TY - JOUR T1 - Quantum Approximate Optimization with a Trapped-Ion Quantum Simulator Y1 - 2019 A1 - G. Pagano A1 - A. Bapat A1 - P. Becker A1 - K. S. Collins A1 - A. De A1 - P. W. Hess A1 - H. B. Kaplan A1 - A. Kyprianidis A1 - W. L. Tan A1 - Christopher L. Baldwin A1 - L. T. Brady A1 - A. Deshpande A1 - F. Liu A1 - S. Jordan A1 - Alexey V. Gorshkov A1 - C. Monroe AB -

Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly solving exponentially hard problems, such as optimization and satisfiability. Here we report the first implementation of a shallow-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator to estimate the ground state energy of the transverse field Ising model with tunable long-range interactions. First, we exhaustively search the variational control parameters to approximate the ground state energy with up to 40 trapped-ion qubits. We then interface the quantum simulator with a classical algorithm to more efficiently find the optimal set of parameters that minimizes the resulting energy of the system. We finally sample from the full probability distribution of the QAOA output with single-shot and efficient measurements of every qubit. 

UR - https://arxiv.org/abs/1906.02700 ER -