* Enhancements to the custom operations feature

  -- accept np.matrix, 2D arrays
  -- make custom operations avaiable to look up by name in the builder
mode via attribute getter

python/cudaq/kernel/kernel_builder.py

@@ -188,23 +188,40 @@ def supportCommonCast(mlirType, otherTy, arg, FromType, ToType, PyType):
 
 def __generalCustomOperation(self, opName, *args):
     """
-    Utility function for adding a generic quantum operation to the MLIR representation for the PyKernel.
+    Utility function for adding a generic quantum operation to the MLIR 
+    representation for the PyKernel.
+
+    A controlled version can be invoked by passing additional arguments 
+    to the operation. For an N-qubit operation, the last N arguments are 
+    treated as `targets` and excess arguments as `controls`.
     """
 
     global globalRegisteredOperations
     unitary = globalRegisteredOperations[opName]
 
     numTargets = int(np.log2(np.sqrt(unitary.size)))
 
-    targets = []
+    qubits = []
     with self.insertPoint, self.loc:
         for arg in args:
             if isinstance(arg, QuakeValue):
-                targets.append(arg.mlirValue)
+                qubits.append(arg.mlirValue)
             else:
                 emitFatalError(f"invalid argument type passed to {opName}.")
 
-        assert (numTargets == len(targets))
+        targets = []
+        controls = []
+
+        if numTargets == len(qubits):
+            targets = qubits
+        elif numTargets < len(qubits):
+            numControls = len(qubits) - numTargets
+            targets = qubits[-numTargets:]
+            controls = qubits[:numControls]
+        else:
+            emitFatalError(
+                f"too few arguments passed to {opName}, expected ({numTargets})"
+            )
 
         globalName = f'{nvqppPrefix}{opName}_generator_{numTargets}.rodata'
         currentST = SymbolTable(self.module.operation)
@@ -216,7 +233,7 @@ def __generalCustomOperation(self, opName, *args):
         quake.CustomUnitarySymbolOp([],
                                     generator=FlatSymbolRefAttr.get(globalName),
                                     parameters=[],
-                                    controls=[],
+                                    controls=controls,
                                     targets=targets,
                                     is_adj=False)
         return
@@ -1520,6 +1537,11 @@ def getListType(eleType: type):
 
         cudaq_runtime.pyAltLaunchKernel(self.name, self.module, *processedArgs)
 
+    def __getattr__(self, attr_name):
+        if hasattr(self, attr_name):
+            return getattr(self, attr_name)
+        raise AttributeError(f"'{attr_name}' is not supported on PyKernel")
+
 
 setattr(PyKernel, 'h', partialmethod(__singleTargetOperation, 'h'))
 setattr(PyKernel, 'x', partialmethod(__singleTargetOperation, 'x'))

python/cudaq/kernel/register_op.py

@@ -34,19 +34,19 @@ def kernel():
     if isinstance(unitary, Callable):
         raise RuntimeError("parameterized custom operations not yet supported.")
 
-    if isinstance(unitary, np.ndarray):
-        if (len(unitary.shape) != unitary.ndim):
-            raise RuntimeError(
-                "provide a 1D array for the matrix representation in row-major format."
-            )
+    if isinstance(unitary, np.matrix):
+        matrix = np.array(unitary)
+    elif isinstance(unitary, np.ndarray):
         matrix = unitary
     elif isinstance(unitary, List):
         matrix = np.array(unitary)
     else:
         raise RuntimeError("unknown type of unitary.")
 
-    # TODO: Flatten the matrix if not flattened
-    assert (matrix.ndim == len(matrix.shape))
+    matrix = matrix.flatten()
+    assert (
+        matrix.ndim == len(matrix.shape),
+        "provide a 1D array for the matrix representation in row-major format.")
 
     # Size must be a power of 2
     assert (matrix.size != 0)

python/tests/custom/test_custom_operations.py

@@ -179,7 +179,7 @@ def kernel():
 
 
 def test_builder_mode():
-    """Builder-mode API """
+    """Builder-mode API"""
 
     kernel = cudaq.make_kernel()
     cudaq.register_operation("custom_h",
@@ -192,6 +192,19 @@ def test_builder_mode():
     check_bell(kernel)
 
 
+def test_builder_mode_control():
+    """Controlled operation in builder-mode"""
+
+    kernel = cudaq.make_kernel()
+    cudaq.register_operation("custom_x", np.array([0, 1, 1, 0]))
+
+    qubits = kernel.qalloc(2)
+    kernel.h(qubits[0])
+    kernel.custom_x(qubits[0], qubits[1])
+
+    check_bell(kernel)
+
+
 def test_invalid_ctrl():
     cudaq.register_operation("custom_x", np.array([0, 1, 1, 0]))
 

python/tests/mlir/custom_op_builder.py

@@ -0,0 +1,85 @@
+# ============================================================================ #
+# Copyright (c) 2022 - 2024 NVIDIA Corporation & Affiliates.                   #
+# All rights reserved.                                                         #
+#                                                                              #
+# This source code and the accompanying materials are made available under     #
+# the terms of the Apache License 2.0 which accompanies this distribution.     #
+# ============================================================================ #
+
+# RUN: PYTHONPATH=../../ pytest -rP  %s | FileCheck %s
+
+import numpy as np
+import cudaq
+
+
+def test_builder_look_up():
+    """A custom operation can be looked up by its name in builder mode"""
+
+    base_name = 'foo'
+    op_count = 3
+
+    def register_custom_operations(matrix):
+        prev = np.identity(2)
+        for t in range(op_count):
+            new = prev @ matrix
+            cudaq.register_operation(f'{base_name}_{t}', new)
+            prev = new
+
+    register_custom_operations(
+        np.array([[1, 0], [0, np.exp(np.pi * 1j * 1 / 3)]]))
+
+    kernel = cudaq.make_kernel()
+
+    qubit = kernel.qalloc(1)
+    ancilla = kernel.qalloc(2)
+
+    kernel.x(qubit)
+    kernel.h(ancilla)
+
+    for i in range(op_count):
+        kernel.__getattr__(f'{base_name}_{i}')(ancilla, qubit)
+
+    print(kernel)
+    counts = cudaq.sample(kernel)
+
+
+# CHECK-LABEL:   func.func @__nvqpp__mlirgen____nvqppBuilderKernel_{{.*}}() attributes {"cudaq-entrypoint"} {
+# CHECK:           %[[VAL_0:.*]] = arith.constant 2 : i64
+# CHECK:           %[[VAL_1:.*]] = arith.constant 1 : i64
+# CHECK:           %[[VAL_2:.*]] = arith.constant 0 : i64
+# CHECK:           %[[VAL_3:.*]] = quake.alloca !quake.veq<1>
+# CHECK:           %[[VAL_4:.*]] = quake.alloca !quake.veq<2>
+# CHECK:           %[[VAL_5:.*]] = cc.loop while ((%[[VAL_6:.*]] = %[[VAL_2]]) -> (i64)) {
+# CHECK:             %[[VAL_7:.*]] = arith.cmpi slt, %[[VAL_6]], %[[VAL_1]] : i64
+# CHECK:             cc.condition %[[VAL_7]](%[[VAL_6]] : i64)
+# CHECK:           } do {
+# CHECK:           ^bb0(%[[VAL_8:.*]]: i64):
+# CHECK:             %[[VAL_9:.*]] = quake.extract_ref %[[VAL_3]]{{\[}}%[[VAL_8]]] : (!quake.veq<1>, i64) -> !quake.ref
+# CHECK:             quake.x %[[VAL_9]] : (!quake.ref) -> ()
+# CHECK:             cc.continue %[[VAL_8]] : i64
+# CHECK:           } step {
+# CHECK:           ^bb0(%[[VAL_10:.*]]: i64):
+# CHECK:             %[[VAL_11:.*]] = arith.addi %[[VAL_10]], %[[VAL_1]] : i64
+# CHECK:             cc.continue %[[VAL_11]] : i64
+# CHECK:           } {invariant}
+# CHECK:           %[[VAL_12:.*]] = cc.loop while ((%[[VAL_13:.*]] = %[[VAL_2]]) -> (i64)) {
+# CHECK:             %[[VAL_14:.*]] = arith.cmpi slt, %[[VAL_13]], %[[VAL_0]] : i64
+# CHECK:             cc.condition %[[VAL_14]](%[[VAL_13]] : i64)
+# CHECK:           } do {
+# CHECK:           ^bb0(%[[VAL_15:.*]]: i64):
+# CHECK:             %[[VAL_16:.*]] = quake.extract_ref %[[VAL_4]]{{\[}}%[[VAL_15]]] : (!quake.veq<2>, i64) -> !quake.ref
+# CHECK:             quake.h %[[VAL_16]] : (!quake.ref) -> ()
+# CHECK:             cc.continue %[[VAL_15]] : i64
+# CHECK:           } step {
+# CHECK:           ^bb0(%[[VAL_17:.*]]: i64):
+# CHECK:             %[[VAL_18:.*]] = arith.addi %[[VAL_17]], %[[VAL_1]] : i64
+# CHECK:             cc.continue %[[VAL_18]] : i64
+# CHECK:           } {invariant}
+# CHECK:           quake.custom_op @__nvqpp__mlirgen__foo_0_generator_1.rodata {{\[}}%[[VAL_4]]] %[[VAL_3]] : (!quake.veq<2>, !quake.veq<1>) -> ()
+# CHECK:           quake.custom_op @__nvqpp__mlirgen__foo_1_generator_1.rodata {{\[}}%[[VAL_4]]] %[[VAL_3]] : (!quake.veq<2>, !quake.veq<1>) -> ()
+# CHECK:           quake.custom_op @__nvqpp__mlirgen__foo_2_generator_1.rodata {{\[}}%[[VAL_4]]] %[[VAL_3]] : (!quake.veq<2>, !quake.veq<1>) -> ()
+# CHECK:           return
+# CHECK:         }
+# CHECK:         cc.global constant @__nvqpp__mlirgen__foo_0_generator_1.rodata (dense<[(1.000000e+00,0.000000e+00), (0.000000e+00,0.000000e+00), (0.000000e+00,0.000000e+00), (0.50000000000000011,0.8660254037844386)]> : tensor<4xcomplex<f64>>) : !cc.array<complex<f64> x 4>
+# CHECK:         cc.global constant @__nvqpp__mlirgen__foo_1_generator_1.rodata (dense<[(1.000000e+00,0.000000e+00), (0.000000e+00,0.000000e+00), (0.000000e+00,0.000000e+00), (-0.49999999999999978,0.86602540378443881)]> : tensor<4xcomplex<f64>>) : !cc.array<complex<f64> x 4>
+# CHECK:         cc.global constant @__nvqpp__mlirgen__foo_2_generator_1.rodata (dense<[(1.000000e+00,0.000000e+00), (0.000000e+00,0.000000e+00), (-0.000000e+00,0.000000e+00), (-1.000000e+00,4.1434087356338769E-16)]> : tensor<4xcomplex<f64>>) : !cc.array<complex<f64> x 4>