Quantum computing for machine learning

CSoNet 2020 will feature a special track on quantum computing and machine learning. It is an interdisciplinary field that bridges machine learning and quantum technology. Quantum computing will enable solving near-term as well as long-term problems both theoretical and practical and drive the innovations of the future, including AI.

The goal of this track is to provide a venue to discuss the latest advances in quantum computing as well as machine learning in arena of quantum computing such as quantum simulations, development of quantum neural networks on processors, use of hybrid quantum-classical algorithms for approximate optimisation. 

The scope of the special track includes (but is not limited to) the following topics:

  • Quantum algorithms for machine learning tasks
  • Machine Learning for quantum Information processing
  • Quantum Circuit Optimisation
  • Quantum learning theory
  • Quantum machine learning for industry
  • Quantum Error Correcting Codes and Fault Tolerant Quantum Computation
  • Quantum neural network

Accepted papers will be published in the conference proceedings. Also, extended versions of selected best papers will be invited for publication in Journal of Combinatorial Optimization, IEEE Transactions on Network Science and Engineering, and Computational Social Networks.

Important Dates: 

  • Paper Submission : September 23, 2020
  • Acceptance Notification : October 9, 2020
  • Camera Ready and Registration: October 15, 2020
  • Conference Dates: December 11-13, 2020

Papers must be formatted using the LNCS format without altering margins or the font point. The maximum length of a regular paper (including references) is 12 pages.

Submissions are open on Easy Chair

Track Chair: