Fisher information classifier¶

Author(s): Luis Mantilla, Polina Feldmann, Dmytro Bondarenko

Note

This tutorial is under construction

Here, we will learn now to perform a classification of quantum states according to their Fisher information. First, we generate a dataset of states

def gen_states_polina(numstates):
   st1a = np.array([1,0,0,0])
   st1b = np.array([0,0,0,1]) # must be orthogonal
   randtheta1 = np.pi / 2 * np.random.rand(numstates)
   randphi1 = 2 * np.pi * np.random.rand(numstates)

   states1 = np.zeros((numstates, 4), dtype='complex')
   for k in range(numstates):
      th = randtheta1[k]
      phi = randphi1[k]
      states1[k] = np.cos(th) * st1a + np.exp(1j * phi) * np.sin(th) * st1b

   st2a = np.array([1,1,1,1]) / 2.
   st2b = np.array([1,-1,-1,1]) / 2.
   randtheta2 = np.pi / 2 * np.random.rand(numstates)
   randphi2 = 2 * np.pi * np.random.rand(numstates)

   states2 = np.zeros((numstates, 4), dtype='complex')
   for k in range(numstates):
      th = randtheta2[k]
      phi = randphi2[k]
      states2[k] = np.cos(th) * st2a + np.exp(1j * phi) * np.sin(th) * st2b

   states = np.concatenate((states1, states2))
   return states