Optimising, Renormalising, Evolving and Quantising Tensor Networks

International Workshop
18 - 22 June 2018

In the 25 years since the introduction of the density matrix renormalization group, tensor networks have become canonical quantum physics. The methods and applications have ramified, generating many new insights along the way. This meeting will explore these developments under several broad, physically-motivated themes, including chaos and hydrodynamics in quantum thermalisation, quantizing tensor networks, representing and evolving open quantum systems, renormalizing tensor networks, new network structures and connections to machine learning.

The aim is to advance both conceptual understanding of tensor networks representations of quantum Hamiltonians and wavefunctions, and practical algorithms for the study of condensed matter, atom and molecular optics, and quantum information. We encourage participants to step back from technical details and emphasise new insights, their relation to other approaches, and emerging opportunities. The recognition of close parallels in the mathematical underpinnings of machine learning, and modeling the structure and dynamics of quantum entanglement through tensor networks is an important development. This meeting will take advantage of a back-to-back meeting on machine learning in condensed matter physics.

• Chaos and hydrodynamics in quantum thermalisation
• Quantizing tensor networks
• Out-of-equilibrium quantum dynamics
• Representing and evolving open quantum systems
• Renormalising tensor networks
• Connections between MERA and AdS/CFT
• Tensor network Monte Carlo
• New network structures
• Connections between tensor networks and machine learning