Merge pull request #252 from nikhilkhatri/main

Allow fixed parameters in GeneralEncoder

Author: 01110011011101010110010001101111

test/encoding/test_encodings.py

@@ -0,0 +1,81 @@
+"""
+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.
+"""
+
+# test the controlled unitary function
+
+
+import torchquantum as tq
+import torch
+from test.utils import check_all_close
+
+
+def test_GeneralEncoder():
+
+    parameterised_funclist = [
+        {"input_idx": [0], "func": "crx", "wires": [1, 0]},
+        {"input_idx": [1, 2, 3], "func": "u3", "wires": [1]},
+        {"input_idx": [4], "func": "ry", "wires": [0]},
+        {"input_idx": [5], "func": "ry", "wires": [1]},
+    ]
+
+    semiparam_funclist = [
+        {"params": [0.2], "func": "crx", "wires": [1, 0]},
+        {"params": [0.3, 0.4, 0.5], "func": "u3", "wires": [1]},
+        {"input_idx": [0], "func": "ry", "wires": [0]},
+        {"input_idx": [1], "func": "ry", "wires": [1]},
+    ]
+
+    expected_states = torch.complex(
+        torch.Tensor(
+            [[0.8423, 0.4474, 0.2605, 0.1384], [0.7649, 0.5103, 0.3234, 0.2157]]
+        ),
+        torch.Tensor(
+            [[-0.0191, 0.0522, -0.0059, 0.0162], [-0.0233, 0.0483, -0.0099, 0.0204]]
+        ),
+    )
+
+    parameterised_enc = tq.GeneralEncoder(parameterised_funclist)
+    semiparam_enc = tq.GeneralEncoder(semiparam_funclist)
+
+    param_vec = torch.Tensor(
+        [[0.2, 0.3, 0.4, 0.5, 0.6, 0.7], [0.2, 0.3, 0.4, 0.5, 0.8, 0.9]]
+    )
+    semiparam_vec = torch.Tensor([[0.6, 0.7], [0.8, 0.9]])
+
+    qd = tq.QuantumDevice(n_wires=2)
+
+    qd.reset_states(bsz=2)
+    parameterised_enc(qd, param_vec)
+    state1 = qd.get_states_1d()
+
+    qd.reset_states(bsz=2)
+    semiparam_enc(qd, semiparam_vec)
+    state2 = qd.get_states_1d()
+
+    check_all_close(state1, state2)
+    check_all_close(state1, expected_states)
+
+
+if __name__ == "__main__":
+    test_GeneralEncoder()

torchquantum/encoding/encodings.py

@@ -81,6 +81,18 @@ class GeneralEncoder(Encoder, metaclass=ABCMeta):
     {'input_idx': [12, 13, 14], 'func': 'u3', 'wires': [3]},
     {'input_idx': [15], 'func': 'u1', 'wires': [3]},
     ]
+
+    Example 3:
+    [
+    {'params': [0.25], 'func': 'rx', 'wires': [0]},
+    {'params': [0.25], 'func': 'rx', 'wires': [1]},
+    {'params': [0.25], 'func': 'rx', 'wires': [2]},
+    {'params': [0.25], 'func': 'rx', 'wires': [3]},
+    {'input_idx': [0], 'func': 'ry', 'wires': [0]},
+    {'input_idx': [1], 'func': 'ry', 'wires': [1]},
+    {'input_idx': [2], 'func': 'ry', 'wires': [2]},
+    {'input_idx': [3], 'func': 'ry', 'wires': [3]}
+    ]
     """
 
     def __init__(self, func_list):
@@ -91,7 +103,11 @@ def __init__(self, func_list):
     def forward(self, qdev: tq.QuantumDevice, x):
         for info in self.func_list:
             if tq.op_name_dict[info["func"]].num_params > 0:
-                params = x[:, info["input_idx"]]
+                # If params are provided in encoder, use those,
+                #  else use params from x
+                params = (torch.Tensor(info["params"]).repeat(x.shape[0], 1)
+                          if info.get("params")
+                          else x[:, info["input_idx"]])
             else:
                 params = None
             func_name_dict[info["func"]](