PANSATZ: pulse-based ansatz for variational quantum algorithms

Dekel Meirom, Steven H. Frankel
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引用次数: 5

Abstract

Quantum computers promise a great computational advantage over classical computers, which might help solve various computational challenges such as the simulation of complicated quantum systems, finding optimum in large optimization problems, and solving large-scale linear algebra problems. Current available quantum devices have only a limited amount of qubits and a high level of noise, limiting the size of problems that can be solved accurately with those devices. Variational quantum algorithms (VQAs) have emerged as a leading strategy to address these limitations by optimizing cost function based on measurement results of shallow depth circuits. Recently, various pulse engineering methods were suggested in order to improve VQA results, including optimizing pulse parameters instead of gate angles as part of the VQA optimization process. In this paper, we suggest a novel pulse-based ansatz, which is parameterized mainly by pulses’ duration of pre-defined pulse structures. This ansatz structure provides relatively low amounts of optimization parameters while maintaining high expressibility, allowing fast convergence. In addition, the ansatz has structured adaptivity to the entanglement level required by the problem, allowing low noise and accurate results. We tested this ansatz against quantum chemistry problems. Specifically, finding the ground-state energy associated with the electron configuration problem, using the variational quantum eigensolver (VQE) algorithm for several different molecules. We manage to achieve chemical accuracy both in simulation for several molecules and on one of IBM’s NISQ devices for the H 2 molecule in the STO-3G basis, without the need for extensive error mitigation. Our results are compared to a common gate-based ansatz and show better accuracy and significant latency reduction—up to 7× shorter ansatz schedules.
PANSATZ:变分量子算法的脉冲分析
量子计算机比经典计算机具有巨大的计算优势,这可能有助于解决各种计算挑战,如复杂量子系统的模拟,在大型优化问题中找到最优解,以及解决大规模线性代数问题。目前可用的量子设备只有有限的量子位和高水平的噪声,限制了这些设备可以精确解决的问题的规模。变分量子算法(VQAs)已成为解决这些限制的主要策略,通过优化基于浅深度电路测量结果的成本函数。近年来,人们提出了各种脉冲工程方法来改善VQA结果,其中包括优化脉冲参数而不是栅极角作为VQA优化过程的一部分。在本文中,我们提出了一种新的基于脉冲的ansatz,该ansatz主要由预先定义的脉冲结构的脉冲持续时间参数化。这种ansatz结构提供了相对较少的优化参数,同时保持了高可表达性,允许快速收敛。此外,ansatz对问题所需的纠缠水平具有结构化的适应性,允许低噪声和准确的结果。我们对量子化学问题进行了测试。具体来说,利用变分量子特征解算器(VQE)算法对几种不同的分子寻找与电子组态问题相关的基态能量。我们设法在多个分子的模拟和IBM的NISQ设备之一上实现STO-3G基础上的h2分子的化学精度,而不需要大量的误差缓解。我们的结果与常见的基于门的ansatz进行了比较,显示出更好的准确性和显著的延迟减少——ansatz时间表缩短了7倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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