# Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing

arXiv: https://arxiv.org/abs/2505.16332 · HTML: https://mlsanity.com/paper/2505.16332/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Learned Step Size Quantization](https://mlsanity.com/paper/1902.08153/) (2019)
- [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://mlsanity.com/paper/1710.01878/) (2017)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [QUBO Formulations for Training Machine Learning Models](https://mlsanity.com/paper/2008.02369/) (2020)
- [Quantum Permutation Synchronization](https://mlsanity.com/paper/2101.07755/) (2021)
- [Adiabatic Quantum Computing for Multi Object Tracking](https://mlsanity.com/paper/2202.08837/) (2022)
- [OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization](https://mlsanity.com/paper/2205.11141/) (2022)
- [borealis - A generalized global update algorithm for Boolean optimization problems](https://mlsanity.com/paper/1605.09399/) (2016)
- [Traffic Signal Optimization on a Square Lattice with Quantum Annealing](https://mlsanity.com/paper/2003.07527/) (2020)

## Built on by (most cited first, 2 of 2)

- [Neural Network Pruning via QUBO Optimization](https://mlsanity.com/paper/2604.05856/) (2026)
- [Task-Aware QUBO Allocation for Mixed-Precision Quantization](https://mlsanity.com/paper/2609.22238/) (2026)

## Abstract (arXiv)

Quantum optimization is the most mature quantum computing technology to date, providing a promising approach towards efficiently solving complex combinatorial problems. Methods such as adiabatic quantum computing (AQC) have been employed in recent years on important optimization problems across various domains. In deep learning, deep neural networks (DNN) have reached immense sizes to support new predictive capabilities. Optimization of large-scale models is critical for sustainable deployment, but becomes increasingly challenging with ever-growing model sizes and complexity. While quantum optimization is suitable for solving complex problems, its application to DNN optimization is not straightforward, requiring thorough reformulation for compatibility with commercially available quantum devices. In this work, we explore the potential of adopting AQC for fine-grained pruning-quantization of convolutional neural networks. We rework established heuristics to formulate model compression as a quadratic unconstrained binary optimization (QUBO) problem, and assess the solution space offered by commercial quantum annealing devices. Through our exploratory efforts of reformulation, we demonstrate that AQC can achieve effective compression of practical DNN models. Experiments demonstrate that adiabatic quantum computing (AQC) not only outperforms classical algorithms like genetic algorithms and reinforcement learning in terms of time efficiency but also excels at identifying global optima.