def init_comp(self):
"""
Convenient to have extra attributes calculated once,
this is best place to put them
"""
dyn = self.parent
#and now the new code
self.full_dim = np.product(self.sub_dims)
self.dimensional_norm = self.full_dim
self.oper_dims = [self.sub_dims, self.sub_dims]
#Simply the local targets with the relevent number of identities tensored
# before and after it.
# We only ever need the .dag() of this, so only storing that.
self.large_local_targs_dag = []
for sub_sys in range(self.num_sub_sys):
large_local_targ = []
for k in range(sub_sys):
large_local_targ.append(identity(self.sub_dims[k]))
large_local_targ.append(self.U_local_targs[sub_sys])
for k in range(sub_sys + 1, self.num_sub_sys):
large_local_targ.append(identity(self.sub_dims[k]))
large_local_targ = reduce(tensor, large_local_targ)
if dyn.oper_dtype == Qobj:
self.large_local_targs_dag.append(large_local_targ.dag())
else:
self.large_local_targs_dag.append(
large_local_targ.dag().full())
# Calculate the permutation matrices for the partial trace
self.ptrace_perms = {}
for sub_sys in range(self.num_sub_sys):
self.ptrace_perms[sub_sys] = calc_perm(self.oper_dims, sub_sys)
def _ptrace(self, sys, sel):
if isinstance(sel, int):
perm = self.ptrace_perms[sel]
elif len(sel) == 1:
perm = self.ptrace_perms[sel[0]]
else:
perm = self.ptrace_perms.get(str(sel), None)
if perm is None:
perm = calc_perm(self.oper_dims, sel)
self.ptrace_perms[str(sel)] = perm
return partial_trace(sys, self.oper_dims, sel, perm=perm)[0]