Collection

QTML 2022: Quantum Techniques in Machine Learning

Submission status
Closed
Quantum Techniques in Machine Learning (QTML) is an annual international conference that focuses on the pioneering concept of quantum machine learning, an interdisciplinary field that bridges quantum technology and machine learning to try to achieve the quantum advantage in artificial intelligence methodologies and applications. QTML 2022 has been held from November 7 to 12, 2022 at University of Naples Federico II in Naples, Italy. It was the 6th conference in a series that started in Verona, Italy in 2017, and was last held in 2021, hosted by RIKEN-AIP, Japan. The conference will bring together experts from quantum computing and machine learning to discuss the latest progress in the rapidly growing field of quantum machine learning. This year's conference hosted about 180 participants making the event a leading research forum for quantum computing and machine learning topics. This topical collection will collect the extended version of the best original results on recent advances in the development of quantum techniques in machine learning presented at QTML 2022. Topics covered are: • Quantum algorithms for machine learning tasks • Learning and optimization with hybrid quantum-classical methods • Tensor methods and quantum-inspired machine learning • Data encoding and processing in quantum systems • Quantum learning theory • Quantum variational circuits • Quantum computing and approximate reasoning • Quantum optimization and evolutionary algorithms • Quantum state reconstruction from data • Quantum software • Machine learning for experimental quantum information • Quantum machine learning applications

Editors

Articles (9 in this collection)

  4. Data re-uploading with a single qudit

    Authors (first, second and last of 4)

    • Noah L. Wach
    • Manuel S. Rudolph
    • Sebastian Schmitt
    • Content type: Research Article
    • Open Access
    • Published: 14 August 2023
    • Article: 36