From 57d21b93b3572386c140d0bde8b008489f734819 Mon Sep 17 00:00:00 2001
From: Steve Wood <40241007+woodsp-ibm@users.noreply.github.com>
Date: Mon, 9 Oct 2023 09:03:28 -0400
Subject: [PATCH] Remove numpy 1.25 constraint (#83)
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Co-authored-by: Elena Peña Tapia <57907331+ElePT@users.noreply.github.com>
---
 constraints.txt                         | 5 +----
 qiskit_algorithms/optimizers/snobfit.py | 3 +--
 qiskit_algorithms/optimizers/spsa.py    | 2 +-
 3 files changed, 3 insertions(+), 7 deletions(-)

diff --git a/constraints.txt b/constraints.txt
index a442313e..248be8c7 100644
--- a/constraints.txt
+++ b/constraints.txt
@@ -1,5 +1,2 @@
-# Numpy 1.25 deprecated some behaviours that we used, and caused the isometry
-# tests to flake. See https://github.com/Qiskit/qiskit-terra/issues/10305,
-# remove pin when resolving that.
-numpy<1.25
+# Constraints may be listed here
 
diff --git a/qiskit_algorithms/optimizers/snobfit.py b/qiskit_algorithms/optimizers/snobfit.py
index 9269df5f..68ab305a 100644
--- a/qiskit_algorithms/optimizers/snobfit.py
+++ b/qiskit_algorithms/optimizers/snobfit.py
@@ -57,8 +57,7 @@ def __init__(
                 See https://github.com/scikit-quant/scikit-quant/issues/24 for more details.
         """
         # check version
-        version = tuple(map(int, np.__version__.split(".")))
-        if version >= (1, 24, 0):
+        if tuple(map(int, np.__version__.split(".")[:2])) >= (1, 24):
             raise AlgorithmError(
                 "SnobFit is incompatible with NumPy 1.24.0 or above, please "
                 "install a previous version. See also scikit-quant/scikit-quant#24."
diff --git a/qiskit_algorithms/optimizers/spsa.py b/qiskit_algorithms/optimizers/spsa.py
index 79245815..eb18b618 100644
--- a/qiskit_algorithms/optimizers/spsa.py
+++ b/qiskit_algorithms/optimizers/spsa.py
@@ -629,7 +629,7 @@ def minimize(
         logger.info("SPSA: Finished in %s", time() - start)
 
         if self.last_avg > 1:
-            x = np.mean(last_steps, axis=0)  # type: ignore[call-overload]
+            x = np.mean(np.asarray(last_steps), axis=0)
 
         result = OptimizerResult()
         result.x = x