# Quantum autoencoders via quantum adders with genetic algorithms

arXiv: https://arxiv.org/abs/1709.07409 · HTML: https://mlsanity.com/paper/1709.07409/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2017.

## Built on by (most cited first, 19 of 19)

- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [Recent Advances for Quantum Neural Networks in Generative Learning](https://mlsanity.com/paper/2206.03066/) (2022)
- [Automatic design of quantum feature maps](https://mlsanity.com/paper/2105.12626/) (2021)
- [Quantum neural network autoencoder and classifier applied to an industrial case study](https://mlsanity.com/paper/2205.04127/) (2022)
- [Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects](https://mlsanity.com/paper/2407.00736/) (2024)
- [Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage](https://mlsanity.com/paper/2507.19861/) (2025)
- [On exploring the potential of quantum auto-encoder for learning quantum systems](https://mlsanity.com/paper/2106.15432/) (2021)
- [AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images](https://mlsanity.com/paper/2208.13246/) (2022)
- [Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries](https://mlsanity.com/paper/2409.15645/) (2024)
- [Quantum continual learning of quantum data realizing knowledge backward transfer](https://mlsanity.com/paper/2203.14032/) (2022)
- [Several fitness functions and entanglement gates in quantum kernel generation](https://mlsanity.com/paper/2309.03307/) (2023)
- [A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor](https://mlsanity.com/paper/2412.16867/) (2024)
- [From Digital Humanities to Quantum Humanities: Potentials and Applications](https://mlsanity.com/paper/2103.11825/) (2021)
- [Quantum Machine Learning Implementations: Proposals and Experiments](https://mlsanity.com/paper/2303.06263/) (2023)
- [Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study](https://mlsanity.com/paper/2501.04690/) (2025)
- [Distribution alignment based transfer fusion frameworks on quantum devices for seeking quantum advantages](https://mlsanity.com/paper/2411.01822/) (2024)
- [Quantum vs. Classical Machine Learning Algorithms for Software Defect Prediction: Challenges and Opportunities](https://mlsanity.com/paper/2412.07698/) (2024)
- [QSVM-QNN: Quantum Support Vector Machine Based Quantum Neural Network Learning Algorithm for Brain-Computer Interfacing Systems](https://mlsanity.com/paper/2505.14192/) (2025)
- [Quantum Global Variational Learning for Quantum Error Correction](https://mlsanity.com/paper/2606.08592/) (2026)

## Abstract (arXiv)

The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between approximate quantum adders and quantum autoencoders. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms.