Add example showing how to use OpenFermion operators as quantum kernel arguments

# Run this from the command line like this

#

# python3 exp_fermion.py -shots 100

from qcor import *

from openfermion.ops import FermionOperator as FOp

@qjit

def ansatz(q: qreg, x: List[float], exp_args: List[FermionOperator]):

    X(q[0])

    for i, exp_arg in enumerate(exp_args):

        exp_i_theta(q, x[i], exp_args[i])

# Create OpenFermion operators for our quantum kernel...

exp_args_openfermion = [FOp('0^ 1') - FOp('1^ 0'), FOp('0^ 2') - FOp('2^ 0')]

# We need to translate OpenFermion ops into qcor Operators to use with kernels...

exp_args_qcor = [createOperator('fermion', fop) for fop in exp_args_openfermion]

# Custom arg_translator in a Pythonic way

def ansatz_translate(self, q: qreg, x: List[float]):

    ret_dict = {}    

    ret_dict["q"] = q

    ret_dict["x"] = x

    ret_dict["exp_args"] = exp_args_qcor

    return ret_dict

ansatz.translate = MethodType(ansatz_translate, qjit)

# Define the hamiltonian

H = createOperator(

    '5.907 - 2.1433 X0X1 - 2.1433 Y0Y1 + .21829 Z0 - 6.125 Z1 + 9.625 - 9.625 Z2 - 3.91 X1 X2 - 3.91 Y1 Y2')

# Create the ObjectiveFunction, specify central finite diff gradient

obj = createObjectiveFunction(

    ansatz, H, 2, {'gradient-strategy': 'central', 'step': 1e-2})

# create the lbfgs optimizer

optimizer = createOptimizer('nlopt', {'algorithm': 'l-bfgs', 'ftol': 1e-3})

# Run VQE...

results = optimizer.optimize(obj)

print(results)