返回
Optimizing Shot Assignment in Variational Quantum Eigensolver Measurement
DOI:10.1021/acs.jctc.3c01113.png)
摘要
En 中文
Variational quantum eigensolvers (VQEs) show promise for tackling complex quantum chemistry challenges and realizing quantum advantages. However, in VQE, the measurement step encounters difficulties due to errors in objective function evaluation, e.g., the energy of a quantum state. While increasing the number of measurement shots can mitigate measurement errors, this approach leads to higher costs. Strategies for shot assignment have been investigated, allowing for the allocation of varying shot numbers to different Hamiltonian terms and reducing measurement variance through term-specific insights. In this paper, we introduce a dynamic approach, the Variance-Preserved Shot Reduction (VPSR) method. This technique strives to minimize the total number of measurement shots while preserving the variance of measurements throughout the VQE process. Our numerical experiments on H-2 and LiH molecular ground states demonstrate the effectiveness of VPSR in achieving VQE convergence with a notably lower shot count.
期刊
IF:
5.5
论文数:
1.1W
被引数:
5.4W
机构
引用论文
A variational eigenvalue solver on a photonic quantum processor光子量子处理器上的变分特征值求解器
NATURE COMMUNICATIONS
IF15.7
Shot Optimization in Quantum Machine Learning Architectures to Accelerate Training量子机器学习架构中的镜头优化以加速训练
IEEE ACCESS
IF3.6

