# Impact of Single Rotations and Entanglement Topologies in Quantum Neural Networks

arXiv: https://arxiv.org/abs/2509.15722 · HTML: https://mlsanity.com/paper/2509.15722/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [Connecting ansatz expressibility to gradient magnitudes and barren plateaus](https://mlsanity.com/paper/2101.02138/) (2021)
- [Entanglement Induced Barren Plateaus](https://mlsanity.com/paper/2010.15968/) (2020)
- [Experimental Quantum Generative Adversarial Networks for Image Generation](https://mlsanity.com/paper/2010.06201/) (2020)
- [Entanglement Diagnostics for Efficient Quantum Computation](https://mlsanity.com/paper/2102.12534/) (2021)

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

- [Approximate Quantum State Preparation Through Proximal Policy Optimization](https://mlsanity.com/paper/2607.21121/) (2026)

## Abstract (arXiv)

In this work, an analysis of the performance of different Variational Quantum Circuits is presented, investigating how it changes with respect to entanglement topology, adopted gates, and Quantum Machine Learning tasks to be performed. The objective of the analysis is to identify the optimal way to construct circuits for Quantum Neural Networks. In the presented experiments, two types of circuits are used: one with alternating layers of rotations and entanglement, and the other, similar to the first one, but with an additional final layer of rotations. As rotation layers, all combinations of one and two rotation sequences are considered. Four different entanglement topologies are compared: linear, circular, pairwise, and full. Different tasks are considered, namely the generation of probability distributions and images, and image classification. Achieved results are correlated with the expressibility and entanglement capability of the different circuits to understand how these features affect performance.