Seon Kim

TL;DR: The application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA) is demonstrated and an approximation ratio is obtained that is independent of problem size and for the first time, that performance increases with circuit depth.

TL;DR: In this paper, a series of quantum simulations of chemistry were performed, the largest of which involved a dozen qubits, 78 two-qubit gates, and 114 one qubit gates.

TL;DR: In this paper, the Google Sycamore superconducting qubit quantum processor is applied to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA) to solve MaxCut and Sherrington-Kirkpatrick model.

TL;DR: In this article, the authors describe the quantum information propagation process in quantum systems, which can spread initially localized quantum information into the exponentially many degrees of freedom of the entire system, known as quantum quantum information dissemination.

TL;DR: A reset protocol is reported that returns a qubit to the ground state from all relevant higher level states and finds lower rates of logical errors and an improved scaling and stability of error suppression with increasing qubit number.