create a new branch for upgrading to qiskit 1.3

.cursorignore

@@ -0,0 +1,4 @@
+# Add directories or file patterns to ignore during indexing (e.g. foo/ or *.csv)
+docs/
+examples/
+figs/

examples/QuantumNAS/quantumnas.ipynb

@@ -4056,7 +4056,8 @@
       "toc_visible": true
     },
     "kernelspec": {
-      "display_name": "Python 3",
+      "display_name": "torchquantum",
+      "language": "python",
       "name": "python3"
     },
     "language_info": {
@@ -4069,7 +4070,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.16"
+      "version": "3.9.20"
     },
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {

test/qiskit_plugin_test.py

@@ -36,7 +36,7 @@
     get_expectations_from_counts,
     find_global_phase,
 )
-from test.static_mode_test import QLayer as AllRandomLayer
+from static_mode_test import QLayer as AllRandomLayer
 from torchquantum.plugin import tq2qiskit
 from torchquantum.macro import F_DTYPE
 
@@ -54,11 +54,12 @@ def unitary_tq_vs_qiskit_test():
             qiskit_compatible=True,
         )
 
-        unitary_tq = q_layer.get_unitary(q_dev, x)
+        # unitary_tq = q_layer.get_unitary(q_dev, x)
+        unitary_tq = q_layer.get_unitary(x)
         unitary_tq = switch_little_big_endian_matrix(unitary_tq.data.numpy())
 
         # qiskit
-        circ = tq2qiskit(q_layer, x)
+        circ = tq2qiskit(q_dev, q_layer, x)
         simulator = Aer.get_backend("unitary_simulator")
         result = execute(circ, simulator).result()
         unitary_qiskit = result.get_unitary(circ)

test/static_mode_test.py

@@ -155,7 +155,20 @@ def build_random_funcs(self):
         cnt = 0
         while cnt < self.n_funcs:
             func = np.random.choice(self.funcs)
-            n_func_wires = op_name_dict[func]().num_wires
+            # print(f"Selected function: {func}")
+
+            """
+            ORIGINAL: n_func_wires = op_name_dict[func]().num_wires
+            Changed to avoid initialization error with QubitUnitaryFast which requires 
+            parameters during instantiation. Instead, we access num_wires directly 
+            from the class since it's a class attribute.
+            """
+
+            op_class = op_name_dict[func]
+            # print(f"Operator class: {op_class}")
+            # print(f"Number of wires: {op_class.num_wires}")
+            n_func_wires = op_class.num_wires
+
             if n_func_wires > self.n_wires:
                 continue
             cnt += 1
@@ -191,8 +204,9 @@ def forward(self, q_device: tq.QuantumDevice, x):
             self.func_list, self.func_wires_list, self.func_inverse
         ):
             n_func_wires = len(func_wires)
-            n_func_params = op_name_dict[func]().num_params
-
+            op_class = op_name_dict[func]
+            # n_func_params = op_name_dict[func]().num_params
+            n_func_params = op_class.num_params
             if n_func_params == 0:
                 if func in ["multicnot", "multixcnot"]:
                     func_name_dict[func](

torchquantum/functional/u1.py

@@ -110,7 +110,7 @@ def u1(
 
     """
     name = "u1"
-    mat = mat_dict[name]
+    mat = _u1_mat_dict[name]
     gate_wrapper(
         name=name,
         mat=mat,
@@ -157,7 +157,7 @@ def cu1(
 
     """
     name = "cu1"
-    mat = mat_dict[name]
+    mat = _u1_mat_dict[name]
     gate_wrapper(
         name=name,
         mat=mat,

torchquantum/functional/u2.py

@@ -109,7 +109,7 @@ def u2(
 
     """
     name = "u2"
-    mat = mat_dict[name]
+    mat = _u2_mat_dict[name]
     gate_wrapper(
         name=name,
         mat=mat,
@@ -156,7 +156,7 @@ def cu2(
 
     """
     name = "cu2"
-    mat = mat_dict[name]
+    mat = _u2_mat_dict[name]
     gate_wrapper(
         name=name,
         mat=mat,

torchquantum/graph/graphs.py

@@ -156,10 +156,17 @@ def add_func(
         if not self.is_list_finish:
             # graph construction is not finished, build a new operation and
             # add the operation to the graph
-            op = tq.op_name_dict[name]()
-            op.params = params
-            op.n_wires = n_wires
-            op.wires = wires
+            print(tq.op_name_dict[name])
+            # op = tq.op_name_dict[name]()
+            op_class = tq.op_name_dict[name]
+            op = op_class(has_params=True if params is not None else False,
+              trainable=False,
+              init_params=params,
+              n_wires=n_wires,
+              wires=wires)
+            # op.params = params
+            # op.n_wires = n_wires
+            # op.wires = wires
             op.graph = tq.QuantumGraph()
             op.parent_graph = parent_graph
             op.static_mode = True

torchquantum/measurement/measurements.py

@@ -42,8 +42,22 @@ def measure(qdev, n_shots=1024, draw_id=None):
     Returns:
         distribution of bitstrings
     """
-    bitstring_candidates = gen_bitstrings(qdev.n_wires)
+
+    """
+    In measure function, the statevector is copied to the CPU and 
+    a list of all possible 2^n bitstrings is constructed to do the sampling. 
+    This is again a huge CPU memory and runtime overhead since sampling can done on the GPU directly and efficiently. 
+    Here is a sketch of how that might look like in PyTorch using Inverse transform sampling method: 
+    Calculate squared amplitudes on GPU using troch.abs and troch.square. 
+    Calculate the cumulative distribution function using troch.cumsum. 
+    Generate random numbers (as many as the required number of samples) between 0 and 1 using torch.rand. 
+    Find the index of each random number inside the cumulative distribution using troch.searchsorted. 
+    Copy the indices to the CPU and convert each number to its bitstring binary representation.
+    """
+
+    bitstring_candidates = gen_bitstrings(qdev.n_wires)  # length is 2 to the power of n_wires
     if isinstance(qdev, tq.QuantumDevice):
+        # length is 2 to the power of n_wires
         state_mag = qdev.get_states_1d().abs().detach().cpu().numpy()
     elif isinstance(qdev, tq.NoiseDevice):
         '''

torchquantum/operator/op_types.py

@@ -3,11 +3,16 @@
 import torchquantum as tq
 import torchquantum.functional.functionals as tqf
 import numpy as np
+import logging
 from abc import ABCMeta
 from ..macro import C_DTYPE, F_DTYPE
 from typing import Iterable, Union, List
 from enum import IntEnum
 
+
+# Add logging init
+logger = logging.getLogger(__name__)
+
 __all__ = [
     "Operator",
     "Operation",