The remarkable progress of machine learning algorithms in the last few years has not escaped the notice of physicists.
In fact, this community have been using techniques derived from machine learning for years, in research as far flung as materials science, quantum chemistry, and high-energy experiment. New developments are now bringing the fields even closer together; particularly interesting are the new synergies developing between quantum physicists and the machine learning community.
The conference "Quantum Machine Learning" brings together experts from a variety of backgrounds who are interested in connections between many-body physics, quantum computing and machine learning. The bridges between quantum mechanics and machine learning are diverse and varied; the conference will include topics related to:
The use of technologies developed for machine learning, such as neural networks, statistical learning, support vector machines, etc., to tackle quantum and classical many-body problems. This might include discriminating phases of matter, analyzing phase transitions, or "learning" a Hamiltonian given samples of wavefunction configurations (the inverse problem).
Physics-inspired algorithms for machine learning and neural networks, such as Boltzmann machines (classical statistical mechanical learning) and their quantum extensions, and connections between deep learning, the renormalization group, and tensor networks.
Opportunities for machine learning that quantum computing will enable. This includes algorithmic advances for fault tolerant computers, as well as currently-available hardware systems such as quantum annealers.
The participant list for the conference boasts a diverse group from academia, as well as industry, including Google, Microsoft, D-wave Systems, Intel, Scotiabank, and many more.
More information can be found at https://perimeterinstitute.ca/conferences/quantum-machine-learning