Experimental Implementation of a Quantum Autoencoder via Quantum Adders
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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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jul 2018.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 2; 2025: 1.
12 papers.
- 2022Recent Advances for Quantum Neural Networks in Generative Learningcited by 39
- 2022An Invitation to Distributed Quantum Neural Networkscited by 13
- 2022Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulationcited by 13
- 2025Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantagecited by 7
- 2021On exploring the potential of quantum auto-encoder for learning quantum systemscited by 6
- 2021F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuitscited by 3
- 2024Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industriescited by 3
- 2024A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processorcited by 2
- 2021From Digital Humanities to Quantum Humanities: Potentials and Applicationscited by 1
- 2023Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networkscited by 1
- 2023Error mitigation of entangled states using brainbox quantum autoencoderscited by 1
- 2023Quantum Machine Learning Implementations: Proposals and Experimentscited by 1
- 2025Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage26 Jul 2025
- 2024A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor22 Dec 2024
- 2024Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries24 Sep 2024
- 2023Quantum Machine Learning Implementations: Proposals and Experiments11 Mar 2023
- 2023Error mitigation of entangled states using brainbox quantum autoencoders2 Mar 2023
- 2023Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks19 Jan 2023
- 2022Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation16 Nov 2022
- 2022An Invitation to Distributed Quantum Neural Networks14 Nov 2022
- 2022Recent Advances for Quantum Neural Networks in Generative Learning7 Jun 2022
- 2021F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits8 Oct 2021
- 2021On exploring the potential of quantum auto-encoder for learning quantum systems29 Jun 2021
- 2021From Digital Humanities to Quantum Humanities: Potentials and Applications17 Mar 2021