Misc improvements

src/qutip_cuquantum/__init__.py

@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 try:
     import cupy
 except ImportError as err:
@@ -28,7 +32,7 @@
 from .operator import CuOperator
 from .state import CuState
 import numpy
-
+from .mixed_dispatch import * #Ensure that the mixed dispatch is registered
 
 # TODO: The split per density is not great
 # Add an operator / state split in qutip?
@@ -95,7 +99,6 @@ def set_as_default(ctx: cuquantum.densitymat.WorkStream=None, reverse=False):
     if not reverse:
         settings.cuDensity["ctx"] = ctx
         settings.core["default_dtype"] = "cuDensity"
-        settings.core['numpy_backend'] = cupy
 
         if True:  # if mpi, how to check from ctx?
             settings.core["auto_real_casting"] = False
@@ -111,7 +114,6 @@ def set_as_default(ctx: cuquantum.densitymat.WorkStream=None, reverse=False):
 
     else:
         settings.core["default_dtype"] = "core"
-        settings.core['numpy_backend'] = numpy
         settings.core["auto_real_casting"] = True
 
         SESolver.solver_options['method'] = "adams"
@@ -145,7 +147,6 @@ def __enter__(self):
         self.previous_values["default_dtype"] = qutip.settings.core["default_dtype"]
         settings.core["default_dtype"] = "cuDensity"
         self.previous_values["numpy_backend"] = qutip.settings.core["numpy_backend"]
-        settings.core['numpy_backend'] = cupy
 
         self.previous_values["auto_real"] = settings.core["auto_real_casting"]
         if True:  # if mpi, how to check from ctx?
@@ -169,7 +170,6 @@ def __enter__(self):
 
     def __exit__(self, exc_type, exc_value, traceback):
         settings.core["default_dtype"] = self.previous_values["default_dtype"]
-        settings.core['numpy_backend'] = self.previous_values["numpy_backend"]
         settings.core["auto_real_casting"] = self.previous_values["auto_real"]
         SESolver.solver_options['method'] = self.previous_values["SESolverM"]
         MESolver.solver_options['method'] = self.previous_values["MESolverM"]

src/qutip_cuquantum/mixed_dispatch.py

@@ -1,3 +1,7 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 import qutip.core.data as _data
 from qutip import settings
 from .state import zeros_like_cuState, CuState
@@ -8,17 +12,23 @@
 from cuquantum.densitymat import Operator
 
 @_data.matmul.register(CuOperator, CuState, CuState)
-def matmul_cuoperator_custate_custate(left, right, scale=1., out=None):
-    merged_hilbert = _compare_hilbert(left.hilbert_dims, right.base.hilbert_space_dims)
-    if not merged_hilbert:
-        raise ValueError("Hilbert space missmatch")
+def matmul_cuoperator_custate_custate(left, right, scale=1., out=None):    
+
     if left.shape[1] == right.shape[0]:
         dual = False
+        merged_hilbert = _compare_hilbert(left.hilbert_dims, right.base.hilbert_space_dims)
     elif left.shape[1] == right.shape[0] * right.shape[1]:
         dual = True
+        print(left.hilbert_dims[:len(left.hilbert_dims) // 2], right.base.hilbert_space_dims)
+        merged_hilbert = _compare_hilbert(left.hilbert_dims[:len(left.hilbert_dims) // 2], right.base.hilbert_space_dims)
     else:
         raise ValueError("Shape missmatch")
 
+    if not merged_hilbert:
+        raise ValueError("Hilbert space missmatch")
+
+    if(scale != 1.):
+        left = left * scale
     oper = Operator(merged_hilbert, [left.to_OperatorTerm(dual=dual, hilbert_dims=merged_hilbert)])
 
     oper.prepare_action(settings.cuDensity["ctx"], right.base)
@@ -28,3 +38,7 @@ def matmul_cuoperator_custate_custate(left, right, scale=1., out=None):
     oper.compute_action(0, [], state_in=right.base, state_out=out.base)
 
     return out
+
+@_data.matmul.register(CuState, CuOperator, CuState)
+def matmul_custate_cuoperator_custate(left, right, scale=1., out=None):
+    return matmul_cuoperator_custate_custate(right.transpose(), left.transpose(), scale, out).transpose()

src/qutip_cuquantum/qobjevo.pyx

@@ -1,5 +1,9 @@
 #cython: language_level=3
 
+# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 import cuquantum.densitymat as cudense
 from cuquantum.densitymat import Operator
 
@@ -91,7 +95,7 @@ cdef class CuQobjEvo(QobjEvo):
             self.expect_ready = True
         # Workaround for a bug in cudensity 0.2.0.
         settings.cuDensity["ctx"].release_workspace()
-        return self.operator.compute_expectation(t, None, state.base)
+        return self.operator.compute_expectation(t, None, state.base).get()
 
     def arguments(self, args):
         raise NotImplementedError

src/qutip_cuquantum/state.py

@@ -1,3 +1,8 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+from typing import Any
 from cuquantum.densitymat import DensePureState, DenseMixedState
 
 import numpy as np
@@ -12,6 +17,11 @@
 except ImportError:
     CuPyDense = None
 
+try:
+    import mpi4py.MPI as MPI
+except ImportError:
+    MPI = None
+
 
 class CuState(Data):
     def __init__(self, arg, hilbert_dims=None, shape=None, copy=True):
@@ -30,19 +40,21 @@ def __init__(self, arg, hilbert_dims=None, shape=None, copy=True):
                 hilbert_dims = arg.hilbert_space_dims
             base = arg
 
-        elif CuPyDense is not None and isinstance(arg, CuPyDense):
+        elif (CuPyDense is not None and isinstance(arg, CuPyDense)) or isinstance(arg, cp.ndarray):
+            if CuPyDense is not None and isinstance(arg, CuPyDense):
+                arg = arg._cp
             if shape is None:
                 shape = arg.shape
             if hilbert_dims is None:
                 hilbert_dims = arg.shape[:1]
 
-            if arg.shape[0] != np.prod(hilbert_dims) or arg.shape[1] != 1:
-                # TODO: Add sanity check for hilbert_dims
+            if arg.shape[0] != 1 and arg.shape[1] != 1:
+                assert arg.shape[0] == np.prod(hilbert_dims) and arg.shape[1] == np.prod(hilbert_dims), "Shape does not match hilbert_dims"
                 base = DenseMixedState(ctx, hilbert_dims, 1, "complex128")
                 sizes, offsets = base.local_info
                 sls = tuple(slice(s, s+n) for s, n in zip(offsets, sizes))[:-1]
                 N = np.prod(sizes)
-                if len(arg._cp) == N:
+                if len(arg) == N:
                     base.attach_storage(cp.array(
                         arg._cp
                         .reshape(hilbert_dims * 2)[sls]
@@ -52,7 +64,7 @@ def __init__(self, arg, hilbert_dims=None, shape=None, copy=True):
                 else:
                     base.allocate_storage()
                     base.storage[:N] = (
-                        arg._cp
+                        arg
                         .reshape(hilbert_dims * 2)[sls]
                         .ravel(order="F")
                     )
@@ -62,16 +74,16 @@ def __init__(self, arg, hilbert_dims=None, shape=None, copy=True):
                 sizes, offsets = base.local_info
                 sls = tuple(slice(s, s+n) for s, n in zip(offsets, sizes))[:-1]
                 N = np.prod(sizes)
-                if len(arg._cp) == N:
+                if len(arg) == N:
                     base.attach_storage(cp.array(
-                        arg._cp
+                        arg
                         .reshape(hilbert_dims)[sls]
                         .ravel(order="F"), copy=copy
                     ))
                 else:
                     base.allocate_storage()
                     base.storage[:N] = (
-                        arg._cp
+                        arg
                         .reshape(hilbert_dims)[sls]
                         .ravel(order="F")
                     )
@@ -122,17 +134,25 @@ def to_array(self, as_tensor=False):
         return self.to_cupy(as_tensor).get()
 
     def to_cupy(self, as_tensor=False):
-        # TODO: How to implement for mpi?
         if type(self.base) is DenseMixedState:
             tensor_shape = self.base.hilbert_space_dims * 2
         else:
             tensor_shape = self.base.hilbert_space_dims
+
+        local_tensor = self.base.view()[..., 0]
         if self.base.local_info[0][:-1] != tensor_shape:
-            raise NotImplementedError(
-                "Not Implemented for MPI distributed array."
-                f"{self.base.local_info[0][:-1]} vs {self.base.hilbert_space_dims}"
-            )
-        tensor = self.base.view()[..., 0]
+            if MPI is None:
+                raise ImportError("mpi4py is not imported. Distributed tensor assembly requires mpi4py.")
+            comm = MPI.COMM_WORLD
+            tensor = cp.empty(tensor_shape, dtype=cp.complex128)
+            sizes, offsets = self.base.local_info
+            local_sls = tuple(slice(s, s+n) for s, n in zip(offsets, sizes))[:-1]
+            all_sls = comm.allgather(local_sls)
+            all_tensor = comm.allgather(local_tensor)
+            for rank in range(comm.Get_size()):
+                tensor[all_sls[rank]] = all_tensor[rank]
+        else:
+            tensor = local_tensor
         if not as_tensor:
             tensor = tensor.reshape(*self.shape, order="C")
         return tensor
@@ -145,7 +165,8 @@ def __add__(self, other):
             if isinstance(other, Data):
                 return _data.add(self, other)
             return NotImplemented
-
+        if(self.shape != other.shape):
+            raise ValueError("Incompatible shapes")
         new = self.copy()
         new.base.inplace_accumulate(other.base, 1.)
         return new
@@ -156,6 +177,8 @@ def __sub__(self, other):
                 return _data.sub(self, other)
             return NotImplemented
 
+        if(self.shape != other.shape):
+            raise ValueError("Incompatible shapes")
         new = self.copy()
         new.base.inplace_accumulate(other.base, -1.)
         return new
@@ -175,17 +198,20 @@ def conj(self):
         )
 
     def transpose(self):
-        raise NotImplementedError()
+        arr = self.to_cupy().transpose()
+        return CuState(arr, hilbert_dims=self.base.hilbert_space_dims, shape=(self.shape[1], self.shape[0]))
 
-    def adjoint(self):
-        raise NotImplementedError()
 
+    def adjoint(self):
+        arr = self.to_cupy().transpose().conj()
+        return CuState(arr, hilbert_dims=self.base.hilbert_space_dims, shape=(self.shape[1], self.shape[0]))
 
 def CuState_from_Dense(mat):
     return CuState(mat)
 
 
 def Dense_from_CuState(mat):
+    print("Dense_from_CuState")
     return _data.Dense(mat.to_array())
 
 
@@ -300,3 +326,49 @@ def isherm(state, tol=-1):
 
 def zeros_like_cuState(state):
     return CuState(state.base.clone(cp.zeros_like(state.base.storage, order="F")))
+
+@_data.conj.register(CuState)
+def conj_cuState(state):
+    return state.conj()
+
+@_data.transpose.register(CuState)
+def transpose_cuState(state):
+    return state.transpose()
+
+@_data.adjoint.register(CuState)
+def adjoint_cuState(state):
+    return state.adjoint()
+
+@_data.sub.register(CuState)
+def sub_cuState(left, right):
+    return add_cuState(left, right, -1)
+
+@_data.iszero.register(CuState)
+def iszero_cuState(state):
+    return not cp.any(state.base.storage)
+
+
+@_data.matmul.register(CuState)
+def matmul_cuState(left, right):
+    if(left.shape[1] != right.shape[0]):
+        raise ValueError("Incompatible shapes")
+
+    if left.base.hilbert_space_dims != right.base.hilbert_space_dims:
+        raise ValueError(
+            f"Incompatible hilbert space: {left.base.hilbert_space_dims} "
+            f"and {right.base.hilbert_space_dims}."
+        )
+
+    output_shape = (left.shape[0], right.shape[1])
+    ctx = settings.cuDensity["ctx"]
+    if(left.shape[0] == 1 and right.shape[1] == 1):
+        # Scalar case
+        hilbert_dims = (1,)    
+    else:
+        hilbert_dims = left.base.hilbert_space_dims
+
+    left_array = left.to_cupy()
+    right_array = right.to_cupy()
+    arr = left_array @ right_array
+
+    return CuState(arr, hilbert_dims=hilbert_dims, shape=output_shape)