Change order of arguments to be consistent across different quantum modules

Author: ghanem-nv

examples/cuquantum/qaoa.py

@@ -50,8 +50,8 @@ def __init__(self, n_wires, input_graph, n_layers):
             hamiltonian[pauli_string] = 0.5
 
         backend = CuTensorNetworkBackend(TNConfig(num_hyper_samples=10))
-        self.energy = QuantumExpectation(self.circuit, [hamiltonian], backend)
-        self.sampling = QuantumSampling(self.circuit, 100, backend)
+        self.energy = QuantumExpectation(self.circuit, backend, [hamiltonian])
+        self.sampling = QuantumSampling(self.circuit, backend, 100)
 
     def forward(self):
         start_time = torch.cuda.Event(enable_timing=True)

torchquantum/plugin/cuquantum/amplitude.py

@@ -18,11 +18,11 @@ class QuantumAmplitude(nn.Module):
 
     Args:
         circuit: The quantum circuit that prepares the state.
-        bitstrings: List of bitstrings whose amplitudes to compute.
         backend: The quantum backend to use for computation.
+        bitstrings: List of bitstrings whose amplitudes to compute.
     """
 
-    def __init__(self, circuit: ParameterizedQuantumCircuit, bitstrings: List[str], backend: QuantumBackend):
+    def __init__(self, circuit: ParameterizedQuantumCircuit, backend: QuantumBackend, bitstrings: List[str]):
         super().__init__()
         self._circuit = circuit.copy()
         self._bitstrings = bitstrings.copy()

torchquantum/plugin/cuquantum/expectation.py

@@ -19,18 +19,18 @@ class QuantumExpectation(nn.Module):
 
     Args:
         circuit: The quantum circuit that prepares the state.
+        backend: The quantum backend to use for computation.
         pauli_ops: List of Pauli operators to compute expectations for. Each Pauli operator can be either:
             - A single Pauli string specifying the pauli operator for each qubit ("I", "X", "Y", or "Z").
             - A linear combination of Pauli strings specified as a dictionary mapping each single Pauli string to
               its corresponding coefficient.
-        backend: The quantum backend to use for computation.
     """
 
     def __init__(
         self,
         circuit: ParameterizedQuantumCircuit,
-        pauli_ops: Union[List[str], Dict[str, float]],
         backend: QuantumBackend,
+        pauli_ops: Union[List[str], Dict[str, float]],
     ):
         super().__init__()
         self._circuit = circuit.copy()

torchquantum/plugin/cuquantum/sampling.py

@@ -14,17 +14,23 @@
 class QuantumSampling(nn.Module):
     """A PyTorch module for sampling from quantum states.
 
-    This module generates samples from the quantum state prepared by a given quantum circuit. It can sample from all 
+    This module generates samples from the quantum state prepared by a given quantum circuit. It can sample from all
     qubits or a specified subset of qubits.
 
     Args:
         circuit: The quantum circuit that prepares the state.
-        n_samples: Number of samples to generate per batch.
         backend: The quantum backend to use for computation.
+        n_samples: Number of samples to generate per batch.
         wires: Optional list of wires/qubits to sample from. If not provided, all wires/qubits are sampled from.
     """
-    
-    def __init__(self, circuit:ParameterizedQuantumCircuit, n_samples: int, backend: QuantumBackend, wires: Optional[List[int]]=None):
+
+    def __init__(
+        self,
+        circuit: ParameterizedQuantumCircuit,
+        backend: QuantumBackend,        
+        n_samples: int,
+        wires: Optional[List[int]] = None,
+    ):
         super().__init__()
         self.circuit = circuit
         self.n_samples = n_samples