2023/12/25 by Aruto Hosaka, Hosaka, Aruto, Koichi Yanagisawa +9
Computer Science · #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Matrix Theory and Algorithms
paper · pdf · doi:10.48550/arxiv.2312.15657
We apply preconditioning, which is widely used in classical solvers for linear systems Ax=b, to the variational quantum linear solver. By utilizing incomplete LU factorization as a preconditioner for linear equations formed by 128×128 random sparse matrices, we numerically demonstrate a notable reduction in the required ansatz depth, demonstrating that preconditioning is useful for quantum algorithms. This reduction in circuit depth is crucial to improving the efficiency and accuracy of Noisy Intermediate-Scale Quantum (NISQ) algorithms. Our findings suggest that combining classical computing techniques, such as preconditioning, with quantum algorithms can significantly enhance the performance of NISQ algorithms.