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作者机构:Goldman Sachs & Co New York NY 10282 USA AWS Ctr Quantum Comp Pasadena CA 91125 USA CALTECH Pasadena CA 91125 USA Amazon Quantum Solut Lab Seattle WA 98170 USA Imperial Coll London Dept Comp London SW7 2BX England Rhein Westfal TH Aachen Inst Quantum Informat D-52062 Aachen Germany
出 版 物:《IEEE TRANSACTIONS ON QUANTUM ENGINEERING》 (IEEE. Trans. Quantum. Eng.)
年 卷 期:2022年第3卷第1期
页 面:1页
核心收录:
主 题:Qubit Logic gates Costs Signal processing algorithms Registers Quantum circuit Integrated circuit modeling Block encoding quantum circuit synthesis quantum circuits quantum computing quantum random access memory (QRAM) quantum resources
摘 要:We provide modular circuit-level implementations and resource estimates for several methods of block-encoding a dense NxN matrix of classical data to precision & varepsilon;the minimal-depth method achieves a T-depth of O(log(N/& varepsilon;)), while the minimal-count method achieves a T-count of O(Nlog(1/& varepsilon;)). We examine resource tradeoffs between the different approaches, and we explore implementations of two separate models of quantum random access memory (QRAM). As part of this analysis, we provide a novel state preparation routine with T-depth O(log(N/& varepsilon;)), improving on previous constructions with scaling O(log(2)(N/& varepsilon;)). Our results go beyond simple query complexity and provide a clear picture into the resource costs when large amounts of classical data are assumed to be accessible to quantum algorithms.