Predicting molecular vibronic spectra using time-domain analog quantum simulation
Ryan J. MacDonell, Tomas Navickas, Tim F. Wohlers-Reichel, Christophe H. Valahu, Arjun D. Rao, Maverick J. Millican, Michael A. Currington, Michael J. Biercuk, … 展开作者
Ryan J. MacDonell, Tomas Navickas, Tim F. Wohlers-Reichel, Christophe H. Valahu, Arjun D. Rao, Maverick J. Millican, Michael A. Currington, Michael J. Biercuk, Ting Rei Tan, Cornelius Hempel, Ivan Kassal
Spectroscopy is one of the most accurate probes of the molecular world. However, predicting molecular spectra accurately is computationally difficult because of the presence of entanglement between electronic and nuclear degrees of freedom. Although quantum computers promise to reduce this computational cost, existing quantum approaches rely on combining signals from individual eigenstates, an approach that is difficult to scale because the number of eigenstates grows exponentially with molecule size. Here, we introduce a method for scalable analog quantum simulation of molecular spectroscopy, by performing simulations in the time domain. Our approach can treat more complicated molecular models than previous ones, requires fewer approximations, and can be extended to open quantum systems with minimal overhead. We present a direct mapping of the underlying problem of time-domain simulation of molecular spectra to the degrees of freedom and control fields available in a trapped-ion quantum simulator. We experimentally demonstrate our algorithm on a trapped-ion device, exploiting both intrinsic electronic and motional degrees of freedom, showing excellent quantitative agreement for a single-mode vibronic photoelectron spectrum of SO$_2$.
Simulating Electron Transfer on Noisy Quantum Computers
Institute of Engineering Thermodynamics, German Aerospace Center (DLR)（德国航空航天中心工程热力学研究所）；Helmholtz Institute Ulm（乌尔姆亥姆霍兹研究所）；Institute of Software Technology, German Aerospace Center (DLR)（德国航空航天中心软件技术研究所）；HQS Quantum Simulations GmbH（HQS量子模拟有限公司）；Department of Physics, Ulm University（乌尔姆大学物理系）
Predicting quantum advantage by quantum walk with convolutional neural networks
ITMO University（ITMO大学）；Valiev Institute of Physics and Technology, Russian Academy of Sciences（俄罗斯科学院瓦维洛夫物理与技术研究所）；Moscow Institute of Physics and Technology（莫斯科物理与技术学院）
