Learning in a quantum world

• Abstract:This talk has two parts. In the first part I’ll reflect on the current status and prospects for quantum computing. In the second part I’ll describe recent results about using classical machine learning and quantum data to predict properties of complex quantum systems. In particular, these results highlight the potential for machine learning models to predict the output of a complex quantum process much faster than the time needed to run the process itself. The talk draws on material from these references:
  • Quantum computing in the NISQ era and beyond
  • Predicting many properties of a quantum system from very few measurements
  • Provably efficient machine learning for quantum many-body problems
  • Learning to predict arbitrary quantum processes

Light refreshments will be served starting at 3:30 p.m. Talk begins at 4 p.m.
This seminar series is sponsored by CUbit with generous support of the Caruso Foundation.