def diff(t, y):
"""This function will not change the arrays in tensor network.
"""
tensor.set_array(np.reshape(y, tensor.shape))
ans = np.zeros_like(y)
for n in self.term_visitor():
ans += np.reshape(self._single_diff(tensor, n), -1)
return ans
def diff(t, y):
"""This function will not change the arrays in tensor network.
"""
origin = tensor.array
tensor.set_array(np.reshape(y, tensor.shape))
ans = np.zeros_like(y)
self.time = t if not imaginary else None
for n in self.term_visitor(use_cache=True):
ans += np.reshape(self._single_eom(tensor, n, cache=cache), -1)
if self.init_energy is not None:
ans -= self.init_energy * y
ans /= self.coefficient()
tensor.set_array(origin)
return ans
def _matvec(self, vec):
v = np.reshape(vec, self.io_sizes)
ans = np.zeros_like(v)
for i, h_i in enumerate(self.h_list):
v_i = np.swapaxes(v, -1, i)
size_i = (self.io_sizes[:i] + self.io_sizes[i + 1:] + [self.io_sizes[i]])
v_i = np.reshape(v_i, (-1, self.io_sizes[i]))
tmp = np.array(list(map(h_i.dot, v_i)))
tmp = np.reshape(tmp, size_i)
ans += np.swapaxes(tmp, -1, i)
ans = np.reshape(ans, -1)
if self.v_rst is not None:
ans = ans + self.v_rst * vec
return ans
def lowering(self, vec):
self.check_vec(vec)
ans = np.zeros_like(vec)
tmp = np.array([np.sqrt(i) for i in range(self.dim)]) * vec
ans[:-1] = tmp[1:]
return ans
def raising(self, vec):
self.check_vec(vec)
ans = np.zeros_like(vec)
ans[1:] = vec[:-1]
ans *= np.array([np.sqrt(i) for i in range(self.dim)])
return ans
def _matvec(self, vec):
ans = np.zeros_like(vec, dtype=complex)
for i in range(self.n_terms):
ans += self.term_func(i, vec)
return ans