docs for error part

Author: yutuer21

docs/source/quickstart.rst

@@ -450,24 +450,26 @@ For the Monte Carlo trajectory noise simulator, the unitary Kraus channel can be
 
 .. code-block:: python
 
-    >>> c = tc.Circuit(2)
-    >>> c.unitary_kraus(tc.channels.depolarizingchannel(0.2, 0.2, 0.2), 0)
-    0.0
-    >>> c.general_kraus(tc.channels.resetchannel(), 1)
-    0.0
-    >>> c.state()
-    array([0.+0.j, 0.+0.j, 0.+1.j, 0.+0.j], dtype=complex64)
+    def noisecircuit(random):
+        c = tc.Circuit(1)
+        c.x(0)
+        c.thermalrelaxation(
+            0,
+            t1=300,
+            t2=400,
+            time=1000,
+            method="ByChoi",
+            excitedstatepopulation=0,
+            status=random,
+        )
+        return c.expectation_ps(z=[0])
 
-    >>> def noisecircuit(X):
-    >>>     c = tc.Circuit(n)
-    >>>     c.x(0)
-    >>>     c.thermalrelaxtion(0, t1 = 300, t2 = 400, time=1000, method = "ByChoi",excitedstatepopulation = 0,status = X)
-    >>>     return c.expectation_ps(z=[0])
-    >>> K = tc.set_backend("tensorflow")
-    >>> noisec_vmap = K.jit(K.vmap(noisecircuit, vectorized_argnums=0))
-    >>> nmc = 10000
-    >>> X = K.implicit_randu(nmc)
-    >>> valuemc = sum(K.numpy(noisec_vmap(X))) / nmc
+
+    K = tc.set_backend("tensorflow")
+    noisec_vmap = K.jit(K.vmap(noisecircuit, vectorized_argnums=0))
+    nmc = 10000
+    random = K.implicit_randu(nmc)
+    valuemc = K.mean(K.numpy(noisec_vmap(random)))
     (0.931+0j)
 
 
@@ -477,14 +479,18 @@ Density matrix simulator ``tc.DMCircuit`` simulates the noise in a full form, bu
 
 .. code-block:: python
 
-    >>> def noisecircuitdm():
-    >>>     dmc = tc.DMCircuit(1)
-    >>>     dmc.x(0)
-    >>>     dmc.thermalrelaxation(0, t1=300, t2=400, time=1000, method="ByChoi", excitedstatepopulation=0)
-    >>>     return dmc.expectation_ps(z=[0])
-    >>> K = tc.set_backend("tensorflow")
-    >>> noisec_jit = K.jit(noisecircuitdm)
-    >>> valuedm = noisec_jit()
+    def noisecircuitdm():
+        dmc = tc.DMCircuit(1)
+        dmc.x(0)
+        dmc.thermalrelaxation(
+            0, t1=300, t2=400, time=1000, method="ByChoi", excitedstatepopulation=0
+        )
+        return dmc.expectation_ps(z=[0])
+
+
+    K = tc.set_backend("tensorflow")
+    noisec_jit = K.jit(noisecircuitdm)
+    valuedm = noisec_jit()
     (0.931+0j)
 
 
@@ -496,10 +502,12 @@ Multiple quantum errors can be added on circuit.
 
     c = tc.Circuit(1)
     c.x(0)
-    c.thermalrelaxation(0,t1 = 300, t2 = 400, time = 1000,method = "ByChoi", excitedstatepopulation = 0)
-    c.generaldepolarizing(0,p = 0.01, num_qubits =1)
-    c.phasedamping(0,gamma=0.2)
-    c.amplitudedamping(0,gamma = 0.25,p = 0.2)
+    c.thermalrelaxation(
+        0, t1=300, t2=400, time=1000, method="ByChoi", excitedstatepopulation=0
+    )
+    c.generaldepolarizing(0, p=0.01, num_qubits=1)
+    c.phasedamping(0, gamma=0.2)
+    c.amplitudedamping(0, gamma=0.25, p=0.2)
     c.reset(0)
     c.expectation_ps(z=[0])
 
@@ -510,16 +518,26 @@ Readout error can be added in experiments for sampling and expectation value cal
 
 .. code-block:: python
 
-    >>> c = tc.Circuit(3)
-    >>> c.X(0)
-    >>> readout_error = []
-    >>> readout_error.append([0.9, 0.75])  # readout error of qubit 0
-    >>> readout_error.append([0.4, 0.7])  # readout error of qubit 1
-    >>> readout_error.append([0.7, 0.9])  # readout error of qubit 2
-    >>> value = c.sample_expectation_ps(z=[0, 1, 2], readout_error=readout_error)
-    tf.Tensor(0.039999977, shape=(), dtype=float32)
-    >>> instance = c.sample(allow_state=True, readout_error=readout_error)
-    (<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 1, 0], dtype=int32)>, <tf.Tensor: shape=(), dtype=float32, numpy=0.315>)
+    c = tc.Circuit(3)
+    c.X(0)
+    readout_error = []
+    readout_error.append([0.9, 0.75])  # readout error of qubit 0
+    readout_error.append([0.4, 0.7])  # readout error of qubit 1
+    readout_error.append([0.7, 0.9])  # readout error of qubit 2
+    value = c.sample_expectation_ps(z=[0, 1, 2], readout_error=readout_error)
+    # tf.Tensor(0.039999977, shape=(), dtype=float32)
+    instances = c.sample(
+        batch=3,
+        allow_state=True,
+        readout_error=readout_error,
+        random_generator=tc.backend.get_random_state(42),
+    )
+    #[(<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 0, 0], dtype=int32)>,
+    # <tf.Tensor: shape=(), dtype=float32, numpy=0.21000001>),
+    #(<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 0, 0], dtype=int32)>,
+    # <tf.Tensor: shape=(), dtype=float32, numpy=0.21000001>),
+    #(<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 0, 1], dtype=int32)>,
+    # <tf.Tensor: shape=(), dtype=float32, numpy=0.09>)]
 
 
 MPS and MPO