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Quantum field theory (QFT) is a universal language for theoretical physics describing the Standard Model gravity early universe inflation and condensed matter phenomena such as phase transitions superconductors and quantum Hall fluids. A triumph of 20th century physics was to understand weakly coupled QFTs: theories whose interactions can be treated as small perturbations of otherwise freely moving particles. However weakly coupled QFTs represent a tiny island in an ocean of possibilities. They cannot capture many of the most interesting and important physical phenomena from the strong nuclear force to high temperature superconductivity.The critical challenge for the 21st century is to understand and solve strongly coupled QFTs. Meeting this challenge will require new physical insight new mathematics and new computational tools. Our collaboration combines deep knowledge of novel non-perturbative techniques with a concrete plan for attacking the problem of strong coupling. The starting point is the astonishing discovery that in numerous physical systems there is a unique quantum field theory consistent with general principles of symmetry and quantum mechanics. By analyzing the full implications of these general principles one can make sharp predictions for physical observables without resorting to approximations.This strategy is called the Bootstrap the topic of this three week program.

Machine learning techniques are rapidly being adopted into the field of quantum many-body physics including condensed matter theory experiment and quantum information science.  The steady increase in data being produced by highly-controlled quantum experiments brings the potential of machine learning algorithms to the forefront of scientific advancement.  Particularly exciting is the prospect of using machine learning for the discovery and design of quantum materials devices and computers.  In order to make progress the field must address a number of fundamental questions related to the challenges of studying many-body quantum mechanics using classical computing algorithms and hardware. The goal of this conference is to bring together experts in computational physics machine learning and quantum information to make headway on a number of related topics including: Data-drive quantum state reconstruction Machine learning strategies for quantum error correction Neural-network based wavefunctions Near-term prospects for data from quantum devices Machine learning for quantum algorithm discovery Registration for this event is now closed

On Thursday June 13 the Institute for Quantum Computing (IQC) and Perimeter Institute for Theoretical Physics (PI) will participate in the one-day Many-Body States and Dynamics Workshop II. The goal of the workshop is to describe ongoing efforts to experimentally realize quantum many-body states and dynamics and discuss interesting classes of states and dynamics that could be targeted. Experimentalists working on several platforms (such as photons atom and ion traps superconducting qubits exciton-polaritons or NMR) and theoreticians specialized in many-body theory (entanglement topological order gauge theories criticality chaos error correction holography) and numerical simulations (exact diagonalization Monte Carlo DMRG tensor networks) will meet for a morning workshop to identify and discuss common interests.