# Weight Re-Mapping for Variational Quantum Algorithms

arXiv: https://arxiv.org/abs/2306.05776 · HTML: https://mlsanity.com/paper/2306.05776/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping](https://mlsanity.com/paper/2212.14807/) (2022)

## Built on by (most cited first, 4 of 4)

- [Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting](https://mlsanity.com/paper/2404.09213/) (2024)
- [Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures](https://mlsanity.com/paper/2311.05559/) (2023)
- [Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits](https://mlsanity.com/paper/2504.07273/) (2025)
- [Multi-VQC: A Novel QML Approach for Enhancing Healthcare Classification](https://mlsanity.com/paper/2505.20797/) (2025)

## Abstract (arXiv)

Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum machine learning applications. The promising outcomes shown by VQCs, such as improved generalization and reduced parameter training requirements, are attributed to the robust algorithmic capabilities of quantum computing. However, the current gradient-based training approaches for VQCs do not adequately accommodate the fact that trainable parameters (or weights) are typically used as angles in rotational gates. To address this, we extend the concept of weight re-mapping for VQCs, as introduced by K"olle et al. (2023). This approach unambiguously maps the weights to an interval of length 2π, mirroring data rescaling techniques in conventional machine learning that have proven to be highly beneficial in numerous scenarios. In our study, we employ seven distinct weight re-mapping functions to assess their impact on eight classification datasets, using variational classifiers as a representative example. Our results indicate that weight re-mapping can enhance the convergence speed of the VQC. We assess the efficacy of various re-mapping functions across all datasets and measure their influence on the VQC's average performance. Our findings indicate that weight re-mapping not only consistently accelerates the convergence of VQCs, regardless of the specific re-mapping function employed, but also significantly increases accuracy in certain cases.