Neural Network Based Qubit Environment Characterization

Abstract

The exact microscopic structure of the environments that produces $1/f$ noise in superconducting qubits remains largely unknown, hindering our ability to have robust simulations and harness the noise. In this paper we show how it is possible to infer information about such an environment based on a single measurement of the qubit coherence, circumventing any need for separate spectroscopy experiments. Similarly to other spectroscopic techniques, the qubit is used as a probe which interacts with its environment. The complexity of the relationship between the observed qubit dynamics and the impurities in the environment makes this problem ideal for machine learning methods - more specifically neural networks. With our algorithm we are able to reconstruct the parameters of the most prominent impurities in the environment, as well as differentiate between different environment models, paving the way towards a better understanding of $1/f$ noise in superconducting circuits.

14 + 6 pages, 5 figures, 1 table

References in corpus (15)

• Charge insensitive qubit design derived from the Cooper pair box
• Quantum Non-Markovianity: Characterization, Quantification and Detection
• How to Enhance Dephasing Time in Superconducting Qubits
• Decoherence benchmarking of superconducting qubits
• Low-frequency noise as a source of dephasing of a qubit
• Decoherence in qubits due to low-frequency noise
• On the Computational Efficiency of Training Neural Networks
• Coherence oscillations in dephasing by non-Gaussian shot noise
• Decoherence of a qubit by non-Gaussian noise at an arbitrary working point
• Lindblad Tomography of a Superconducting Quantum Processor
• Full Counting Statistics: An elementary derivation of Levitov's formula
• Microscopic models for charge-noise-induced dephasing of solid-state qubits
• Ohmic and step noise from a single trapping center hybridized with a Fermi sea
• Learning Non-Markovian Quantum Noise from Moiré-Enhanced Swap Spectroscopy with Deep Evolutionary Algorithm
• Fitting quantum noise models to tomography data