# A Quantum Interior-Point Predictor-Corrector Algorithm for Linear Programming

## TL;DR

This paper presents a quantum algorithm for dense linear programming that accelerates classical interior-point methods by leveraging quantum linear system algorithms, providing a quantum speed-up in the number of variables.

## Contribution

It introduces a quantum interior-point predictor-corrector algorithm that outputs classical solutions and achieves a speed-up over classical methods for dense LP problems.

## Key findings

- Quantum speed-up in the number of variables n.
- Algorithm outputs classical solution and optimal value.
- Complexity improves over classical interior-point methods.

## Abstract

We introduce a new quantum optimization algorithm for dense Linear Programming problems, which can be seen as the quantization of the Interior Point Predictor-Corrector algorithm \cite{Predictor-Corrector} using a Quantum Linear System Algorithm \cite{DenseHHL}. The (worst case) work complexity of our method is, up to polylogarithmic factors, $O(L\sqrt{n}(n+m)\overline{||M||_F}\bar{\kappa}^2\epsilon^{-2})$ for $n$ the number of variables in the cost function, $m$ the number of constraints, $\epsilon^{-1}$ the target precision, $L$ the bit length of the input data, $\overline{||M||_F}$ an upper bound to the Frobenius norm of the linear systems of equations that appear, $||M||_F$, and $\bar{\kappa}$ an upper bound to the condition number $\kappa$ of those systems of equations. This represents a quantum speed-up in the number $n$ of variables in the cost function with respect to the comparable classical Interior Point algorithms when the initial matrix of the problem $A$ is dense: if we substitute the quantum part of the algorithm by classical algorithms such as Conjugate Gradient Descent, that would mean the whole algorithm has complexity $O(L\sqrt{n}(n+m)^2\bar{\kappa} \log(\epsilon^{-1}))$, or with exact methods, at least $O(L\sqrt{n}(n+m)^{2.373})$. Also, in contrast with any Quantum Linear System Algorithm, the algorithm described in this article outputs a classical description of the solution vector, and the value of the optimal solution.