A python framework for qubit simulations and optimal control. This code provides classes to assist in setting up Hamiltonians for multi-qubit superconducting qubit simulations. After setting up the system there is both a pure python and a C++ implementation of an open and closed system simulator and an optimal control module based on the GRAPE algorithm. The C++ backend relies on the Eigen library for matrix manipulations and eignsolvers.

These are the latest versions I have worked with. Nearby versions should be just fine too.

• Python 2.7.4
• numpy 1.9
• scipy 0.13
• Cython 0.20 (for C++ backend) (note Cython 0.16-0.19 had a bug that broke assigning to std::vector)
• Eigen 3.2 (for C++ backend)
• scons (for C++ backend)

The pure python implementation should always work as a fall back. However, particularly for small systems, the C++ back-end can be significantly faster. For better or worse, the build script is written in scons. You must pass it the path to the eigen install.

cd PySim
#Clean any old build files
scons -c
#Build
scons EIGENDIR=/path/to/eigen

The SimulatorTests.py in the tests folder gives some ideas of how to get going.

More examples to come...