Understanding the spectrum of noise acting on a qubit can yield valuable
information about its environment, and crucially underpins the optimization of
dynamical decoupling protocols that can mitigate such noise. However,
extracting accurate noise spectra from typical time-dynamics measurements on
qubits is intractable using standard methods. Here, we propose to address this
challenge using deep learning algorithms, leveraging the remarkable progress
made in the field of image recognition, natural language processing, and more
recently, structured data. We demonstrate a neural network based methodology
that allows for extraction of the noise spectrum associated with any qubit
surrounded by an arbitrary bath, with significantly greater accuracy than the
current methods of choice. The technique requires only a two-pulse echo decay
curve as input data and can further be extended either for constructing
customized optimal dynamical decoupling protocols or for obtaining critical
qubit attributes such as its proximity to the sample surface. Our results can
be applied to a wide range of qubit platforms, and provide a framework for
improving qubit performance with applications not only in quantum computing and
nanoscale sensing but also in material characterization techniques such as
magnetic resonance.Comment: Accepted for publication, 15 pages, 10 figure
