Merge pull request #4 from GenericP3rson/dev

Merging Dev to Get Latest Updates (Esp. with Isometry)

README.md

@@ -7,15 +7,19 @@
 
 
 <p align="center">
-    <a href="https://github.com/mit-han-lab/torchquantum/blob/master/LICENSE">
-        <img alt="MIT License" src="https://img.shields.io/github/license/mit-han-lab/torchquantum">
-    </a>
     <a href="https://torchquantum.readthedocs.io/">
         <img alt="Documentation" src="https://img.shields.io/readthedocs/torchquantum/main">
     </a>
+    <a href="https://github.com/mit-han-lab/torchquantum/blob/master/LICENSE">
+        <img alt="MIT License" src="https://img.shields.io/github/license/mit-han-lab/torchquantum">
+    </a>
+
     <a href="https://join.slack.com/t/torchquantum/shared_invite/zt-1ghuf283a-OtP4mCPJREd~367VX~TaQQ">
         <img alt="Chat @ Slack" src="https://img.shields.io/badge/slack-chat-2eb67d.svg?logo=slack">
     </a>
+    <a href="https://discord.gg/VTHZAB5E">
+        <img alt="Chat @ Discord" src="https://img.shields.io/badge/contact-me-blue?logo=discord&logoColor=white">
+    </a>
     <a href="https://qmlsys.hanruiwang.me">
         <img alt="Forum" src="https://img.shields.io/discourse/status?server=https%3A%2F%2Fqmlsys.hanruiwang.me%2F">
     </a>

docs/source/api_layers.rst

@@ -0,0 +1,29 @@
+torchquantum.layers
+======================
+
+.. currentmodule:: torchquantum.layer
+
+Layers
+---------
+.. autosummary::
+    :toctree: generated
+    :template: classtemplate_controlflow.rst
+
+
+    QuantumModuleFromOps
+    TrainableOpAll
+    ClassicalInOpAll
+    FixedOpAll
+    TwoQAll
+    RandomLayer
+    RandomLayerAllTypes
+    Op1QAllLayer
+    RandomOp1All
+    Op2QAllLayer
+    Op2QButterflyLayer
+    Op2QDenseLayer
+    CXLayer
+    CXCXCXLayer
+    SWAPSWAPLayer
+    RXYZCXLayer0
+    QFTLayer

docs/source/api_operators.rst

@@ -1,7 +1,7 @@
 torchquantum.operators
 ======================
 
-.. currentmodule:: torchquantum.operators
+.. currentmodule:: torchquantum.operator
 
 Classes
 ---------

docs/source/index.rst

@@ -5,6 +5,7 @@
    api_torchquantum
    api_functional
    api_operators
+   api_layers
 
 .. toctree::
    :maxdepth: 1

examples/gradient_pruning/q_models.py

@@ -154,7 +154,7 @@ def shift_and_run(
                 node.shift_this_step[idx] = True
             elif self.pruning_method == "perlayer_pruning":
                 node.shift_this_step[:] = False
-                idxs = torch.range(0, self.n_params - 1, dtype=int).view(
+                idxs = torch.arange(0, self.n_params, dtype=int).view(
                     self.n_qubits, self.n_layers
                 )
                 sampled_colums = self.colums
@@ -164,7 +164,7 @@ def shift_and_run(
                 self.colums %= self.n_layers
             elif self.pruning_method == "perqubit_pruning":
                 node.shift_this_step[:] = False
-                idxs = torch.range(0, self.n_params - 1, dtype=int).view(
+                idxs = torch.arange(0, self.n_params, dtype=int).view(
                     self.n_qubits, self.n_layers
                 )
                 sampled_rows = self.rows

examples/index.rst

@@ -8,4 +8,6 @@ TorchQuantum Examples
    param_shift_onchip_training/param_shift_onchip_training.ipynb
    quantum_kernel_method/quantum_kernel_method.ipynb
    quanvolution/quanvolution.ipynb
-   superdense_coding/superdense_coding_torchquantum.ipynb
+   superdense_coding/superdense_coding_torchquantum.ipynb
+   qubit_rotation/TQ_Qubit_Rotation_Tutorial.ipynb
+

examples/superdense_coding/superdense_coding_torchquantum.ipynb

@@ -502,7 +502,7 @@
         "id": "nFC9-bqHbG2I"
       },
       "source": [
-        "# References:\n",
+        "## References:\n",
         "\n",
         "[1] Bennett, C.H., Brassard, G., Crépeau, C., Jozsa, R., Peres, A. and Wootters, W.K., 1993. Teleporting an unknown quantum state via dual classical and Einstein-Podolsky-Rosen channels. Physical review letters, 70(13), p.1895.\n",
         "\n",

test/layers/test_rotgate.py

@@ -0,0 +1,55 @@
+import torchquantum as tq
+import qiskit
+from qiskit import Aer, execute
+
+from torchquantum.util import (
+    switch_little_big_endian_matrix,
+    find_global_phase,
+)
+
+from qiskit.circuit.library import GR, GRX, GRY, GRZ
+import numpy as np
+
+all_pairs = [
+    {"qiskit": GR, "tq": tq.layer.GlobalR, "params": 2},
+    {"qiskit": GRX, "tq": tq.layer.GlobalRX, "params": 1},
+    {"qiskit": GRY, "tq": tq.layer.GlobalRY, "params": 1},
+    {"qiskit": GRZ, "tq": tq.layer.GlobalRZ, "params": 1},
+]
+
+ITERATIONS = 10
+
+# test each pair
+for pair in all_pairs:
+    # test 2-5 wires
+    for num_wires in range(2, 5):
+        # try multiple random parameters
+        for _ in range(ITERATIONS):
+            # generate random parameters
+            params = [
+                np.random.uniform(-2 * np.pi, 2 * np.pi) for _ in range(pair["params"])
+            ]
+
+            # create the qiskit circuit
+            qiskit_circuit = pair["qiskit"](num_wires, *params)
+
+            # get the unitary from qiskit
+            backend = Aer.get_backend("unitary_simulator")
+            result = execute(qiskit_circuit, backend).result()
+            unitary_qiskit = result.get_unitary(qiskit_circuit)
+
+            # create tq circuit
+            qdev = tq.QuantumDevice(num_wires)
+            tq_circuit = pair["tq"](num_wires, *params)
+            tq_circuit(qdev)
+
+            # get the unitary from tq
+            unitary_tq = tq_circuit.get_unitary(qdev)
+            unitary_tq = switch_little_big_endian_matrix(unitary_tq.data.numpy())
+
+            # phase?
+            phase = find_global_phase(unitary_tq, unitary_qiskit, 1e-4)
+
+            assert np.allclose(
+                unitary_tq * phase, unitary_qiskit, atol=1e-6
+            ), f"{pair} not equal with {params=}!"

torchquantum/layer/__init__.py

@@ -24,3 +24,4 @@
 
 from .layers import *
 from .nlocal import *
+from .general import *

torchquantum/layer/general.py

@@ -0,0 +1,104 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+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.
+"""
+
+
+import torch
+import torchquantum as tq
+from torchquantum.layer.layers import (
+    LayerTemplate0,
+    Op1QAllLayer,
+    Op2QAllLayer,
+    RandomOp1All,
+)
+
+__all__ = [
+    "GlobalR",
+    "GlobalRX",
+    "GlobalRY",
+    "GlobalRZ",
+]
+
+
+class GlobalR(tq.QuantumModule):
+    """Layer Template for a Global R General Gate"""
+
+    def __init__(
+        self,
+        n_wires: int = 0,
+        theta: float = 0,
+        phi: float = 0,
+    ):
+        """Create the layer"""
+        super().__init__()
+        self.n_wires = n_wires
+        self.params = torch.tensor([[theta, phi]])
+
+    @tq.static_support
+    def forward(self, q_device, x=None):
+        for k in range(self.n_wires):
+            tq.R()(q_device, wires=k, params=self.params)
+
+
+class GlobalRX(GlobalR):
+    """Layer Template for a Global RX General Gate"""
+
+    def __init__(
+        self,
+        n_wires: int = 0,
+        theta: float = 0,
+    ):
+        """Create the layer"""
+        super().__init__(n_wires, theta, phi=0)
+
+
+class GlobalRY(GlobalR):
+    """Layer Template for a Global RY General Gate"""
+
+    def __init__(
+        self,
+        n_wires: int = 0,
+        theta: float = 0,
+    ):
+        """Create the layer"""
+        super().__init__(n_wires, theta, phi=torch.pi / 2)
+
+class GlobalRZ(tq.QuantumModule):
+    """Layer Template for a Global RZ General Gate"""
+
+    def __init__(
+        self,
+        n_wires: int = 0,
+        phi: float = 0,
+    ):
+        """Create the layer"""
+        super().__init__()
+        self.n_wires = n_wires
+        self.params = torch.tensor([[phi]])
+
+    @tq.static_support
+    def forward(self, q_device, x=None):
+        for k in range(self.n_wires):
+            tq.RZ()(q_device, wires=k, params=self.params)
+
+