Resource Estimation of Quantum Multiplication Algorithms

Ethan R. Hansen, Sanskriti Joshi, Hannah Rarick · 2023 · IEEE QCE 2023, 199–202

| 编号 QM005 | 主题 模运算与乘法 | 链接 arXiv:2402.01891 |

摘要

摘要保留原文（英文）

摘要取自 arXiv / 出版方元数据，未作翻译，以保证与原文一致。

As quantum computers progress towards a larger scale, it is imperative that the “top” of the computing-technology stack is improved. This project investigates the quantum resources required to compute primitive arithmetic algorithms, particularly multiplication. By using various quantum resource estimators, like Microsoft’s Azure Quantum Resource Estimator, one can determine the resources required for numerous quantum algorithms [5]. In this paper, we will provide a comprehensive resource analysis of numerous quantum multiplication algorithms such as Karatsuba, schoolbook, and windowed arithmetic for different qubit platforms (trapped ion, superconducting, and Majorana) using the new Azure Quantum Resource Estimator.

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