# Quantum speedups of some general-purpose numerical optimisation algorithms

arXiv: https://arxiv.org/abs/2004.06521 · HTML: https://mlsanity.com/paper/2004.06521/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [On Optimizing Hyperparameters for Quantum Neural Networks](https://mlsanity.com/paper/2403.18579/) (2024)

## Abstract (arXiv)

We give quantum speedups of several general-purpose numerical optimisation methods for minimising a function f:ℝn → ℝ. First, we show that many techniques for global optimisation under a Lipschitz constraint can be accelerated near-quadratically. Second, we show that backtracking line search, an ingredient in quasi-Newton optimisation algorithms, can be accelerated up to quadratically. Third, we show that a component of the Nelder-Mead algorithm can be accelerated by up to a multiplicative factor of O(√n). Fourth, we show that a quantum gradient computation algorithm of Gily'en et al. can be used to approximately compute gradients in the framework of stochastic gradient descent. In each case, our results are based on applying existing quantum algorithms to accelerate specific components of the classical algorithms, rather than developing new quantum techniques.