Added C4X Gate

test/operators/test_op.py

@@ -35,6 +35,7 @@
     # {'qiskit': qiskit_gate.?, 'tq': tq.SSWAP},
     {"qiskit": qiskit_gate.CSwapGate, "tq": tq.CSWAP},
     {"qiskit": qiskit_gate.CCXGate, "tq": tq.Toffoli},
+    {"qiskit": qiskit_gate.C4XGate, "tq": tq.C4X},
     {"qiskit": qiskit_gate.PhaseGate, "tq": tq.PhaseShift},
     # {'qiskit': qiskit_gate.?, 'tq': tq.Rot},
     # {'qiskit': qiskit_gate.?, 'tq': tq.MultiRZ},

torchquantum/functional.py

@@ -36,6 +36,7 @@
     "sswap",
     "cswap",
     "toffoli",
+    "c4x",
     "multicnot",
     "multixcnot",
     "rx",
@@ -1100,6 +1101,24 @@ def singleexcitation_matrix(params):
 
     return matrix.squeeze(0)
 
+def c4x_matrix():
+    """Compute unitary matrix for C4X gate.
+
+    Args:
+        None
+
+    Returns:
+        torch.Tensor: The computed unitary matrix.
+
+    """
+    mat = torch.eye(32, dtype=C_DTYPE)
+    mat[30][30] = 0
+    mat[30][31] = 1
+    mat[31][31] = 0
+    mat[31][30] = 1
+
+    return mat
+
 
 mat_dict = {
     "hadamard": torch.tensor(
@@ -1166,6 +1185,7 @@ def singleexcitation_matrix(params):
         ],
         dtype=C_DTYPE,
     ),
+    "c4x": c4x_matrix(),
     "ecr": torch.tensor(
         [[0, 0, 1, 1j], [0, 0, 1j, 1], [1, -1j, 0, 0], [-1j, 1, 0, 0]], dtype=C_DTYPE
     )
@@ -2280,6 +2300,53 @@ def toffoli(
     )
 
 
+def c4x(
+    q_device,
+    wires,
+    params=None,
+    n_wires=None,
+    static=False,
+    parent_graph=None,
+    inverse=False,
+    comp_method="bmm",
+):
+    """Perform the c4x gate.
+
+    Args:
+        q_device (tq.QuantumDevice): The QuantumDevice.
+        wires (Union[List[int], int]): Which qubit(s) to apply the gate.
+        params (torch.Tensor, optional): Parameters (if any) of the gate.
+            Default to None.
+        n_wires (int, optional): Number of qubits the gate is applied to.
+            Default to None.
+        static (bool, optional): Whether use static mode computation.
+            Default to False.
+        parent_graph (tq.QuantumGraph, optional): Parent QuantumGraph of
+            current operation. Default to None.
+        inverse (bool, optional): Whether inverse the gate. Default to False.
+        comp_method (bool, optional): Use 'bmm' or 'einsum' method to perform
+        matrix vector multiplication. Default to 'bmm'.
+
+    Returns:
+        None.
+
+    """
+    name = "c4x"
+    mat = mat_dict[name]
+    gate_wrapper(
+        name=name,
+        mat=mat,
+        method=comp_method,
+        q_device=q_device,
+        wires=wires,
+        params=params,
+        n_wires=n_wires,
+        static=static,
+        parent_graph=parent_graph,
+        inverse=inverse,
+    )
+
+
 def phaseshift(
     q_device,
     wires,
@@ -3270,6 +3337,7 @@ def ecr(
     "sswap": sswap,
     "cswap": cswap,
     "toffoli": toffoli,
+    "c4x": c4x,
     "phaseshift": phaseshift,
     "p": p,
     "cp": cp,

torchquantum/operators.py

@@ -43,6 +43,7 @@
     "SSWAP",
     "CSWAP",
     "Toffoli",
+    "C4X",
     "PhaseShift",
     "Rot",
     "MultiRZ",
@@ -118,6 +119,7 @@ class Operator(tq.QuantumModule):
         "SSWAP",
         "CSWAP",
         "Toffoli",
+        "C4X",
         "MultiCNOT",
         "MultiXCNOT",
         "Reset",
@@ -809,6 +811,17 @@ class Toffoli(Operation, metaclass=ABCMeta):
     def _matrix(cls, params):
         return cls.matrix
 
+class C4X(Operation, metaclass=ABCMeta):
+    """Class for C4X Gate."""
+
+    num_params = 0
+    num_wires = 5
+    matrix = mat_dict["c4x"]
+    func = staticmethod(tqf.c4x)
+
+    @classmethod
+    def _matrix(cls, params):
+        return cls.matrix
 
 class RX(Operation, metaclass=ABCMeta):
     """Class for RX Gate."""
@@ -1358,6 +1371,7 @@ def _matrix(cls, params):
     "cswap": CSWAP,
     "toffoli": Toffoli,
     "ccx": Toffoli,
+    "c4x": C4X,
     "phaseshift": PhaseShift,
     "rot": Rot,
     "multirz": MultiRZ,