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arXiv 2502.02405quant-ph
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利用机器学习解锁全局量子门的力量

Unlocking the power of global quantum gates with machine learning

Vinit Singh, Bin Yan

AI总结:

本研究提出利用变分方法将全局量子门用于参数化电路拟设,解决编译复杂性问题,并在海森堡模型和环面码基态制备中展示其表达能力和实际优势。

摘要:

在传统的基于电路的量子计算架构中,标准门集包括任意单量子比特旋转和双量子比特纠缠门。这一选择并不总是与某些硬件中可用的原生操作一致,在这些硬件中,自然的纠缠门不限于两个量子比特,而是可以同时作用于多个甚至所有量子比特。然而,利用全局量子操作的能力来实现算法极具挑战性,因为直接将局部门序列编译为全局门通常会产生比原始电路更复杂的量子电路。在这里,我们通过变分方法规避了这一困难。具体而言,我们研究了由有限数量的全局门和单量子比特酉层组成的参数化电路拟设。我们证明了这些拟设的表达能力,并将其应用于海森堡模型和环面码哈密顿量的基态制备问题,突显了它们提供实际优势的潜力。

英文摘要:

In conventional circuit-based quantum computing architectures, the standard gate set includes arbitrary single-qubit rotations and two-qubit entangling gates. This choice is not always aligned with the native operations available in certain hardware, where the natural entangling gates are not restricted to two qubits but can act on multiple, or even all, qubits simultaneously. However, leveraging the capabilities of global quantum operations for algorithm implementations is highly challenging, as directly compiling local gate sequences into global gates usually gives rise to a quantum circuit that is more complex than the original one. Here, we circumvent this difficulty using a variational approach. Specifically, we study parameterized circuit ansatze composed of a finite number of global gates and layers of single-qubit unitaries. We demonstrate the expressibility of these ansatze and apply them to the problem of ground state preparation for the Heisenberg model and the toric code Hamiltonian, highlighting their potential for offering practical advantages.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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