# A quantum central path algorithm for linear optimization

arXiv: https://arxiv.org/abs/2311.03977 · HTML: https://mlsanity.com/paper/2311.03977/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2023.

## Built on by (most cited first, 3 of 3)

- [Quantum Algorithms for Bandits with Knapsacks with Improved Regret and Time Complexities](https://mlsanity.com/paper/2507.04438/) (2025)
- [Operator-Level Quantum Acceleration of Non-Logconcave Sampling](https://mlsanity.com/paper/2505.05301/) (2025)
- [Quantum Optimization via Gradient-Based Hamiltonian Descent](https://mlsanity.com/paper/2505.14670/) (2025)

## Abstract (arXiv)

We propose a novel quantum algorithm for solving linear optimization problems by quantum-mechanical simulation of the central path. While interior point methods follow the central path with an iterative algorithm that works with successive linearizations of the perturbed KKT conditions, we perform a single simulation working directly with the nonlinear complementarity equations. This approach yields an algorithm for solving linear optimization problems involving m constraints and n variables to varepsilon-optimality using O left( √m + n fracR1varepsilonright) queries to an oracle that evaluates a potential function, where R1 is an ℓ1-norm upper bound on the size of the optimal solution. In the standard gate model (i.e., without access to quantum RAM) our algorithm can obtain highly-precise solutions to LO problems using at most O left( √m + n textsfnnz (A) fracR1varepsilonright) elementary gates, where textsfnnz (A) is the total number of non-zero elements found in the constraint matrix.