利用程序合成发现量子电路元件

IF 6.3 2区物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Machine Learning Science and Technology Pub Date : 2024-05-02 DOI:10.1088/2632-2153/ad4252

Leopoldo Sarra, Kevin Ellis and Florian Marquardt

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引用次数: 0

摘要

尽管该领域进展迅速，但发现利用量子计算的新方法仍然充满挑战：所有量子算法都必须手工设计，而量子力学又是出了名的反直觉。在本文中，我们研究了程序合成形式的人工智能如何帮助克服其中一些困难，展示了计算机如何通过经验逐步学习与量子电路合成相关的概念，并在未见过的任务中重复使用这些概念。我们特别关注将单元矩阵分解为量子电路，并展示了如何从一组基本门开始，自动发现有用的新复合门库，并利用它们分解日益复杂的单元。

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Discovering quantum circuit components with program synthesis

Despite rapid progress in the field, it is still challenging to discover new ways to leverage quantum computation: all quantum algorithms must be designed by hand, and quantum mechanics is notoriously counterintuitive. In this paper, we study how artificial intelligence, in the form of program synthesis, may help overcome some of these difficulties, by showing how a computer can incrementally learn concepts relevant to quantum circuit synthesis with experience, and reuse them in unseen tasks. In particular, we focus on the decomposition of unitary matrices into quantum circuits, and show how, starting from a set of elementary gates, we can automatically discover a library of useful new composite gates and use them to decompose increasingly complicated unitaries.

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来源期刊

Machine Learning Science and Technology Computer Science-Artificial Intelligence

CiteScore

9.10

自引率

4.40%

发文量

审稿时长

5 weeks

期刊介绍： Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.