The last decades have witnessed a surge of activity in network science and machine learning and an unprecedented success in understanding and extracting information from big data.
In Physics Challenges for Machine Learning and Network Science we will explore the interface between Physics, Machine Learning and Network Science.
We’ll examine urgent questions including:
What is the most efficient network architecture for a quantum computer, and which are the key structural universalities?
How can a quantum network interact with a classical network?
To what extent can machine learning methodology be a resource for quantum computation, and vice-versa?
Can network theory be used to boost the network architecture of machinelearning tools?
Can quantum statistical physics bring a better understanding as to how heuristic machine learning approaches work?
The workshop will take place over two days and aims to bring together researchers in the above fields to further explore the fertile grounds for collaboration and cross-fertilization, and address the big questions in the field.