Quantum-inspired algorithms for solving low-rank linear equation systems with logarithmic dependence on the dimension
We present two efficient classical analogues of the quantum matrix inversion algorithm  for low-rank matrices. Inspired by recent work of Tang , 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 , our results indicate that more low-rank quantum algorithms can be effectively “dequantised” into classical length-square sampling 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