Resilience-runtime tradeoff relations for quantum algorithms

作     者：Pedro Garcia-Pintos, Luis O'Leary, Tom Biswas, Tanmoy Bringewatt, Jacob Cincio, Lukasz Brady, Lucas T. Liu, Yi-Kai 

作者机构：Los Alamos Natl Lab Theoret Div T4 Los Alamos NM 87545 USA Univ Oxford Clarendon Lab Parks Rd Oxford OX1 3PU England Univ Maryland Joint Ctr Quantum Informat & Comp Sci College Pk MD 20742 USA Univ Maryland Joint Quantum Inst College Pk MD 20742 USA Harvard Univ Dept Phys Cambridge MA 02138 USA NASA Ames Res Ctr Quantum Artificial Intelligence Lab Moffett Field CA 94035 USA Natl Inst Stand & Technol Appl & Computat Math Div Gaithersburg MD 20899 USA 

出 版 物：《REPORTS ON PROGRESS IN PHYSICS》 (Rep. Prog. Phys.)

年 卷 期：2025年第88卷第3期

页      面：037601-037601页

核心收录：

学科分类：07[理学] 0702[理学-物理学] 

基　　金：Defense Advanced Research Projects Agencyhttp://dx.doi.org/10.13039/100000185 U.S. Department of Energy, Office of Advanced Scientific Computing Research Fundamental Algorithmic Research for Quantum Computing (FAR-QC) Fundamental Algorithmic Research towards Quantum Utility [20230049DR] Laboratory Directed Research and Development (LDRD) program of Los Alamos National Laboratory (LANL) Beyond Moore's Law project of the Advanced Simulation and Computing Program at LANL [89233218CNA000001] National Nuclear Security Administration of the U.S. DOE [DE-SC0020312] DoE ASCR Accelerated Research in Quantum Computing program [DE-SC0019040, DE-SC0024220] DoE ASCR Quantum Testbed Pathfinder program [OMA-2120757] NSF QLCI AFOSR AFOSR MURI DARPA SAVaNT ADVENT Harvard Quantum Initiative U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers [IAA 8839, Annex 130] DARPA Quantum Benchmarking program National Institute of Standards and Technology EPSRC through an EPSRC iCASE studentship award U.S. Department of Energy (DOE) [DE-SC0024220] Funding Source: U.S. Department of Energy (DOE) 

主　　题：quantum algorithms noise resilience quantum computing quantum compilation 

摘      要：A leading approach to algorithm design aims to minimize the number of operations in an algorithm s compilation. One intuitively expects that reducing the number of operations may decrease the chance of errors. This paradigm is particularly prevalent in quantum computing, where gates are hard to implement and noise rapidly decreases a quantum computer s potential to outperform classical computers. Here, we find that minimizing the number of operations in a quantum algorithm can be counterproductive, leading to a noise sensitivity that induces errors when running the algorithm in non-ideal conditions. To show this, we develop a framework to characterize the resilience of an algorithm to perturbative noises (including coherent errors, dephasing, and depolarizing noise). Some compilations of an algorithm can be resilient against certain noise sources while being unstable against other noises. We condense these results into a tradeoff relation between an algorithm s number of operations and its noise resilience. We also show how this framework can be leveraged to identify compilations of an algorithm that are better suited to withstand certain noises.