Me too. I am doing data science/analytics after 8 years of theoretical/computational physics (I have a bunch of papers in the pipeline, hope I can get them out [!]) I use Julia. But, I am learning and using python for some things now because: I have to work with other people; we are pretty sure what the python API and ecosystem will look like in two years, etc. (The depth and breadth of python is great. But, immediately I am sorely missing the ability to “talk about types” at the core of the language.)
Exact diagonalizations of QM models like @mason is doing are a bread-and-butter task for which Julia is well suited. In fact, for a very large number of physical science projects, the code is shared by one or a few people. And its lifetime is measured in months. Or maybe a couple of years. And there is usually no PHB vetoing your language choice. This makes physical science a great vector for Julia. In the past two years I did get one new postdoc to try Julia for a project. I offered basically unlimited support. I don’t think it was language partisanship that prevented uptake, rather perceived practicality. I gave a Julia talk a the Barcelona supercomputer center too. There is obviously a great interest. The room was full, which doesn’t happen often; I hope someone gave it a try. My contacts there have not had time to try it for anything yet, but have a genuine interest. My guess is that language adoption has some features of dynamic growth of a scale-free structure. There won’t be a critical-mass event, but adoption will still be in a sense fast.