Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator.

ABSTRACT: Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly improving performance for solving exponentially hard problems, such as optimization and satisfiability. Here, we report the implementation of a low-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator. We estimate the ground-state energy of the Transverse Field Ising Model with long-range interactions with tunable range, and we optimize the corresponding combinatorial classical problem by sampling the QAOA output with high-fidelity, single-shot, individual qubit measurements. We execute the algorithm with both an exhaustive search and closed-loop optimization of the variational parameters, ap

SUBMITTER: Pagano G 

PROVIDER: S-EPMC7568299 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature