Merge pull request #175 from GenericP3rson/nlocal

Hanrui-Wang · web-flow · commit dd8316313f42 · 2023-08-22T21:06:13.000-04:00
NLocal Circuits
diff --git a/torchquantum/layer/__init__.py b/torchquantum/layer/__init__.py
@@ -23,4 +23,5 @@
 """
 
 from .layers import *
+from .nlocal import *
 from .n_local import *
diff --git a/torchquantum/layer/layers.py b/torchquantum/layer/layers.py
@@ -42,6 +42,7 @@
     "RandomLayer",
     "RandomLayerAllTypes",
     "Op1QAllLayer",
+    "RandomOp1All",
     "Op2QAllLayer",
     "Op2QButterflyLayer",
     "Op2QDenseLayer",
@@ -227,6 +228,46 @@ def forward(self, q_device: tq.QuantumDevice):
         self.op(q_device, wires=[self.n_gate - 1, 0])
 
 
+class RandomOp1All(tq.QuantumModule):
+    def __init__(
+        self, n_wires: int, op_types=(tq.RX, tq.RY, tq.RZ), op_ratios=None, seed=None
+    ):
+        """Layer adding a random gate to all wires
+
+        Params:
+            n_wires (int): number of wires/gates in integer format
+            op_types (Iterable): single-wire gates to select from in iterable format
+            op_ratios (Iterable): probabilities to select each gate option in iterable format
+            seed (int): random seed in integer format
+        """
+        super().__init__()
+        self.n_wires = n_wires
+        self.op_types = op_types
+        self.op_ratios = op_ratios
+        self.seed = seed
+        self.gate_all = nn.ModuleList()
+        if seed is not None:
+            np.random.seed(seed)
+        self.build_random_layer()
+
+    def build_random_layer(self):
+        for k in range(self.n_wires):
+            op = np.random.choice(self.op_types, p=self.op_ratios)
+            self.gate_all.append(op())
+
+    @tq.static_support
+    def forward(self, q_device: tq.QuantumDevice, x):
+        # op on all wires, assert the number of gate is the same as the number
+        # of wires in the device.
+        assert self.n_gate == q_device.n_wires, (
+            f"Number of gates ({self.n_wires}) is different from number "
+            f"of wires ({q_device.n_wires})!"
+        )
+
+        for k in range(self.n_wires):
+            self.gate_all[k](q_device, wires=k, params=x[:, k])
+
+
 class RandomLayer(tq.QuantumModule):
     """
         Quantum module that represents a random layer of quantum operations applied to specified wires.
diff --git a/torchquantum/layer/nlocal.py b/torchquantum/layer/nlocal.py
@@ -0,0 +1,324 @@
+"""
+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__ = [
+    "NLocal",
+    "TwoLocal",
+    "ExcitationPreserving",
+    "EfficientSU2",
+    "RealAmplitudes",
+    "PauliTwoDesign",
+]
+
+
+class NLocal(LayerTemplate0):
+    """Layer Template for a NLocal Class
+
+    Args:
+        rotation_ops (list): gates for the rotation layer as a list of torchquantum operations
+        entanglement_ops (list): gates for the entanglement layer as a list of torchquantum operations
+        arch (dict): circuit architecture in a dictionary format
+        rotation_layer (torchquantum.QuantumModule): type of rotation layer in a torchquantum.QuantumModule format
+        entanglement_layer (torchquantum.QuantumModule): type of entanglement layer in a torchquantum.QuantumModule format
+        reps (int): number of reptitions of the rotation and entanglement layers in a integer format
+        rotation_layer_params (dict): additional parameters for the rotation layer in a dictionary format
+        entanglement_layer_params (dict): additional parameters for the entanglement layer in a dictionary format
+        initial_circuit (torchquantum.QuantumModule): initial gates or layer in a QuantumModule format
+        skip_final_rotation_layer (bool): whether or not to add the final rotation layer as a boolean
+    """
+
+    def __init__(
+        self,
+        rotation_ops: list = None,
+        entanglement_ops: list = None,
+        arch: dict = None,
+        rotation_layer: tq.QuantumModule = Op1QAllLayer,
+        entanglement_layer: tq.QuantumModule = Op2QAllLayer,
+        reps: int = 1,
+        rotation_layer_params: dict = {},
+        entanglement_layer_params: dict = {},
+        initial_circuit: tq.QuantumModule = None,
+        skip_final_rotation_layer: bool = False,
+    ):
+        # rotation block options
+        self.rotation_ops = rotation_ops
+        self.rotation_layer = rotation_layer
+        self.rotation_layer_params = rotation_layer_params
+
+        # entanglement block options
+        self.entanglement_ops = entanglement_ops
+        self.entanglement_layer = entanglement_layer
+        self.entanglement_layer_params = entanglement_layer_params
+
+        # extra parameters
+        self.initial_circuit = initial_circuit
+        self.skip_final_rotation_layer = skip_final_rotation_layer
+        self.reps = reps
+
+        # initialize the LayerTemplate0
+        super().__init__(arch)
+
+    def build_initial_layer(self):
+        """Build the initial layer"""
+        return self.initial_circuit
+
+    def build_rotation_block(self):
+        """Build rotation block"""
+        rotation_layers = []
+        for rot in self.rotation_ops:
+            rotation_layers.append(
+                self.rotation_layer(
+                    op=rot, n_wires=self.n_wires, **self.rotation_layer_params
+                )
+            )
+        return rotation_layers
+
+    def build_entanglement_block(self):
+        """Build entanglement block"""
+        entanglement_layers = []
+        for entanglement in self.entanglement_ops:
+            entanglement_layers.append(
+                self.entanglement_layer(
+                    op=entanglement,
+                    n_wires=self.n_wires,
+                    **self.entanglement_layer_params,
+                )
+            )
+        return entanglement_layers
+
+    def build_layers(self):
+        """Build nlocal circuit"""
+        layers_all = tq.QuantumModuleList()
+
+        # add the initial circuit
+        initial_circuit = self.build_initial_layer()
+        if initial_circuit is not None:
+            layers_all.append(initial_circuit)
+
+        # repeat for each rep
+        for _ in range(self.reps):
+            # add rotation blocks to the qubits
+            layers_all.extend(self.build_rotation_block())
+
+            # add entanglement blocks to the qubits
+            layers_all.extend(self.build_entanglement_block())
+
+        # add final rotation layer
+        if not self.skip_final_rotation_layer:
+            layers_all.extend(self.build_rotation_block())
+
+        # return QuantumModuleList
+        return layers_all
+
+
+class TwoLocal(NLocal):
+    """Layer Template for a TwoLocal Class
+
+    Args:
+        rotation_ops (list): gates for the rotation layer as a list of torchquantum operations
+        entanglement_ops (list): gates for the entanglement layer as a list of torchquantum operations
+        arch (dict): circuit architecture in a dictionary format
+        rotation_layer (torchquantum.QuantumModule): type of rotation layer in a torchquantum.QuantumModule format
+        entanglement_layer (str): type of entanglement layer in a string ("linear", "reverse_linear", "circular", "full") or tq.QuantumModule format
+        reps (int): number of reptitions of the rotation and entanglement layers in a integer format
+        entanglement_layer_params (dict): additional parameters for the entanglement layer in a dictionary forma
+        initial_circuit (torchquantum.QuantumModule): initial gates or layer in a QuantumModule formatt
+        skip_final_rotation_layer (bool): whether or not to add the final rotation layer as a boolean
+    """
+
+    def __init__(
+        self,
+        rotation_ops: list = None,
+        entanglement_ops: list = None,
+        arch: dict = None,
+        rotation_layer: tq.QuantumModule = Op1QAllLayer,
+        entanglement_layer: str = "linear",
+        reps: int = 1,
+        entanglement_layer_params: dict = {},
+        initial_circuit: tq.QuantumModule = None,
+        skip_final_rotation_layer: bool = False,
+    ):
+        # if passed as string, determine entanglement type
+        if entanglement_layer == "linear":
+            entanglement_layer = Op2QAllLayer
+        elif entanglement_layer == "reverse_linear":
+            entanglement_layer = Op2QAllLayer
+            entanglement_layer_params = {"wire_reverse": True}
+        elif entanglement_layer == "circular":
+            entanglement_layer = Op2QAllLayer
+            entanglement_layer_params = {"circular": True}
+        elif entanglement_layer == "full":
+            entanglement_layer = Op2QDenseLayer
+
+        # initialize
+        super().__init__(
+            arch=arch,
+            rotation_ops=rotation_ops,
+            rotation_layer=rotation_layer,
+            rotation_layer_params={"has_params": True, "trainable": True},
+            entanglement_ops=entanglement_ops,
+            entanglement_layer=entanglement_layer,
+            entanglement_layer_params=entanglement_layer_params,
+            initial_circuit=initial_circuit,
+            reps=reps,
+            skip_final_rotation_layer=skip_final_rotation_layer,
+        )
+
+
+class ExcitationPreserving(TwoLocal):
+    """Layer Template for a ExcitationPreserving circuit
+
+    Args:
+        arch (dict): circuit architecture in a dictionary format
+        entanglement_layer (str): type of entanglement layer in a string ("linear", "reverse_linear", "circular", "full") or tq.QuantumModule format
+        reps (int): number of reptitions of the rotation and entanglement layers in a integer format
+        skip_final_rotation_layer (bool): whether or not to add the final rotation layer as a boolean
+    """
+
+    def __init__(
+        self,
+        arch: dict = None,
+        entanglement_layer: str = "full",
+        reps: int = 3,
+        skip_final_rotation_layer: bool = False,
+    ):
+        # construct circuit with rotation layers of RZ and entanglement with RXX and RYY
+        super().__init__(
+            arch=arch,
+            rotation_ops=[tq.RZ],
+            entanglement_ops=[tq.RXX, tq.RYY],
+            entanglement_layer=entanglement_layer,
+            entanglement_layer_params={"has_params": True, "trainable": True},
+            reps=reps,
+            skip_final_rotation_layer=skip_final_rotation_layer,
+        )
+
+
+class EfficientSU2(TwoLocal):
+    """Layer Template for a EfficientSU2 circuit
+
+    Args:
+        arch (dict): circuit architecture in a dictionary format
+        entanglement_layer (str): type of entanglement layer in a string ("linear", "reverse_linear", "circular", "full") or tq.QuantumModule format
+        reps (int): number of reptitions of the rotation and entanglement layers in a integer format
+        skip_final_rotation_layer (bool): whether or not to add the final rotation layer as a boolean
+    """
+
+    def __init__(
+        self,
+        arch: dict = None,
+        entanglement_layer: str = "reverse_linear",
+        reps: int = 3,
+        skip_final_rotation_layer: bool = False,
+    ):
+        # construct circuit with rotation layers of RY and RZ and entanglement with CX
+        super().__init__(
+            arch=arch,
+            rotation_ops=[tq.RY, tq.RZ],
+            entanglement_ops=[tq.CNOT],
+            entanglement_layer=entanglement_layer,
+            reps=reps,
+            skip_final_rotation_layer=skip_final_rotation_layer,
+        )
+
+
+class RealAmplitudes(TwoLocal):
+    """Layer Template for a RealAmplitudes circuit
+
+    Args:
+        arch (dict): circuit architecture in a dictionary format
+        entanglement_layer (str): type of entanglement layer in a string ("linear", "reverse_linear", "circular", "full") or tq.QuantumModule format
+        reps (int): number of reptitions of the rotation and entanglement layers in a integer format
+        skip_final_rotation_layer (bool): whether or not to add the final rotation layer as a boolean
+    """
+
+    def __init__(
+        self,
+        arch: dict = None,
+        entanglement_layer: str = "reverse_linear",
+        reps: int = 3,
+        skip_final_rotation_layer: bool = False,
+    ):
+        # construct circuit with rotation layers of RY and entanglement with CX
+        super().__init__(
+            arch=arch,
+            rotation_ops=[tq.RY],
+            entanglement_ops=[tq.CNOT],
+            entanglement_layer=entanglement_layer,
+            reps=reps,
+            skip_final_rotation_layer=skip_final_rotation_layer,
+        )
+
+
+class PauliTwoDesign(TwoLocal):
+    """Layer Template for a PauliTwoDesign circuit
+
+    Args:
+        arch (dict): circuit architecture in a dictionary format
+        entanglement_layer (str): type of entanglement layer in a string ("linear", "reverse_linear", "circular", "full") or tq.QuantumModule format
+        reps (int): number of reptitions of the rotation and entanglement layers in a integer format
+        skip_final_rotation_layer (bool): whether or not to add the final rotation layer as a boolean
+    """
+
+    def __init__(
+        self,
+        arch: dict = None,
+        entanglement_layer: str = "reverse_linear",
+        reps: int = 3,
+        skip_final_rotation_layer: bool = False,
+        seed: int = 0,
+    ):
+        # set seed
+        self.seed = seed
+        # construct circuit with entanglement with CX
+        super().__init__(
+            arch=arch,
+            entanglement_ops=[tq.CNOT],
+            entanglement_layer=entanglement_layer,
+            reps=reps,
+            skip_final_rotation_layer=skip_final_rotation_layer,
+        )
+
+    def build_initial_layer(self):
+        # add an initial layer of ry with rotation pi/4
+        return tq.QuantumModule.from_op_history(
+            [
+                {"name": "ry", "wires": wire, "params": torch.pi / 4}
+                for wire in range(self.arch["n_wires"])
+            ]
+        )
+
+    def build_rotation_block(self):
+        # make a random layer of rotations
+        return [RandomOp1All(n_wires=self.n_wires, seed=self.seed)]
