Akel Hashim

TL;DR: This work demonstrates significant performance gains under randomized compiling for the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor, and accurately predicts algorithm performance using experimentally-measured error rates.

TL;DR: Rudinger et al. as mentioned in this paper used gate set tomography to identify and characterize crosstalk errors in quantum information processors and demonstrated their methods on a two-qubit trapped-ion processor and a twoqubit subsystem of a superconducting transmon processor.

TL;DR: The phase contrast imaging instrument at the Matter in Extreme Conditions (MEC) endstation of the Linac Coherent Light Source as mentioned in this paper can image phenomena with a spatial resolution of a few hundreds of nanometers and reveal the atomic structure through X-ray diffraction, with a temporal resolution better than 100 fs.

TL;DR: In this article, the authors incorporate features of quantum mechanics in the design of their ML approach to characterize the dynamics of a quantum device and learn device parameters, which outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data obtained from continuous weak measurement of a driven superconducting transmon qubit.