# Evolutionary-enhanced quantum supervised learning model

arXiv: https://arxiv.org/abs/2311.08081 · HTML: https://mlsanity.com/paper/2311.08081/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 4 references that are in the map.
- First posted on arXiv 14 Nov 2023.

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

- [Quantum Circuit Evolution on NISQ Devices](https://mlsanity.com/paper/2012.13453/) (2020)
- [Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning](https://mlsanity.com/paper/2309.15130/) (2023)

## Abstract (arXiv)

Quantum supervised learning, utilizing variational circuits, stands out as a promising technology for NISQ devices due to its efficiency in hardware resource utilization during the creation of quantum feature maps and the implementation of hardware-efficient ansatz with trainable parameters. Despite these advantages, the training of quantum models encounters challenges, notably the barren plateau phenomenon, leading to stagnation in learning during optimization iterations. This study proposes an innovative approach: an evolutionary-enhanced ansatz-free supervised learning model. In contrast to parametrized circuits, our model employs circuits with variable topology that evolves through an elitist method, mitigating the barren plateau issue. Additionally, we introduce a novel concept, the superposition of multi-hot encodings, facilitating the treatment of multi-classification problems. Our framework successfully avoids barren plateaus, resulting in enhanced model accuracy. Comparative analysis with variational quantum classifiers from the technology's state-of-the-art reveal a substantial improvement in training efficiency and precision. Furthermore, we conduct tests on a challenging dataset class, traditionally problematic for conventional kernel machines, demonstrating a potential alternative path for achieving quantum advantage in supervised learning for NISQ era.