroneous implementation of control methods for state preparation and measurements and the qubit’s inability to store information for long periods in the presence of uncontrollable noise sources. Conventional noise spectroscopy protocols can characterize and model environmental noise but are usually resource-intensive and lengthy. Moreover, the underlying noise can vary over time, making noise profile extraction futile as this new information cannot be harnessed to improve quantum error correction or dynamical decoupling protocols. In this work, we address this challenge using a machine learning-based methodology to swiftly extract noise spectra of multiple qubits and demonstrate a possible noise mitigation strategy. The procedure involves implementing undemanding dynamical decoupling sequences to record coherence decays of the investigated qubits and then predict the underlying noise spectra with the help of a convolution neural network pre-trained on a synthetic dataset. The protocol is virtually hardware-agnostic. However, we validated its effectiveness using IBM’s superconducting qubits. We used these rapidly obtained yet accurate noise spectra to design bespoke dynamic decoupling sequences and perform time-dependent noise spectroscopy.