Year: 2024
Type: preprint
Abstract: The variational quantum eigensolver (VQE), a type of variational quantum algorithm, is a hybrid quantum-classical algorithm to find the lowest-energy eigenstate of a particular Hamiltonian. We investigate ways to optimize the VQE solving process on multiple instances of the same problem, by observing the process on one instance of the problem to inform initialization for other processes. We aim to take advantage of the VQE solution process to obtain useful information while disregarding information which we can predict to not be very useful. In particular, we find that the solution process produces lots of data with very little new information. Therefore, we can safely disregard much of this repetitive information with little effect on the outcome of the solution process.
Source: arXiv (Cornell University)
Authors: Hutchings, Adam, Yarnot, Eric, Li, Xinpeng, Guan, Qiang, Xie, Ning, Xu, Shuai, Chaudhary, Vipin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: MRI: Acquisition of Artificial Intelligence Super Computer (AISC) for Accelerating Scientific Discovery, Collaborative Research: OAC Core: An Integrated Framework for Enabling Temporal-Reliable Quantum Learning on NISQ-era Devices, CAREER: The Research of Noise-Aware Scheduling for Noisy Intermediate-Scale Quantum Systems, Collaborative Research: PPoSS: Planning: Software Stack for Scalable Heterogeneous NISQ Cluster, Collaborative Research: PPoSS: Planning: Software Stack for Scalable Heterogeneous NISQ Cluster
OpenAlex ID: W4404340713