diff --git a/presentation/spin_chain/time_evolution.py b/presentation/spin_chain/time_evolution.py
index 88a78d4..fef4539 100644
--- a/presentation/spin_chain/time_evolution.py
+++ b/presentation/spin_chain/time_evolution.py
@@ -33,6 +33,7 @@ measure_coefficient_mask = [False if (i & measure) else True for i in range(2**n
 results_qc = []
 results_np = []
 errors_sampling = []
+amplitudes_qc = []
 
 print()
 for t in np.arange(0, t_stop, delta_t):
@@ -46,10 +47,10 @@ for t in np.arange(0, t_stop, delta_t):
     result = sample(state, measure, n_sample)
     results_qc.append(result[0] / n_sample)
 
-    errors_sampling.append(bootstrap(result[0], n_sample, n_sample // 4, n_sample // 10, np.average))
+    errors_sampling.append(bootstrap(result[0], n_sample, n_sample, n_sample // 2, np.average))
 
-    #amplitude = np.sqrt(np.sum(np.abs(state._qm_state[measure_coefficient_mask])**2))
-    #results_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)
@@ -68,16 +69,17 @@ print("done.")
 
 results_qc = np.array(results_qc)
 
-errors_trotter = (np.arange(0, t_stop, delta_t) * g)**2 / N_trot**2
+errors_trotter = (np.arange(0, t_stop, delta_t) * g)**3 / N_trot**3
 errors_sampling = np.array(errors_sampling)
 
 
 h0 = plt.errorbar(np.arange(0, t_stop, delta_t)
                 , results_qc
-                , yerr=(errors_trotter + errors_sampling)
+                , yerr=errors_sampling
                 , label=f"Quantum computing ({n_sample} samples, {N_trot} trotterization steps)"
                 , )
 h1, = plt.plot(np.arange(0, t_stop, delta_t), results_np, label="Classical simulation using explicit transfer matrix")
+#h2, = plt.plot(np.arange(0, t_stop, delta_t), amplitudes_qc, label="QC amplitude")
 plt.xlabel("t")
 plt.ylabel(r"$|0\rangle$ probability amplitude for second spin")
 plt.title(f"{nqbits} site spin chain with g={g} coupling to external field")