Merge branch 'main' into fix_fidelity

.github/workflows/deploy-code.yml

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -20,6 +20,9 @@ on:
 jobs:
   code_publish:
     runs-on: ubuntu-latest
+    environment: release
+    permissions:
+      id-token: write
     strategy:
       matrix:
         python-version: [3.8]
@@ -28,12 +31,12 @@ jobs:
       - uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
+      - name: Install deps
+        run: pip install -U pip setuptools virtualenv wheel
+      - name: Build sdist
+        run: python3 setup.py sdist bdist_wheel
+      - uses: actions/upload-artifact@v3
+        with:
+          path: ./dist/*
       - name: Deploy to Pypi
-        env:
-          TWINE_PASSWORD: ${{ secrets.TWINE_PASSWORD }}
-          TWINE_USERNAME: qiskit
-        run : |
-          pip install -U twine pip setuptools virtualenv wheel
-          python3 setup.py sdist bdist_wheel
-          twine upload dist/qiskit*
-        shell: bash
+        uses: pypa/gh-action-pypi-publish@release/v1

qiskit_algorithms/optimizers/gsls.py

@@ -119,8 +119,8 @@ def minimize(
             var_lb = np.array([-np.inf] * x0.size)
             var_ub = np.array([np.inf] * x0.size)
         else:
-            var_lb = np.array([l for (l, _) in bounds])
-            var_ub = np.array([u for (_, u) in bounds])
+            var_lb = np.array([l if l is not None else -np.inf for (l, _) in bounds])
+            var_ub = np.array([u if u is not None else np.inf for (_, u) in bounds])
 
         x, fun_, nfev, _ = self.ls_optimize(x0.size, fun, x0, var_lb, var_ub)
 

qiskit_algorithms/state_fidelities/base_state_fidelity.py

@@ -21,6 +21,7 @@
 
 from qiskit import QuantumCircuit
 from qiskit.circuit import ParameterVector
+from qiskit.primitives.utils import _circuit_key
 
 from ..algorithm_job import AlgorithmJob
 
@@ -172,8 +173,8 @@ def _construct_circuits(
         circuits = []
         for (circuit_1, circuit_2) in zip(circuits_1, circuits_2):
 
-            # TODO: improve caching, what if the circuit is modified without changing the id?
-            circuit = self._circuit_cache.get((id(circuit_1), id(circuit_2)))
+            # Use the same key for circuits as qiskit.primitives use.
+            circuit = self._circuit_cache.get((_circuit_key(circuit_1), _circuit_key(circuit_2)))
 
             if circuit is not None:
                 circuits.append(circuit)
@@ -192,7 +193,7 @@ def _construct_circuits(
                 )
                 circuits.append(circuit)
                 # update cache
-                self._circuit_cache[id(circuit_1), id(circuit_2)] = circuit
+                self._circuit_cache[_circuit_key(circuit_1), _circuit_key(circuit_2)] = circuit
 
         return circuits
 

releasenotes/notes/fix_fidelity_cache-3a4cc328344716e0.yaml

@@ -0,0 +1,7 @@
+---
+fixes:
+  - |
+    Fixes internal cache used by state fidelities so that circuits are cached
+    using the same generated key method as that used by the reference primitives.
+    This avoids a potential incorrect result that could have occurred with the
+    key as it was before when id() was used.

releasenotes/notes/fix_gsls_bounds-29d4a7506130cd69.yaml

@@ -0,0 +1,6 @@
+---
+fixes:
+  - |
+    Fixes :class:`.GSLS` optimizer :meth:`~.GSLS.minimize` so that if the ``bounds`` parameter
+    is passed with tuples that have entries of ``None`` then the entry is treated
+    as equivalent to infinity.

test/optimizers/test_optimizers.py

@@ -160,6 +160,13 @@ def test_gsls(self):
         self.assertLessEqual(x_value, 0.01)
         self.assertLessEqual(n_evals, 10000)
 
+        with self.subTest("Bounds (None, None)"):
+            algorithm_globals.random_seed = 1
+            res = optimizer.minimize(rosen, x_0, bounds=[(None, None)] * len(x_0))
+
+            self.assertLessEqual(res.fun, 0.01)
+            self.assertLessEqual(res.nfev, 10000)
+
     def test_scipy_optimizer(self):
         """scipy_optimizer test"""
         optimizer = SciPyOptimizer("BFGS", options={"maxiter": 1000})