I work at a solver company (SAS Institute Inc.) and can probably weigh in on this a little bit.
The problem with saying anything about performance is that there is a lot of variability between instances in mixed integer programming. Naturally that evens out a bit if looking at a longer time frame and more instances. In the past solver developers typically lacked the resources to get data from running a lot of instances (and almost never added randomization). This has been changing in the last few years so benchmarking got better but that meant that the resulting perceived gains got smaller.
What speed-up is it most likely we will see in the next 10 years?
From my experience, and also from talking to other people in my field, I would guess that an average improvement on a big set of instances (thousands) of about factor 1.3 year-to-year is a good year in solver development. This might vary a lot, but that is the number that is in my head.
What are the reasons for a lower rate in the future than what we have seen in the last 30 years?
There was a time in the late 90s and early 2000s in which the solver companies mined the research community for useful results and achieved a lot of speedup. Recently, in my perception, less research is focused on mixed integer linear programming, so there is probably less to mine there.
What are the reasons for a higher rate in the future than what we have seen in the last 30 years?
More computing resources allow for more benchmarking and more experimentation. Also storing more test data is less of a problem than it used to be. There is always a chance that revolutionary new algorithm idea emerges, but I would not hold my breath for that. Solvers are very sophisticated, any significant improvement kind of has to build on what we already have or it will be very time consuming to re-do all the previous work in a different framework.
Which trends and technologies will have an impact on this?
Some people claim that tuning and making decisions in the solver with machine learning will make a huge impact, but I personally am skeptical and am not aware of a real breakthrough result there. More cores and more parallelization are also not going to help, except maybe from improvements in compilers and programming languages, such as micro-threading and the like. From all information I have, quantum computing is many, many years away from being reliable and accurate enough to matter for linear algebra heavy applications, if it ever gets there, so I don't think it will matter in your 10 year timeframe. The best bet is improvements from theoretical results that are successfully transferred into working code. In the past that was what made solvers faster, the last big innovation there, symmetry handling through orbital branching, was such a deeply mathematical result applied in a clever way to solve certain instances.