This is a software package to support the mapping of combinatorial optimization problems to quantum computing interfaces via QUBO and Ising problems.
This is a software package to support the mapping of combinatorial optimization problems\n to quantum computing interfaces via QUBO and Ising problems.
The full documentation can be found here.
The combinatorial optimization problem is rewritten as a single (quadratic unconstrained binary) objective function.
The usual way to build it up is to use the following structure:
In the Instance we describe the problem defining parameters.
From the instance we construct the ObjectiveTerms containing the different contributions to the objective function,
in particular the ones derived from problem constraints.
The objective terms can be implemented directly or derived from a ConstrainedObjective
which contains the objective function and ConstraintBinary's.
The objective terms can now be used to create the Objective
by summing up the single terms weighted with a certain so-called penalty weight.
All objective objects contain Polynomials representing the functions.
There are special polynomials PolyBinary and PolyIsing
which take advantage of the restriction to either binary (0 or 1) or spin (-1 or 1) variables.
The ScipModel is an interface to the classical MILP solver SCIP
which can solve a ConstrainedObjective or a (small enough) Objective for comparison.
In Solution we store not only the optimal variable assignment but also further information,
like runtime etc., which are obtained during the solving process.
Furthermore, we have the HardwareAdjacency and the Embedding,
which are useful when dealing with actual hardware.
All mentioned objects also provide methods to store and load their information in and from hdf5 files.
