The advent of modern machine learning has ushered in rapid advances in the classification and interpretation of large data sets, sparking a revolution in areas such as image and natural language processing. Much of our current understanding of the techniques that underlie this revolution owes a great debt to insights first gleaned from condensed matter and statistical physics. This raises the important question of what further insights remain to be found at the intersection of machine learning and fields such as statistical physics, condensed matter, and quantum information. In response to this question, this workshop aims to bring together experts from a variety of backgrounds who are interested in connections between many-body physics, quantum computing and machine learning. The scope of the conference will include:
- The use of techniques from machine learning, such as neural networks or statistical learning, to tackle quantum many-body problems, such as discriminating phases of matter, analyzing phase transitions, and addressing the inverse Hamiltonian problem.
- Physics-inspired algorithms for machine learning and neural networks, such as extensions of Boltzmann machines (classical statistical mechanical learning) and connections between deep learning, the renormalization group, and tensor networks/MERA.
- 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.