We present two efficient classical analogues of the quantum matrix inversion algorithm [16] for low-rank matrices. Inspired by recent work of Tang [27], assuming length-square sampling access to input data, we implement the pseudoinverse of a low-rank matrix allowing us to sample from the solution to the problem Ax = b using fast sampling techniques. We construct implicit descriptions of the pseudo-inverse by finding approximate singular value decomposition of A via subsampling, then inverting the singular values. In principle, our approaches can also be used to apply any desired “smooth” function to the singular values. Since many quantum algorithms can be expressed as a singular value transformation problem [15], our results indicate that more low-rank quantum algorithms can be effectively “dequantised” into classical length-square sampling algorithms.

Importance sampling, Quantum machine learning, Quantum-inspired, Regression, Sublinear algorithms
Leibniz International Proceedings in Informatics, LIPIcs
International Symposium on Algorithms and Computation

Chia, N.-H, Gilyén, A.P, Lin, H.-H, Lloyd, S, Tang, E, & Wang, C. (2020). Quantum-inspired algorithms for solving low-rank linear equation systems with logarithmic dependence on the dimension. In 31st International Symposium on Algorithms and Computation (ISAAC 2020). doi:10.4230/LIPIcs.ISAAC.2020.47