@article {2520, title = {Observation of Domain Wall Confinement and Dynamics in a Quantum Simulator}, year = {2019}, month = {12/23/2019}, abstract = {

Confinement is a ubiquitous mechanism in nature, whereby particles feel an attractive force that increases without bound as they separate. A prominent example is color confinement in particle physics, in which baryons and mesons are produced by quark confinement. Analogously, confinement can also occur in low-energy quantum many-body systems when elementary excitations are confined into bound quasiparticles. Here, we report the first observation of magnetic domain wall confinement in interacting spin chains with a trapped-ion quantum simulator. By measuring how correlations spread, we show that confinement can dramatically suppress information propagation and thermalization in such many-body systems. We are able to quantitatively determine the excitation energy of domain wall bound states from non-equilibrium quench dynamics. Furthermore, we study the number of domain wall excitations created for different quench parameters, in a regime that is difficult to model with classical computers. This work demonstrates the capability of quantum simulators for investigating exotic high-energy physics phenomena, such as quark collision and string breaking

}, url = {https://arxiv.org/abs/1912.11117}, author = {W. L. Tan and P. Becker and F. Liu and G. Pagano and K. S. Collins and A. De and L. Feng and H. B. Kaplan and A. Kyprianidis and R. Lundgren and W. Morong and S. Whitsitt and Alexey V. Gorshkov and C. Monroe} } @article {2410, title = {Quantum Approximate Optimization with a Trapped-Ion Quantum Simulator}, year = {2019}, month = {06/06/2019}, abstract = {

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.\ 

}, url = {https://arxiv.org/abs/1906.02700}, author = {G. Pagano and A. Bapat and P. Becker and K. S. Collins and A. De and P. W. Hess and H. B. Kaplan and A. Kyprianidis and W. L. Tan and Christopher L. Baldwin and L. T. Brady and A. Deshpande and F. Liu and S. Jordan and Alexey V. Gorshkov and C. Monroe} } @article {2289, title = {Cryogenic Trapped-Ion System for Large Scale Quantum Simulation}, year = {2018}, abstract = {

We present a cryogenic ion trapping system designed for large scale quantum simulation of spin models. Our apparatus is based on a segmented-blade ion trap enclosed in a 4 K cryostat, which enables us to routinely trap over 100 171Yb+ ions in a linear configuration for hours due to a low background gas pressure from differential cryo-pumping. We characterize the cryogenic vacuum by using trapped ion crystals as a pressure gauge, measuring both inelastic and elastic collision rates with the molecular background gas. We demonstrate nearly equidistant ion spacing for chains of up to 44 ions using anharmonic axial potentials. This reliable production and lifetime enhancement of large linear ion chains will enable quantum simulation of spin models that are intractable with classical computer modelling.

}, url = {https://arxiv.org/abs/1802.03118}, author = {G. Pagano and P. W. Hess and H. B. Kaplan and W. L. Tan and P. Richerme and P. Becker and A. Kyprianidis and J. Zhang and E. Birckelbaw and M. R. Hernandez and Y. Wu and C. Monroe} }