Expedited Noise Spectroscopy of Transmon Qubits

Abstract

There has been tremendous progress in the physical realization of quantum computing hardware in recent times, bringing us closer than ever before to realizing the promise of quantum computing. However, noise continues to pose a crucial challenge when it comes to scaling up present day quantum processors. While decoherence limits the qubits ability to store information for long periods in the presence of uncontrollable noise sources, the erroneous implementation of control methods for state preparation and measurements leads to faulty implementations of quantum circuits. Conventional noise spectroscopy protocols can characterize and model environmental noise but are usually resource intensive and lengthy. Moreover, the underlying noise can vary in nature 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 quickly 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. While our protocol is virtually hardware-agnostic, we validate its effectiveness using superconducting qubits available on the IBM Quantum platform. We further use these rapidly obtained, yet accurate, noise spectra to design bespoke dynamic decoupling sequences and perform time-dependent noise spectroscopy.

First two authors have equal contribution to this work

References in corpus (13)

• Dynamical decoupling and noise spectroscopy with a superconducting flux qubit
• Fault-Tolerant Quantum Dynamical Decoupling
• How to Enhance Dephasing Time in Superconducting Qubits
• Experimental noise filtering by quantum control
• Multiqubit Spectroscopy of Gaussian Quantum Noise
• Hardware-efficient quantum error correction via concatenated bosonic qubits
• Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes
• Measurement-free fault-tolerant quantum error correction in near-term devices
• Analytic Filter Function Derivatives for Quantum Optimal Control
• Taming the Bloch-Redfield equation: Recovering an accurate Lindblad equation for general open quantum systems
• Noise-adapted recovery circuits for quantum error correction
• Noise-adapted Quantum Error Correction for Non-Markovian Noise
• Quantifying Quantum Steering with Limited Resources: A Semi-supervised Machine Learning Approach