Reducing the resources required by ADAPT-VQE using coupled exchange operators and improved subroutines

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Mafalda Ramôa, Panagiotis G. Anastasiou, Luis Paulo Santos, Nicholas J. Mayhall, Edwin Barnes, Sophia E. Economou
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Abstract

Adaptive variational quantum algorithms arguably offer the best prospects for quantum advantage in the Noisy Intermediate-Scale Quantum era. Since the inception of the first such algorithm, the Adaptive Derivative-Assembled Problem-Tailored Variational Quantum Eigensolver (ADAPT-VQE), many improvements have appeared in the literature. We combine the key improvements along with a novel operator pool—which we term Coupled Exchange Operator (CEO) pool—to assess the cost of running state-of-the-art ADAPT-VQE on hardware in terms of measurement counts and circuit depth. We show a dramatic reduction of these quantum computational resources compared to the early versions of the algorithm: CNOT count, CNOT depth and measurement costs are reduced by up to 88%, 96% and 99.6%, respectively, for molecules represented by 12 to 14 qubits (LiH, H6 and BeH2). We also find that our state-of-the-art CEO-ADAPT-VQE outperforms the Unitary Coupled Cluster Singles and Doubles ansatz, the most widely used static VQE ansatz, in all relevant metrics, and offers a five order of magnitude decrease in measurement costs as compared to other static ansätze with competitive CNOT counts.

Abstract Image

使用耦合交换操作符和改进的子例程减少ADAPT-VQE所需的资源
自适应变分量子算法在嘈杂的中尺度量子时代为量子优势提供了最好的前景。自第一个这样的算法,自适应导数组合问题定制变分量子特征求解器(ADAPT-VQE)开始以来,文献中出现了许多改进。我们将这些关键改进与一个新的运营商池(我们称之为耦合交换运营商(CEO)池)结合起来,以评估在硬件上运行最先进的ADAPT-VQE在测量计数和电路深度方面的成本。与早期版本的算法相比,我们展示了这些量子计算资源的大幅减少:对于由12至14个量子比特(LiH, H6和BeH2)表示的分子,CNOT计数,CNOT深度和测量成本分别减少了88%,96%和99.6%。我们还发现,我们最先进的CEO-ADAPT-VQE在所有相关指标上都优于使用最广泛的静态VQE分析,并且与具有竞争CNOT计数的其他静态ansätze相比,测量成本降低了五个数量级。
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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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