Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension

Cited from the HHL algorithm for Theorems 1 and 2. Under length-square sampling access, a low-rank system can be solved to multiplicative error with a classical cost polylogarithmic in the dimensions and polynomial in the rank, the condition number, the Frobenius norm, and the error. The access model is not the sparse oracle model of the 2009 algorithm. Also cited from linear systems.