Sandeep Mavadia

TL;DR: In this article, the authors use control theory and machine learning to predict the future evolution of a qubit's state, and deploy this information to suppress stochastic, semiclassical decoherence.

TL;DR: In a Penning trap the creation and manipulation of a wide variety of ion Coulomb crystals formed from small numbers of ions is demonstrated, which has potential applications for quantum simulation, quantum information processing and tests of fundamental physics models from quantum field theory to cosmology.

TL;DR: D discrete prolate spheroidal sequences are exploited to synthesize provably optimal narrowband controls ideally suited to spectral estimation of a qubit’s noisy environment, and classical multitaper techniques for spectral analysis can be ported to the quantum domain and combined with Bayesian estimation tools to experimentally reconstruct complex noise spectra.

TL;DR: Techniques from control theory and machine learning are used to predict the future evolution of a qubit's state; this information is deployed to suppress stochastic, semiclassical decoherence, even when access to measurements is limited.

TL;DR: This work quantitatively characterize the performance of both single- and multitaper Slepian estimation protocols by numerically reconstructing representative spectral densities, and demonstrates their advantage over dynamical-decoupling noise spectroscopy approaches in reducing bias from spectral leakage as well as in compensating for aliasing effects while maintaining a desired sampling resolution.