01764nas a2200289 4500008004100000245007400041210006900115260001500184520095900199100001501158700001401173700001501187700002001202700001101222700001701233700001901250700002001269700001601289700002901305700001801334700001801352700001201370700001501382700002501397700001501422856003701437 2019 eng d00aQuantum Approximate Optimization with a Trapped-Ion Quantum Simulator0 aQuantum Approximate Optimization with a TrappedIon Quantum Simul c06/06/20193 a
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.
1 aPagano, G.1 aBapat, A.1 aBecker, P.1 aCollins, K., S.1 aDe, A.1 aHess, P., W.1 aKaplan, H., B.1 aKyprianidis, A.1 aTan, W., L.1 aBaldwin, Christopher, L.1 aBrady, L., T.1 aDeshpande, A.1 aLiu, F.1 aJordan, S.1 aGorshkov, Alexey, V.1 aMonroe, C. uhttps://arxiv.org/abs/1906.02700