# Quadratic Unconstrained Binary Optimisation via Quantum-Inspired Annealing

arXiv: https://arxiv.org/abs/2108.08064 · HTML: https://mlsanity.com/paper/2108.08064/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2021.

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

- [Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition](https://mlsanity.com/paper/2301.08292/) (2023)
- [Efficient Combinatorial Optimization via Heat Diffusion](https://mlsanity.com/paper/2403.08757/) (2024)
- [Evaluating the Performance of a D-Wave Quantum Annealing System for Feature Subset Selection in Software Defect Prediction](https://mlsanity.com/paper/2410.16469/) (2024)

## Abstract (arXiv)

We present a classical algorithm to find approximate solutions to instances of quadratic unconstrained binary optimisation. The algorithm can be seen as an analogue of quantum annealing under the restriction of a product state space, where the dynamical evolution in quantum annealing is replaced with a gradient-descent based method. This formulation is able to quickly find high-quality solutions to large-scale problem instances, and can naturally be accelerated by dedicated hardware such as graphics processing units. We benchmark our approach for large scale problem instances with tuneable hardness and planted solutions. We find that our algorithm offers a similar performance to current state of the art approaches within a comparably simple gradient-based and non-stochastic setting.