A quantum central path algorithm for linear optimization

• URL: http://arxiv.org/abs/2311.03977v2
• Date: Wed, 16 Oct 2024 12:11:03 GMT
• Title: A quantum central path algorithm for linear optimization
• Authors: Brandon Augustino, Jiaqi Leng, Giacomo Nannicini, Tamás Terlaky, Xiaodi Wu,
• Abstract summary: We propose a novel quantum algorithm for solving linear optimization problems by quantum-mechanical simulation of the central path. This approach yields an algorithm for solving linear optimization problems involving $m$ constraints and $n$ variables to $varepsilon$-optimality. 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 $$mathcalO left( sqrtm + n textsfnnz (A) fracR_1
• Score: 5.450016817940232
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• Abstract: We propose a novel quantum algorithm for solving linear optimization problems by quantum-mechanical simulation of the central path. While interior point methods follow the central path with an iterative algorithm that works with successive linearizations of the perturbed KKT conditions, we perform a single simulation working directly with the nonlinear complementarity equations. This approach yields an algorithm for solving linear optimization problems involving $m$ constraints and $n$ variables to $\varepsilon$-optimality using $\mathcal{O} \left( \sqrt{m + n} \frac{R_{1}}{\varepsilon}\right)$ queries to an oracle that evaluates a potential function, where $R_{1}$ is an $\ell_{1}$-norm upper bound on the size of the optimal solution. In the standard gate model (i.e., without access to quantum RAM) our algorithm can obtain highly-precise solutions to LO problems using at most $$\mathcal{O} \left( \sqrt{m + n} \textsf{nnz} (A) \frac{R_1}{\varepsilon}\right)$$ elementary gates, where $\textsf{nnz} (A)$ is the total number of non-zero elements found in the constraint matrix.

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