This repo includes a basic example implemented in PennyLane highlighting the tailgating procedure. I also make use the autohf library (Github repo): the prototype version of the pennylane.hf module, as well as the bigvqe library (Github repo): a package for faster computation of sparse fermionic Hamiltonians.
This simply allows us to return the value of the gradient, the value of the cost function, and the updated parameters simultaneously when performing gradient descent.
To install this package, run:
python3 setup.py build_ext --inplace install --user
Please cite our paper!