Aashna Anil Zade, Kenji Sugisaki, Matthias Werner, Ana Palacios, Jordi Riu, Jan Nogué, Artur Garcia-Saez, Arnau Riera, V. S. Prasannaa
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引用次数: 0
摘要
量子-经典混合变分量子本征求解(VQE)算法可以说是量子化学中最流行的噪声中尺度量子(NISQ)时代方法。我们认为尚未开发的量子退火特征求解(QAE)算法是一个有价值的替代方案。我们结合了强相关效应占主导地位的系统的数值计算,并从我们对QAE和VQE的初步尺度分析中得出结论,使QAE成为NISQ时代量子化学VQE的竞争者。对于前者,我们选择了计算矩形几何中H \(_4\)分子中避免交叉的代表性示例,并证明我们获得的结果在1.2左右% of the full configuration interaction value on the D-Wave Advantage system 4.1 hardware. We carry out analyses on the effect of the number of shots, anneal time, and the choice of Lagrange multiplier on our obtained results. Following our numerical results, we carry out a detailed yet preliminary analysis of the scaling behaviours of both the QAE and the VQE algorithms. We analyse the non-recurring and recurring costs involved in both the algorithms and arrive at their net scaling behaviours.
Quantum annealing eigensolver as a NISQ era tool for probing strong correlation effects in quantum chemistry
The quantum–classical hybrid variational quantum eigensolver (VQE) algorithm is arguably the most popular noisy intermediate-scale quantum (NISQ) era approach to quantum chemistry. We consider the underexplored quantum annealing eigensolver (QAE) algorithm as a worthy alternative. We use a combination of numerical calculations for a system where strong correlation effects dominate, and conclusions drawn from our preliminary scaling analysis for QAE and VQE to make the case for QAE as a NISQ era contender to VQE for quantum chemistry. For the former, we pick the representative example of computing avoided crossings in the H\(_4\) molecule in a rectangular geometry and demonstrate that we obtain results to within about 1.2% of the full configuration interaction value on the D-Wave Advantage system 4.1 hardware. We carry out analyses on the effect of the number of shots, anneal time, and the choice of Lagrange multiplier on our obtained results. Following our numerical results, we carry out a detailed yet preliminary analysis of the scaling behaviours of both the QAE and the VQE algorithms. We analyse the non-recurring and recurring costs involved in both the algorithms and arrive at their net scaling behaviours.
期刊介绍:
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