diff --git a/examples/stabilizer/run_all_narma.sh b/examples/stabilizer/run_all_narma.sh
new file mode 100644
index 0000000..b5c68ad
--- /dev/null
+++ b/examples/stabilizer/run_all_narma.sh
@@ -0,0 +1,35 @@
+#!/bin/bash
+
+for i in {0..4}
+do
+    sbatch run_reservoir_narma.sh 3 2 20 classical None $i 1 narma 3
+    sbatch run_reservoir_narma.sh 3 2 20 quantum_part None $i 1 narma 3
+    sbatch run_reservoir_narma.sh 3 2 20 quantum_stab None $i 1 narma 3
+    sbatch run_reservoir_narma.sh 3 2 20 quantum_part True $i 1 narma 3
+    sbatch run_reservoir_narma.sh 3 2 20 quantum_stab True $i 1 narma 3
+
+    for j in {2..3}
+    do
+        sbatch run_reservoir_narma.sh 4 $j 20 classical None $i 1 narma 3
+        sbatch run_reservoir_narma.sh 4 $j 20 quantum_part None $i 1 narma 3
+        sbatch run_reservoir_narma.sh 4 $j 20 quantum_stab None $i 1 narma 3 
+        sbatch run_reservoir_narma.sh 4 $j 20 quantum_part True $i 1 narma 3
+        sbatch run_reservoir_narma.sh 4 $j 20 quantum_stab True $i 1 narma 3
+    done
+
+
+    for j in {2..4}
+    do
+        sbatch run_reservoir_narma.sh 5 $j 20 classical None $i 1 narma 5
+        sbatch run_reservoir_narma.sh 5 $j 20 quantum_part None $i 1 narma 5
+        sbatch run_reservoir_narma.sh 5 $j 20 quantum_stab None $i 1 narma 5
+        sbatch run_reservoir_narma.sh 5 $j 20 quantum_part True $i 1 narma 5
+        sbatch run_reservoir_narma.sh 5 $j 20 quantum_stab True $i 1 narma 5 
+    done
+
+done
+
+sbatch run_reservoir_narma.sh 3 2 20 quantum_part None $i 0 narma 5
+sbatch run_reservoir_narma.sh 3 2 20 quantum_stab None $i 0 narma 5
+sbatch run_reservoir_narma.sh 4 3 20 quantum_part None $i 0 narma 5
+sbatch run_reservoir_narma.sh 4 3 20 quantum_stab None $i 0 narma 5
\ No newline at end of file
diff --git a/examples/stabilizer/run_reservoir_narma.py b/examples/stabilizer/run_reservoir_narma.py
new file mode 100644
index 0000000..888454b
--- /dev/null
+++ b/examples/stabilizer/run_reservoir_narma.py
@@ -0,0 +1,177 @@
+import sys
+from reservoirpy.datasets import logistic_map, narma
+import numpy as np
+import reservoirpy as rpy
+import pickle
+from sklearn.model_selection import TimeSeriesSplit
+from sklearn.linear_model import LinearRegression, Ridge
+from quantumreservoirpy.util import create_shifted_array
+from qiskit_aer.noise import (NoiseModel, QuantumError, ReadoutError,
+    pauli_error, depolarizing_error, thermal_relaxation_error)
+from itertools import combinations
+from quantumreservoirpy.util import randomIsing
+from reservoirpy.nodes import Reservoir
+from quantumreservoirpy.partialmeasurement import PartialMeasurement
+from quantumreservoirpy.stabilizer import Stabilizer
+from qiskit_aer import AerSimulator
+
+def fit_model(model, res_states, series, WARMUP, timeplex=1):
+    warmup = int(len(series) * WARMUP)
+
+    X = res_states[warmup:-1]
+    y = series[warmup + 1 :]
+
+    if timeplex > 1:
+        X = create_shifted_array(X, timeplex)
+    model.fit(X, y)
+
+    return model, X, y
+
+
+def run_prediction(model, res_states, timeplex=1):
+
+    X = np.copy(res_states)
+
+    if timeplex > 1:
+        X = create_shifted_array(X, timeplex)
+    X = X[-1,:]
+    X = X.reshape((1, -1))
+    return model.predict(X)
+
+def henon1d(n, a=1.4, b=0.3):
+    ts=[0,0]
+    for i in range(2,n+2):
+        ts.append(1 - a*ts[i-1]**2 + b*ts[i-2])
+    return np.array(ts[2:])
+
+
+def main(num_qubits, num_meas, num_reservoirs, method, noise, lentrain, decode, casename, order, timeplex=10, degree=None):
+
+    if not degree:
+        degree = num_meas
+    #degree = min(degree, 3)
+
+    num_neurons=num_reservoirs*(2**num_meas-1)
+
+    if casename == "henon":
+        ts = henon1d(200)
+    elif casename == "logistic":
+        ts=logistic_map(200, r=3.9, x0=0.5).flatten()
+    elif casename == 'narma':
+        u = np.loadtxt('u_driver.txt', dtype=float)
+        u = u[:(200+order)].reshape(-1, 1)
+        ts = narma(n_timesteps=200, order=order, u=u).flatten()
+
+    #ts=narma(200).flatten()
+
+    string_identifier="casename"+str(casename)+"_num_qubits"+str(num_qubits)+"_num_meas"+str(num_meas)
+    string_identifier+="_degree"+str(degree)+"_num_reservoirs"+str(num_reservoirs)+"_timeplex"+str(timeplex)
+    string_identifier+="_method"+str(method)+"_noise"+str(noise)
+    if not decode:
+        string_identifier+="_decodeFalse"
+
+    print(string_identifier, " number of neurons/observables=",num_neurons )
+
+
+
+    linreg = Ridge(alpha=1e-7)#LinearRegression()
+
+
+
+    if noise == "None":
+        noise_model = None
+    else:
+# Create an empty noise model
+        noise_model = NoiseModel()
+# Add depolarizing error to all single qubit u1, u2, u3 gates
+        error = depolarizing_error(0.01, 1)
+        noise_model.add_all_qubit_quantum_error(error, ['u1', 'u2', 'u3'])
+        error = depolarizing_error(0.1, 2)
+        noise_model.add_all_qubit_quantum_error(error, ['cx'])
+
+    WARMUP=0.3
+
+    with open("isingparams_"+"num_qubits"+str(num_qubits)+"_num_reservoirs"+str(num_reservoirs)+".pickle","rb") as f:
+        isingparams = pickle.load(f)
+
+
+
+    if method == "classical":
+        res = Reservoir(num_neurons, lr=0.5, sr=0.9)
+    elif method == "quantum_part":
+        res = PartialMeasurement(num_qubits, num_meas, backend = AerSimulator(noise_model=noise_model),\
+                                 degree=degree, num_reservoirs=num_reservoirs, isingparams=isingparams,decode=decode)
+    elif method == "quantum_stab":
+        res = Stabilizer(num_qubits, num_meas, backend = AerSimulator(noise_model=noise_model),\
+                         degree=degree, num_reservoirs=num_reservoirs, isingparams=isingparams,decode=decode)
+
+    tscv = TimeSeriesSplit()
+
+    for i, (train_index, test_index) in enumerate(tscv.split(ts)):
+        if not i == lentrain:
+            continue
+
+        X_train=ts[train_index]
+        X_test=ts[test_index]
+        num_pred = len(test_index)
+
+        with open("X_train"+str(i)+"_"+string_identifier+".pickle","wb") as f:
+            pickle.dump(X_train, f)
+        with open("X_test"+str(i)+"_"+string_identifier+".pickle","wb") as f:
+            pickle.dump(X_test, f)
+        
+        if method =='classical':
+            states = res.run(X_train.reshape(-1, 1), reset=True)
+            linreg, X, y = fit_model(linreg, states, X_train, WARMUP, 1)
+        else:
+            states = res.run(timeseries=X_train, shots=1e3, precision=1e-2)
+            linreg, X, y = fit_model(linreg, states, X_train, WARMUP, timeplex)
+
+        score = linreg.score(X, y)
+        print("score[",method,"]=", score)
+        
+        with open("score"+str(i)+"_"+string_identifier+".pickle","wb") as f:
+            pickle.dump(score, f)
+
+#        if not method == "classical":
+        with open("state"+str(i)+"_"+string_identifier+".pickle","wb") as f:
+            pickle.dump(states, f)
+
+        firsttime=True
+        prediction = X_train
+
+        for j in range(num_pred):
+            print(j,"/",num_pred)
+            if method=='classical':
+                if firsttime:
+                    states = res.state()
+                else:
+                    states = res.run(prediction[-1])
+                tmp = run_prediction(linreg, states, 1)
+            else:
+                states = res.run(prediction[-2*int(timeplex):], shots=1e3, precision=1e-2)
+                tmp = run_prediction(linreg, states, timeplex)
+            
+            prediction = np.append(prediction,tmp)
+            
+            firsttime=False
+
+        with open("prediction"+str(i)+"_"+string_identifier+".pickle","wb") as f:
+            pickle.dump(prediction, f)
+
+
+
+if __name__ == "__main__":
+    num_qubits = int(sys.argv[1])
+    num_meas = int(sys.argv[2])
+    num_reservoirs = int(sys.argv[3])
+    method = str(sys.argv[4])
+    noise = str(sys.argv[5])
+    lentrain = int(sys.argv[6])
+    decode = bool(int(sys.argv[7]))
+    casename = str(sys.argv[8])
+    order = int(sys.argv[9])
+
+    print("Running:", num_qubits, num_meas, num_reservoirs, method, noise, lentrain, decode, casename, order)
+    main(num_qubits, num_meas, num_reservoirs, method, noise, lentrain, decode, casename, order)
+
diff --git a/examples/stabilizer/run_reservoir_narma.sh b/examples/stabilizer/run_reservoir_narma.sh
new file mode 100644
index 0000000..e6b1bd1
--- /dev/null
+++ b/examples/stabilizer/run_reservoir_narma.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --job-name=random_sample
+#              d-hh:mm:ss
+#SBATCH --time=30-00:00:00
+#SBATCH --output=/home/rubenp/quantumreservoirpy_vivaldi/%j.out
+#SBATCH --nodes=1
+#SBATCH --tasks-per-node=1
+#SBATCH --cpus-per-task=1
+
+source qiskit/bin/activate
+
+python run_reservoir_narma.py "$1" "$2" "$3" "$4" "$5" "$6" "$7" "$8" "$9"
+
diff --git a/examples/stabilizer/u_driver.txt b/examples/stabilizer/u_driver.txt
new file mode 100644
index 0000000..e0089cb
--- /dev/null
+++ b/examples/stabilizer/u_driver.txt
@@ -0,0 +1,1000 @@
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