• Quantum Techniques in Machine Learning 2022

    Naples, 8 - 11 November 2022

  •     Extended deadline for Abstract Submission!    

        New deadline: 15 July 2022     

The conference Quantum Techniques in Machine Learning (QTML) is an annual international event that focuses on quantum machine learning, an interdisciplinary field that bridges quantum technology and machine learning.

Conference Committees

Important Dates

Call for Papers



Quantum Techniques in Machine Learning (QTML) is an annual international conference focusing on the interdisciplinary field of quantum technology and machine learning. The goal of the conference is to gather leading academic researchers and industry players to interact through a series of scientific talks focused on the interplay between machine learning and quantum physics.
​QTML was first hosted in Verona, Italy (2017), then in Durban, South Africa (2018), Daejeon, South Korea (2019), virtual (2020, hosted by Zapata Computing), virtual (2021, hosted by RIKEN-AIP). This is the conference's sixth annual year and will be held in Naples.

Naples has the largest historical city center in Europe, listed by UNESCO as a World Heritage Site. In the immediate vicinity of Naples are numerous culturally and historically significant sites, including the Royal Palace of Caserta and the Roman ruins of Pompeii and Herculaneum. Culinarily, Naples is synonymous with pizza, which originated in the city. Neapolitan music has furthermore been highly influential, and it had an important and vibrant role over the centuries not just in the music of Italy, but in the general history of western European musical traditions. Around Naples are located some of the most beautiful seaside towns of the world such as Capri, Ischia, Sorrento and the Amalfi coast.

Naples hosts the University Federico II, founded by emperor of the Holy Roman Empire Frederick II on 5 June 1224, the most ancient state-supported institution of higher education and research in the world.

Conference Topics

Example topics include, but are not limited to

  • 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
  • Fuzzy logic for quantum machine learning
  • Quantum state reconstruction from data
  • Quantum tomography
  • Machine learning for experimental quantum information
  • Quantum optimization
  • Quantum evolutionary algorithms
  • Quantum machine learning applications for industry, chemistry, biology, finance, cybersecurity


Enjoy QTML 2022 · Enjoy Naples