一种用于分组编码配对哈密顿量的高效量子电路

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Science Pub Date : 2025-02-01 Epub Date: 2024-11-26 DOI:10.1016/j.jocs.2024.102480
Diyi Liu , Weijie Du , Lin Lin , James P. Vary , Chao Yang
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引用次数: 0

摘要

提出了一种有效的量子电路,用于对核物理中经常研究的配对哈密顿量进行分组编码。我们的分组编码方案不需要将产生算子和湮灭算子映射到泡利算子,也不需要将哈密顿算子表示为一元的线性组合。相反,我们将展示如何使用受控交换操作直接对哈密顿量进行编码。我们分析了分组编码电路的门复杂度,并表明它相对于表示与配对哈密顿量相关的量子态所需的量子比特数呈多项式缩放。我们还展示了如何将块编码电路与量子奇异值变换相结合,以构造一个有效的量子电路来近似配对哈密顿算子的态密度。所提出的技术可以扩展到编码更一般的二阶量化哈密顿量。
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An efficient quantum circuit for block encoding a pairing Hamiltonian
We present an efficient quantum circuit for block encoding a pairing Hamiltonian often studied in nuclear physics. Our block encoding scheme does not require mapping the creation and annihilation operators to the Pauli operators and representing the Hamiltonian as a linear combination of unitaries. Instead, we show how to encode the Hamiltonian directly using controlled swap operations. We analyze the gate complexity of the block encoding circuit and show that it scales polynomially with respect to the number of qubits required to represent a quantum state associated with the pairing Hamiltonian. We also show how the block encoding circuit can be combined with the quantum singular value transformation to construct an efficient quantum circuit for approximating the density of states of a pairing Hamiltonian. The techniques presented can be extended to encode more general second-quantized Hamiltonians.
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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