# A comparative study of universal quantum computing models: towards a physical unification

arXiv: https://arxiv.org/abs/2108.07909 · HTML: https://mlsanity.com/paper/2108.07909/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 154 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [A high-bias, low-variance introduction to Machine Learning for physicists](https://mlsanity.com/paper/1803.08823/) (2018)
- [Quantum Random Access Stored-Program Machines](https://mlsanity.com/paper/2003.03514/) (2020)

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

- [Universal resources for quantum computing](https://mlsanity.com/paper/2303.03715/) (2023)

## Abstract (arXiv)

Quantum computing has been a fascinating research field in quantum physics. Recent progresses motivate us to study in depth the universal quantum computing models (UQCM), which lie at the foundation of quantum computing and have tight connections with fundamental physics. Although being developed decades ago, a physically concise principle or picture to formalize and understand UQCM is still lacking. This is challenging given the diversity of still-emerging models, but important to understand the difference between classical and quantum computing. In this work, we carried out a primary attempt to unify UQCM by classifying a few of them as two categories, hence making a table of models. With such a table, some known models or schemes appear as hybridization or combination of models, and more importantly, it leads to new schemes that have not been explored yet. Our study of UQCM also leads to some insights into quantum algorithms. This work reveals the importance and feasibility of systematic study of computing models.