深度神经网络辅助量子化学 量子计算机上的量子化学计算
Kalpak Ghosh1,2, Sumit Kumar1,2, Nirmal Mammavalappil Rajan3
1Department of Chemistry, Indian Institute of Technology Madras, Chennai, 600036, India.
ACS omega
|December 25, 2023
概括
与变量量子自溶解器 (VQE) 集成的深度神经网络 (DNN) 在杂的量子硬件上显示出准确的分子基态能量预测的前景. 推DNN1方法用于较低深度的电路,在杂的环境中性能优于标准VQE.
科学领域:
- 量子计算是一种量子计算.
- 计算化学是一种计算化学.
- 人工智能的人工智能是人工智能.
背景情况:
- 由于固有的噪音,噪音中等尺度量子 (NISQ) 设备对变量量子自溶器 (VQE) 的准确性提出了挑战.
- 在VQE中代错误放大加剧了NISQ硬件上的不准确性.
- 深度神经网络 (DNN) 已被探索以减轻VQE错误,但主要是在无噪声模拟中.
研究的目的:
- 评估两个DNN-VQE方法 (DNN1和DNNF) 的有效性,用于预测噪音量子设备上的分子基态能量.
- 为了比较DNN1,DNNF和标准VQE在各种量子电路,ansatzes,量子比特计数和电路深度中的准确性和性能.
- 确定最合适的DNN-VQE方法,以在当前量子硬件上进行准确和高效的基态能量计算.
主要方法:
- 用各种量子电路对DNN1和DNNF方法进行训练的DNN模型.
- 在噪音模拟器和真实量子硬件上测试了DNN1,DNNF和VQE.
- 在不同的安萨兹,量子比特数量和电路深度 (例如,深度15与深度83) 中评估了准确性.
主要成果:
- 在杂的量子环境中,DNN1和DNNF在预测更准确的基态能量方面始终超过标准VQE.
- DNN-VQE方法仅在较低的电路深度 (深度=15,门=21) 提供了有意义的结果;在更高的深度 (深度=83,门=112),准确性明显降低.
- 与VQE相比,DNNF没有提供速度优势,而DNN1则展示了更快速计算的潜力.
结论:
- DNN-VQE方法提供了比标准VQE更好的准确性,用于在杂的量子硬件上进行分子基态能量计算.
- 推DNN1方法作为在当前量子硬件上获得高效和准确结果的首选方法,特别是对于深度较低,量子比特较少的电路.
- 需要进一步的研究来解决更高电路深度的精度限制.
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