The guiding focus of the workshop EVONET18 was the study of the varied methods and applications of tensor networks in quantum physics and beyond. In particular, the meeting was intended to explore key directions organised under several broad, physically-motivated themes. These included, chaos and hydrodynamics in quantum thermalisation, quantizing tensor networks, representing and evolving open quantum systems, renormalizing tensor networks, and new network structures. Another key goal of the workshop was to intensify research on connections between tensor networks and machine learning networks, to which end, the meeting benefitted from the machine learning conference organised in the following week.

There were several key participants of the workshop. Here we were lucky to have Miles Stoudenmire present the colloquium, who gave an excellent overview of both the fields of tensor networks and machine learning. This played a crucial role during the remainder of the workshop as the participants could relate to each other via the common vocabulary Miles introduced. Further significant participants included Robert Konik who gave a guiding talk on thermalization, Ehud Altman who gave an important contribution on hydrodynamics of thermalization, and Zohar Ringel who gave an excellent talk on connections between machine learning, tensor networks, and quantum information ideas.

The scientific newcomers provided some of the most interesting and inspiring contributions to the workshop. Here notable talks include those of Gemma De las Cuevas, Andrew James, Andrew Hallam, Sebastian Wetzel, and Thorsten Wahl. The speakers made extra efforts to make their work accessible while also managing to touch on advanced research topics.

The scientific results reported on during EVONET18 provided a broad cross-section of the cutting edge research taking place in the rapidly diversifying field of tensor networks. Several key areas of very active research were discussed thoroughly, including many body localization, hydrodynamics and thermalization, and machine learning. In addition, state-of-the-art numerical methods were intensively reported. Multiple guiding problems were discussed and newly arising research areas at the intersection of machine learning and tensor networks were explored.