Minimum hybrid time

For benchmarking purposes we would like to investigate how much the quality of the solution of the hybrid solver improves given it is given more time to work on the problem. We want to do this by running random generated problem instances for different time limits.

The problem that we face is that the minimum_time_limit, specified here results already yields the optimal solution to our problems. 

Is there a way to set the['minimum_time_limit'] by hand, so that we can better study the output of the hybrid solver when it has to little time to produce good results? Or alternatively, what would be even better, is to be able to inspect current best solution over time. Although I don't think that is possible 




  • Hello,

    Unfortunately it is not possible to change the minimum_time_limit, as this is the minimum amount of time needed to run a given problem on a Hybrid Solver.

    There also isn't a way to check the current best solution over time.

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  • Hi Robert!

    For your research you might consider using the open-source package dwave-hybrid. This package is a framework for building hybrid algorithms, and comes with some initial building blocks to get you started.

    You can learn more about the package in our documentation ( or through our collection of Jupyter notebooks (  These open-source tools allow you to use all of the QPU parameters in your subproblem sampling step (including anneal time, num reads, etc) as well as the timing for the classical components, and so you can manually control exactly how long your algorithm runs for.

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