CERN Hosts International Quantum Techniques in Machine Learning Conference

Date:

CERN is set to host the 7th edition of the annual international Quantum Techniques in Machine Learning (QTML) conference from 19 to 24 November 2023. The conference, which aims to bring together leading academic researchers and industry players to discuss the interplay between machine learning and quantum physics, will include a series of scientific talks, including key-note and invited speakers, as well as tutorials. The event will cover topics related to the application of quantum techniques in machine-learning tasks and the use of machine-learning algorithms for studying quantum systems.

Previous editions of QTML were held in Verona, Italy (2017), Durban, South Africa (2018), Daejeon, South Korea (2019), and Naples (Italy), with online versions in 2020 and 2021, hosted by Zapata Computing and Riken, respectively.

This year’s event will be held at CERN, Switzerland, and will be followed by a workshop on quantum software on Saturday, 25 November, organized by Alessandra Di Pierro from the University of Verona and Carsten Blank from Data Cybernetics ssc GmbH.

The research areas covered during the conference include, but are not limited to, quantum machine learning, quantum algorithms for optimization, quantum simulation, quantum cryptography, quantum sensors, and quantum-enhanced reinforcement learning.

The aim of the conference is to provide a platform for experts from various fields to discuss advancements in quantum machine learning and the practical utilization of quantum computing. Academic researchers, industry professionals, students, and enthusiasts are encouraged to attend.

Registration for the event is set to open shortly. Updates and more details regarding the conference can be found on the event’s website: https://qtml-2023.web.cern.ch/.

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Frequently Asked Questions (FAQs) Related to the Above News

What is the Quantum Techniques in Machine Learning (QTML) conference?

The Quantum Techniques in Machine Learning (QTML) conference is an annual international conference that brings together leading academic researchers and industry players to discuss the interplay between machine learning and quantum physics.

When and where will the 7th edition of the QTML conference be held?

The 7th edition of the QTML conference will be held from 19 to 24 November 2023 at CERN in Switzerland.

What topics will be covered during the conference?

The conference will cover topics related to the application of quantum techniques in machine-learning tasks and the use of machine-learning algorithms for studying quantum systems. These topics include quantum machine learning, quantum algorithms for optimization, quantum simulation, quantum cryptography, quantum sensors, and quantum-enhanced reinforcement learning.

Who can attend the conference?

Academic researchers, industry professionals, students, and enthusiasts are encouraged to attend the conference.

Will there be a workshop on quantum software after the conference?

Yes, there will be a workshop on quantum software on Saturday, 25 November, organized by Alessandra Di Pierro from the University of Verona and Carsten Blank from Data Cybernetics ssc GmbH.

How can I register for the conference?

Registration for the conference will open shortly. Updates and more details regarding the conference can be found on the event's website: https://qtml-2023.web.cern.ch/.

Please note that the FAQs provided on this page are based on the news article published. While we strive to provide accurate and up-to-date information, it is always recommended to consult relevant authorities or professionals before making any decisions or taking action based on the FAQs or the news article.

Kunal Joshi
Kunal Joshi
Meet Kunal, our insightful writer and manager for the Machine Learning category. Kunal's expertise in machine learning algorithms and applications allows him to provide a deep understanding of this dynamic field. Through his articles, he explores the latest trends, algorithms, and real-world applications of machine learning, making it accessible to all.

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