Physical Review A - April 2023

Physical Review A covering atomic, molecular, and optical physics and quantum information View Email Online   Volume 107, Issue 4 April 2023   View Issue   Advertisement Join us at CLEO 2023 in San Jose! Join the APS Division of Laser Science and representatives from the Physical Review Journals at booth #409 during CLEO 2023. Play the Prize Drop game and win an exciting prize! And don't miss our Meet the Physical Review Journal Editors event, to be held Tuesday, May 9, 1:15 p.m. – 1:45 p.m. on the Show Floor Theatre. Discuss your latest submission or learn how to become a referee.     Advertisement Attend APS DAMOP Meeting 2023 Registration is open for APS's Division of Atomic, Molecular and Optical Physics Meeting, to be held June 5-9 in Spokane, WA. Witness groundbreaking physics research, attend engaging events, and network with colleagues. Early bird registration rates end May 5. Register today.     Not an APS member? Join today to start connecting with a community of more than 50,000 physicists.   Highlights Editors' Suggestion Increased success probability in Hardy's nonlocality: Theory and demonstration Duc Minh Tran, Van-Duy Nguyen, Le Bin Ho, and Hung Q. Nguyen Phys. Rev. A 107, 042210 (2023) – Published 14 April 2023 The authors introduce a quantum nonlocal scheme for n-particle systems using a theoretical model and a quantum simulation. They find a particular version of Hardy's paradox that yields a very high nonlocal probability that approaches 15.6% as the system size n grows.     Editors' Suggestion Scrambling of algebras in open quantum systems Faidon Andreadakis, Namit Anand, and Paolo Zanardi Phys. Rev. A 107, 042217 (2023) – Published 21 April 2023 Quantum information scrambling describes how local information disperses into a many-body quantum system. Here, the authors introduce an algebraic formalism to better understand these dynamics and demonstrate how their approach allows them to disentangle environmental decoherence from scrambling.     Editors' Suggestion Superpolynomial quantum-classical separation for density modeling Niklas Pirnay, Ryan Sweke, Jens Eisert, and Jean-Pierre Seifert Phys. Rev. A 107, 042416 (2023) – Published 13 April 2023 Density modeling is a machine learning task with the goal of learning an underlying probability distribution from samples. Here the authors show that, when solving certain density modeling problems, algorithms that use fault-tolerant quantum computers have an advantage over classical ones.     Editors' Suggestion Ability of error correlations to improve the performance of variational quantum algorithms Joris Kattemölle and Guido Burkard Phys. Rev. A 107, 042426 (2023) – Published 21 April 2023 Recent experiments indicate that uncorrelated noise models may not be enough to explain the physical behavior of quantum devices, and that correlated error must be taken into account. Here, the authors use a simple model to numerically study variational quantum algorithms with error that is correlated either in time or in space. They find that correlated error can actually improve algorithm performance, due to the fact that there are simply more ways for uncorrelated errors to occur in a system.     Editors' Suggestion Sensitivity of quantum gate fidelity to laser phase and intensity noise X. Jiang, J. Scott, Mark Friesen, and M. Saffman Phys. Rev. A 107, 042611 (2023) – Published 14 April 2023 The authors analyze gate errors for neutral-atom qubits, quantifying how sensitive gate fidelities are to laser noise for both single- and two-photon gate operation. They consider relevant experimental issues like the noise induced by the finite bandwidth of feedback circuits.     Editors' Suggestion Quantum feature maps for graph machine learning on a neutral atom quantum processor Boris Albrecht, Constantin Dalyac, Lucas Leclerc, Luis Ortiz-Gutiérrez, Slimane Thabet, Mauro D'Arcangelo, Julia R. K. Cline, Vincent E. Elfving, Lucas Lassablière, Henrique Silvério, Bruno Ximenez, Louis-Paul Henry, Adrien Signoles, and Loïc Henriet Phys. Rev. A 107, 042615 (2023) – Published 19 April 2023 Using a Rydberg quantum processor with up to 32 qubits, the authors implement machine learning tasks on data structured into graphs and show that their platform can distinguish two different graph connectivities. To illustrate the potential of such a method, they show that it can classify the toxicity of a given molecule based on a real-world biochemistry dataset.     Editors' Suggestion Doppler-free spectroscopy of an atomic beam probed in traveling-wave fields Jin-Lu Wen (温金录), Jia-Dong Tang (唐家栋), Jun-Feng Dong (董俊峰), Xiao-Jiao Du (杜小娇), Shui-Ming Hu (胡水明), and Y. R. Sun (孙羽) Phys. Rev. A 107, 042811 (2023) – Published 18 April 2023 The authors propose and demonstrate a method for precision spectroscopy of atoms in a beam using two sequential rather than simultaneous laser pulses, thereby overcoming systematic challenges associated with standing-wave beams that plague other methods. Their results show that this method can effectively suppress the first-order Doppler effect and reduce systematic uncertainty.     Editors' Suggestion Direct imaging of the hyperfine depolarization in electronically excited NO molecules Dai Ikeda, Kenta Mizuse, and Yasuhiro Ohshima Phys. Rev. A 107, 043116 (2023) – Published 17 April 2023 Using pump-probe imaging, the authors study the depolarization dynamics of electronically excited NO molecules by tracking the angular distribution of the molecular axis. They show an evolution from alignment to antialignment, a manifestation of rotational angular momentum depolarization by the hyperfine interaction with the N nuclear spin.       American Physical Society: 1 Physics Ellipse, College Park, MD 20740 ©2023 American Physical Society | All rights reserved   You are receiving this email because you are subscribed to Table of Content alerts. If you no longer wish to receive these emails, please update your preferences.