量子随机存取存储器的基本因果界限

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Yunfei Wang, Yuri Alexeev, Liang Jiang, Frederic T. Chong, Junyu Liu
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引用次数: 0

摘要

我们的研究评估了量子随机存取存储器(QRAM)在量子物理学和相对论原理下的局限性和潜力。QRAM对于推进线性代数和机器学习等领域的量子算法至关重要,据称它能以({{mathcal{O}}}}(\log N))电路深度有效管理大型数据集。然而,当考虑到量子比特局部交互的相对论约束时,它的可扩展性就受到了质疑。利用相对论量子场论和列布-罗宾逊约束,我们深入研究了 QRAM 基于因果关系的限制。我们的研究在混合量子声学系统中引入了一个可行的 QRAM 模型,在实际操作参数范围内,该模型能够在不同维度上支持大量逻辑量子比特,一维可达 ~107 个,二维可达 ~1015 至 ~1020 个,三维可达 ~1024 个。这项分析表明,相对论因果关系原理可能会普遍影响量子计算硬件,这突出表明需要创新的量子存储器解决方案来克服这些基础性障碍,从而增强数据科学领域未来的量子计算努力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Fundamental causal bounds of quantum random access memories

Fundamental causal bounds of quantum random access memories

Our study evaluates the limitations and potentials of Quantum Random Access Memory (QRAM) within the principles of quantum physics and relativity. QRAM is crucial for advancing quantum algorithms in fields like linear algebra and machine learning, purported to efficiently manage large data sets with \({{{\mathcal{O}}}}(\log N)\) circuit depth. However, its scalability is questioned when considering the relativistic constraints on qubits interacting locally. Utilizing relativistic quantum field theory and Lieb–Robinson bounds, we delve into the causality-based limits of QRAM. Our investigation introduces a feasible QRAM model in hybrid quantum acoustic systems, capable of supporting a significant number of logical qubits across different dimensions-up to ~107 in 1D, ~1015 to ~1020 in 2D, and ~1024 in 3D, within practical operation parameters. This analysis suggests that relativistic causality principles could universally influence quantum computing hardware, underscoring the need for innovative quantum memory solutions to navigate these foundational barriers, thereby enhancing future quantum computing endeavors in data science.

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来源期刊
npj Quantum Information
npj Quantum Information Computer Science-Computer Science (miscellaneous)
CiteScore
13.70
自引率
3.90%
发文量
130
审稿时长
29 weeks
期刊介绍: The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.
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