Quantum Algorithms for Efficiently Solving Large-Scale Linear Programs via Interior Point Methods

We describe a quantum algorithm based on an interior point method that can solve a linear program with n inequality constraints on d variables in time √n · poly(d, log(1/ε)), which is sublinear for tall linear programs (n ≫ d). The algorithm explicitly returns a feasible solution that is ε-close to optimal.