def c_der(self):
n = self.num_of_inds
factor = OpSum(i_op * Op(sigma4bar(n + 1, 0, -1)))
f = Op(
Tensor(self.c_name, [0] + list(range(-2, -n - 1, -1)),
is_field=True,
dimension=1.5,
statistics=fermion))
return factor * f.derivative(n + 1)
def quadratic_terms(self):
n = self.num_of_inds
f1 = Op(
Tensor(self.name,
list(range(n)),
is_field=True,
dimension=1,
statistics=boson))
f2 = Op(
Tensor(self.name, [n] + list(range(1, n)),
is_field=True,
dimension=1,
statistics=boson))
half = OpSum(number_op(Fraction(1, 2)))
kin1 = -half * f1.derivative(n) * f1.derivative(n)
kin2 = half * f2.derivative(0) * f1.derivative(n)
mass_term = half * OpSum(self.mass_sq * f1 * f1)
return kin1 + kin2 + mass_term
def quadratic_terms(self):
n = self.num_of_inds
f1 = Op(
Tensor(self.name,
list(range(n)),
is_field=True,
dimension=1,
statistics=boson))
f1c = Op(
Tensor(self.c_name,
list(range(n)),
is_field=True,
dimension=1,
statistics=boson))
f2c = Op(
Tensor(self.c_name, [n] + list(range(1, n)),
is_field=True,
dimension=1,
statistics=boson))
kin1 = -f1c.derivative(n) * f1.derivative(n)
kin2 = f2c.derivative(0) * f1.derivative(n)
mass_term = OpSum(self.mass_sq * f1c * f1)
return kin1 + kin2 + mass_term