Quantum Speed-Ups for Solving Semidefinite Programs

TLDR: It is proved the algorithm cannot be substantially improved (in terms of n and m) giving a quantum lower bound for solving semidefinite programs with constant s, R, r and δ.

Abstract: We give a quantum algorithm for solving semidefinite programs (SDPs). It has worst-case running time n^{\frac{1}{2}} m^{\frac{1}{2}} s^2 \poly(\log(n), \log(m), R, r, 1/δ), with n and s the dimension and row-sparsity of the input matrices, respectively, m the number of constraints, δ the accuracy of the solution, and R, r upper bounds on the size of the optimal primal and dual solutions, respectively. This gives a square-root unconditional speed-up over any classical method for solving SDPs both in n and m. We prove the algorithm cannot be substantially improved (in terms of n and m) giving a Ω(n^{\frac{1}{2}}+m^{\frac{1}{2}}) quantum lower bound for solving semidefinite programs with constant s, R, r and δ. The quantum algorithm is constructed by a combination of quantum Gibbs sampling and the multiplicative weight method. In particular it is based on a classical algorithm of Arora and Kale for approximately solving SDPs. We present a modification of their algorithm to eliminate the need for solving an inner linear program which may be of independent interest.