pre meeting

presentation/spin_chain/hamiltonian.py

@@ -16,7 +16,7 @@ def Mi(nqbits, i, M):
 
 
 def H_interaction(nqbits):
-    interaction_terms = [Mi(nqbits, i, Z) @ Mi(nqbits, i+1, Z) for i in range(nqbits)]
+    interaction_terms = [Mi(nqbits, i, Z) @ Mi(nqbits, i+1, Z) for i in range(nqbits - 1)]
     return sum(interaction_terms)
 
 def H_field(nqbits, g):

presentation/spin_chain/time_evolution.py

@@ -20,8 +20,8 @@ matplotlib.rcParams.update(
 nqbits = 6
 g = 3
 N_trot = 80
-t_stop = 9
-delta_t = 0.09
+t_stop = 29
+delta_t = 0.1
 qbits = list(range(nqbits))
 
 n_sample = 2200
@@ -49,8 +49,8 @@ for t in np.arange(0, t_stop, delta_t):
 
     errors_sampling.append(bootstrap(result[0], n_sample, n_sample, n_sample // 2, np.average))
 
-    #amplitude = np.sum(np.abs(state._qm_state[measure_coefficient_mask])**2)
-    #amplitudes_qc.append(amplitude)
+    amplitude = np.sum(np.abs(state._qm_state[measure_coefficient_mask])**2)
+    amplitudes_qc.append(amplitude)
 
     # Simulation using matrices
     np_zero_state = np.zeros(2**nqbits)
@@ -68,11 +68,17 @@ print()
 print("done.")
 
 results_qc = np.array(results_qc)
+amplitudes_qc = np.array(amplitudes_qc)
 
-errors_trotter = (np.arange(0, t_stop, delta_t) * g)**3 / N_trot**3
+errors_trotter = (np.arange(0, t_stop, delta_t))**3 / N_trot**3
 errors_sampling = np.array(errors_sampling)
+#errors_sampling = np.abs(results_qc - amplitudes_qc)
+#errors_sampling = (amplitudes_qc * (1 - amplitudes_qc))**2 / n_sample
 
-
+#hm1 = plt.errorbar(np.arange(0, t_stop, delta_t)
+#                , results_qc
+#                , yerr=(errors_sampling + errors_trotter)
+#                , color="red")
 h0 = plt.errorbar(np.arange(0, t_stop, delta_t)
                 , results_qc
                 , yerr=errors_sampling