Cuquantum 0801

.gitignore

@@ -336,3 +336,5 @@ es_runs/
 .vscode-upload.json
 
 mnist_data/
+.cursor/
+.github/

examples/cuquantum/h2_new.txt

@@ -0,0 +1,6 @@
+h2 bk 2
+0.1790005760614067 X0 X1
+-1.0439125217729273 I0 I1
+-0.42045567978280385 Z0 I1
+0.42045567978280385 I0 Z1
+-0.011507402176827025 Z0 Z1

examples/cuquantum/qaoa.py

@@ -0,0 +1,122 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: MIT
+
+import math
+import argparse
+
+import torch
+from torch import nn
+from torchquantum.plugin.cuquantum import *
+from torchquantum.operator.standard_gates import *
+
+
+
+class MAXCUT(nn.Module):
+    def __init__(self, n_wires, input_graph, n_layers):
+        super().__init__()
+        self.n_wires = n_wires
+        self.input_graph = input_graph
+        self.n_layers = n_layers
+
+        self.circuit = ParameterizedQuantumCircuit(n_wires=n_wires, n_input_params=0, n_trainable_params=2 * n_layers)
+        self.circuit.set_trainable_params(torch.randn(2 * n_layers))
+
+        for wire in range(self.n_wires):
+            self.circuit.append_gate(Hadamard, wires=wire)
+
+        for l in range(self.n_layers):
+            # mixer layer
+            for i in range(self.n_wires):
+                self.circuit.append_gate(RX, wires=i, trainable_idx=l)
+
+            # entangler layer
+            for edge in self.input_graph:
+                self.circuit.append_gate(CNOT, wires=[edge[0], edge[1]])
+                self.circuit.append_gate(RZ, wires=edge[1], trainable_idx=n_layers + l)
+                self.circuit.append_gate(CNOT, wires=[edge[0], edge[1]])
+
+
+        hamiltonian = {}
+        for edge in self.input_graph:
+            pauli_string = ""
+            for wire in range(self.n_wires):
+                if wire in edge:
+                    pauli_string += "Z"
+                else:
+                    pauli_string += "I"
+            hamiltonian[pauli_string] = 0.5
+
+        backend = CuTensorNetworkBackend(TNConfig(num_hyper_samples=10))
+        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)
+        end_time = torch.cuda.Event(enable_timing=True)
+
+        start_time.record()
+        output = self.energy() - len(self.input_graph) / 2
+        end_time.record()
+
+        torch.cuda.synchronize()
+        elapsed_time = start_time.elapsed_time(end_time)
+        print(f"Forward pass took {elapsed_time:.2f} ms")
+
+        return output
+
+
+def optimize(model, n_steps=100, lr=0.1):
+    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
+    print(f"The initial parameters are:\n{next(model.parameters()).data.tolist()}")
+    print("")
+    for step in range(n_steps):
+        optimizer.zero_grad()
+        loss = model()
+        start_time = torch.cuda.Event(enable_timing=True)
+        end_time = torch.cuda.Event(enable_timing=True)
+
+        start_time.record()
+        loss.backward()
+        end_time.record()
+
+        torch.cuda.synchronize()
+        elapsed_time = start_time.elapsed_time(end_time)
+        print(f"Backward pass took {elapsed_time:.2f} ms")
+
+        optimizer.step()
+
+        print(f"Step: {step}, Cost Objective: {loss.item()}")
+
+    print("")
+    print(f"The optimal parameters are:\n{next(model.parameters()).data.tolist()}")
+    print("")
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--n_wires", type=int, default=4, help="number of wires")
+    parser.add_argument("--n_layers", type=int, default=4, help="number of layers")
+    parser.add_argument("--steps", type=int, default=100, help="number of steps")
+    parser.add_argument("--lr", type=float, default=0.01, help="learning rate")
+    parser.add_argument("--seed", type=int, default=0, help="random seed")
+    args = parser.parse_args()
+
+    torch.manual_seed(args.seed)
+
+    # create a fully connected graph
+    input_graph = []
+    for i in range(args.n_wires):
+        for j in range(i):
+            input_graph.append((i, j))
+
+    print(f"Cost Objective Minimum (Analytic Reference Result): {math.floor(args.n_wires**2 // 4)}")
+
+    model = MAXCUT(n_wires=args.n_wires, input_graph=input_graph, n_layers=args.n_layers)
+    optimize(model, n_steps=args.steps, lr=args.lr)
+    samples = model.sampling()
+
+    print(f"Sampling Results: {samples}")
+
+
+if __name__ == "__main__":
+    main()

torchquantum/__init__.py

@@ -35,30 +35,36 @@
 from .layer import *
 from .encoding import *
 from .util import *
-from .noise_model import *
+# Note: noise_model requires qiskit and is not imported by default
+# from .noise_model import *
 from .algorithm import *
 from .dataset import *
 from .pulse import *
 
 # here we check whether the Qiskit parameterization bug is fixed, if not, a
 # warning message will be printed
-import qiskit
-import os
+# This is only done if qiskit is available
+try:
+    import qiskit
+    import os
 
-path = os.path.abspath(qiskit.__file__)
-# print(path)
-# path for aer provider
-path_provider = path.replace("__init__.py", "providers/aer/backends/aerbackend.py")
-# print(path_provider)
+    path = os.path.abspath(qiskit.__file__)
+    # print(path)
+    # path for aer provider
+    path_provider = path.replace("__init__.py", "providers/aer/backends/aerbackend.py")
+    # print(path_provider)
 
-# with open(path_provider, 'r') as fid:
-#     for line in fid.readlines():
-#         if 'FIXED' in line:
-#             # print('The qiskit parameterization bug is already fixed!')
-#             break
-#         else:
-#             print(f'\n\n WARNING: The qiskit parameterization bug is not '
-#                   f'fixed!\n\n'
-#                   f'run python fix_qiskit_parameterization.py to fix it!'
-#                   )
-#             break
+    # with open(path_provider, 'r') as fid:
+    #     for line in fid.readlines():
+    #         if 'FIXED' in line:
+    #             # print('The qiskit parameterization bug is already fixed!')
+    #             break
+    #         else:
+    #             print(f'\n\n WARNING: The qiskit parameterization bug is not '
+    #                   f'fixed!\n\n'
+    #                   f'run python fix_qiskit_parameterization.py to fix it!'
+    #                   )
+    #             break
+except ImportError:
+    # qiskit not available, skip the check
+    pass

torchquantum/layer/entanglement/entanglement.py

@@ -35,7 +35,7 @@
 from .op2_layer import Op2QAllLayer
 
 from typing import Iterable
-from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
+from torchquantum.util.constants import QISKIT_INCOMPATIBLE_FUNC_NAMES
 from torchpack.utils.logging import logger
 
 __all__ = [

torchquantum/layer/entanglement/op2_layer.py

@@ -30,7 +30,7 @@
 
 
 from typing import Iterable
-from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
+from torchquantum.util.constants import QISKIT_INCOMPATIBLE_FUNC_NAMES
 from torchpack.utils.logging import logger
 
 __all__ = [

torchquantum/layer/layers/cx_layer.py

@@ -29,7 +29,7 @@
 import numpy as np
 
 from typing import Iterable
-from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
+from torchquantum.util.constants import QISKIT_INCOMPATIBLE_FUNC_NAMES
 from torchpack.utils.logging import logger
 
 

torchquantum/layer/layers/layers.py

@@ -29,7 +29,7 @@
 import numpy as np
 
 from typing import Iterable
-from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
+from torchquantum.util.constants import QISKIT_INCOMPATIBLE_FUNC_NAMES
 from torchpack.utils.logging import logger
 from torchquantum.layer.entanglement.op2_layer import Op2QAllLayer
 

torchquantum/layer/layers/module_from_ops.py

@@ -30,7 +30,7 @@
 
 
 from typing import Iterable
-from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
+from torchquantum.util.constants import QISKIT_INCOMPATIBLE_FUNC_NAMES
 from torchpack.utils.logging import logger
 
 __all__ = [

torchquantum/layer/layers/op_all.py

@@ -30,7 +30,7 @@
 
 
 from typing import Iterable
-from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
+from torchquantum.util.constants import QISKIT_INCOMPATIBLE_FUNC_NAMES
 from torchpack.utils.logging import logger
 
 __all__ = [

torchquantum/layer/layers/qft_layer.py

@@ -5,7 +5,7 @@
 import numpy as np
 
 from typing import Iterable
-from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
+from torchquantum.util.constants import QISKIT_INCOMPATIBLE_FUNC_NAMES
 from torchpack.utils.logging import logger
 
 class QFTLayer(tq.QuantumModule):

torchquantum/layer/layers/random_layers.py

@@ -29,7 +29,7 @@
 import numpy as np
 
 from typing import Iterable
-from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
+from torchquantum.util.constants import QISKIT_INCOMPATIBLE_FUNC_NAMES
 from torchpack.utils.logging import logger
 
 __all__ = [

torchquantum/layer/layers/ry_layer.py

@@ -5,7 +5,7 @@
 import numpy as np
 
 from typing import Iterable
-from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
+from torchquantum.util.constants import QISKIT_INCOMPATIBLE_FUNC_NAMES
 from torchpack.utils.logging import logger
 
 from .layers import LayerTemplate0

torchquantum/layer/layers/seth_layer.py

@@ -29,7 +29,7 @@
 import numpy as np
 
 from typing import Iterable
-from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
+from torchquantum.util.constants import QISKIT_INCOMPATIBLE_FUNC_NAMES
 from torchpack.utils.logging import logger
 
 from .layers import LayerTemplate0, Op1QAllLayer

torchquantum/layer/layers/swap_layer.py

@@ -29,7 +29,7 @@
 import numpy as np
 
 from typing import Iterable
-from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
+from torchquantum.util.constants import QISKIT_INCOMPATIBLE_FUNC_NAMES
 from torchpack.utils.logging import logger
 
 from .layers import LayerTemplate0

torchquantum/layer/layers/u3_layer.py

@@ -29,7 +29,7 @@
 import numpy as np
 
 from typing import Iterable
-from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
+from torchquantum.util.constants import QISKIT_INCOMPATIBLE_FUNC_NAMES
 from torchpack.utils.logging import logger
 
 from .layers import LayerTemplate0, Op1QAllLayer

torchquantum/noise_model/noise_models.py

@@ -28,7 +28,7 @@
 
 from torchpack.utils.logging import logger
 from qiskit.providers.aer.noise import NoiseModel
-from torchquantum.util import get_provider
+from torchquantum.util.qiskit_utils import get_provider
 
 
 __all__ = [

torchquantum/operator/op_types.py

@@ -239,13 +239,28 @@ def forward(
             else:
                 self.func(q_device, self.wires, n_wires=self.n_wires, inverse=self.inverse)  # type: ignore
         else:
-            if isinstance(self.noise_model_tq, tq.NoiseModelTQPhase):
+            # Avoid hard dependency on tq.noise_model export; fall back gracefully
+            try:
+                is_phase_noise = isinstance(
+                    self.noise_model_tq, tq.noise_model.NoiseModelTQPhase
+                )
+            except AttributeError:
+                # tq.noise_model not exported; treat as no phase noise
+                is_phase_noise = False
+
+            if is_phase_noise:
                 params = self.noise_model_tq.add_noise(self.params)
             else:
                 params = self.params
 
             if self.clifford_quantization:
-                params = CliffordQuantizer.quantize_sse(params)
+                try:
+                    # Local import to avoid circular dependency and undefined name at import time
+                    from torchquantum.util.quantization import CliffordQuantizer
+                    params = CliffordQuantizer.quantize_sse(params)
+                except Exception:
+                    # If quantizer is unavailable, skip quantization
+                    pass
             if self.n_wires is None:
                 self.func(q_device, self.wires, params=params, inverse=self.inverse)
             else:

torchquantum/plugin/__init__.py

@@ -22,4 +22,9 @@
 SOFTWARE.
 """
 
-from .qiskit import *
+# Only import qiskit plugin if qiskit is available
+try:
+    from .qiskit import *
+except ImportError:
+    # qiskit not available, skip importing qiskit plugin
+    pass

torchquantum/plugin/cuquantum/LICENSE

@@ -0,0 +1,9 @@
+MIT License
+
+Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

torchquantum/plugin/cuquantum/__init__.py

@@ -0,0 +1,19 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: MIT
+
+from .circuit import ParameterizedQuantumCircuit
+from .cutn import CuTensorNetworkBackend, TNConfig, MPSConfig
+from .expectation import QuantumExpectation
+from .sampling import QuantumSampling
+from .amplitude import QuantumAmplitude
+
+__all__ = [
+    "ParameterizedQuantumCircuit",
+    "CuTensorNetworkBackend",
+    "TNConfig",
+    "MPSConfig",
+    "QuantumExpectation",
+    "QuantumSampling",
+    "QuantumAmplitude",
+]