# Experimental Implementation of a Quantum Autoencoder via Quantum Adders

arXiv: https://arxiv.org/abs/1807.10643 · HTML: https://mlsanity.com/paper/1807.10643/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2018.

## Built on by (most cited first, 12 of 12)

- [Recent Advances for Quantum Neural Networks in Generative Learning](https://mlsanity.com/paper/2206.03066/) (2022)
- [An Invitation to Distributed Quantum Neural Networks](https://mlsanity.com/paper/2211.07056/) (2022)
- [Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation](https://mlsanity.com/paper/2211.08737/) (2022)
- [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)
- [F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits](https://mlsanity.com/paper/2110.04253/) (2021)
- [Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries](https://mlsanity.com/paper/2409.15645/) (2024)
- [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)
- [Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks](https://mlsanity.com/paper/2301.08167/) (2023)
- [Error mitigation of entangled states using brainbox quantum autoencoders](https://mlsanity.com/paper/2303.01134/) (2023)
- [Quantum Machine Learning Implementations: Proposals and Experiments](https://mlsanity.com/paper/2303.06263/) (2023)

## Abstract (arXiv)

Quantum autoencoders allow for reducing the amount of resources in a quantum computation by mapping the original Hilbert space onto a reduced space with the relevant information. Recently, it was proposed to employ approximate quantum adders to implement quantum autoencoders in quantum technologies. Here, we carry out the experimental implementation of this proposal in the Rigetti cloud quantum computer employing up to three qubits. The experimental fidelities are in good agreement with the theoretical prediction, thus proving the feasibility to realize quantum autoencoders via quantum adders in state-of-the-art superconducting quantum technologies.