[minor] add vqe with noise model initial version

examples/simple_vqe/new_simple_vqe_noise_model.py

@@ -0,0 +1,59 @@
+import torchquantum as tq
+
+from torchquantum.algorithms import VQE, Hamiltonian
+from qiskit import QuantumCircuit, transpile
+
+from torchquantum.plugins import qiskit2tq_op_history, op_history2qiskit
+from torchquantum.plugins import QiskitProcessor
+
+from qiskit import IBMQ
+IBMQ.load_account()
+
+if __name__ == "__main__":
+    hamil = Hamiltonian.from_file("./h2.txt")
+
+    # or alternatively, you can use the following code to generate the ops
+    circ = QuantumCircuit(2)
+    circ.x(0)
+    circ.rz(0.1, 1)
+    circ.cx(0, 1)
+    circ.x(0)
+    circ.rz(0.1, 1)
+    circ.cx(0, 1)
+
+    ops = qiskit2tq_op_history(circ)
+    print(ops)
+
+    ansatz = tq.QuantumModule.from_op_history(ops)
+
+    noise_model_tq = tq.NoiseModelTQ(
+        noise_model_name="ibmq_quito",
+    )
+
+    noise_model_tq.v_c_reg_mapping = {'v2c': {0:0, 1:1, 2:2, 3:3, 4:4, 5:5, 6:6},
+                                      'c2v': {0:0, 1:1, 2:2, 3:3, 4:4, 5:5, 6:6},
+                                      }
+    noise_model_tq.p_c_reg_mapping = {'p2c': {0:0, 1:1, 2:2, 3:3, 4:4, 5:5, 6:6},
+                                      'c2p': {0:0, 1:1, 2:2, 3:3, 4:4, 5:5, 6:6},
+                                      }
+    noise_model_tq.p_v_reg_mapping ={'p2v': {0:0, 1:1, 2:2, 3:3, 4:4, 5:5, 6:6},
+                                      'v2p': {0:0, 1:1, 2:2, 3:3, 4:4, 5:5, 6:6},
+                                      }
+
+    ansatz.set_noise_model_tq(noise_model_tq)
+
+    configs = {
+        "n_epochs": 10,
+        "n_steps": 100,
+        "optimizer": "Adam",
+        "scheduler": "CosineAnnealingLR",
+        "lr": 0.1,
+        "device": "cuda",
+    }
+
+    vqe = VQE(
+        hamil=hamil,
+        ansatz=ansatz,
+        train_configs=configs,
+    )
+    expval = vqe.train()