Deprecation of algorithm utils (algorithm_globals and validation)

qiskit/algorithms/amplitude_amplifiers/grover.py

@@ -296,7 +296,9 @@ def amplify(self, amplification_problem: AmplificationProblem) -> "GroverResult"
 
             # sample from [0, power) if specified
             if self._sample_from_iterations:
-                power = algorithm_globals.random.integers(power)
+                with warnings.catch_warnings():
+                    warnings.filterwarnings("ignore", category=DeprecationWarning)
+                    power = algorithm_globals.random.integers(power)
             # Run a grover experiment for a given power of the Grover operator.
             if self._sampler is not None:
                 qc = self.construct_circuit(amplification_problem, power, measurement=True)

qiskit/algorithms/eigensolvers/numpy_eigensolver.py

@@ -14,6 +14,7 @@
 
 from __future__ import annotations
 
+import warnings
 from typing import Callable, Union, List, Optional
 import logging
 import numpy as np
@@ -61,7 +62,10 @@ def __init__(
                 elements that satisfies the criterion is smaller than ``k``, then the returned list will
                 have fewer elements and can even be empty.
         """
-        validate_min("k", k, 1)
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            validate_min("k", k, 1)
+
         super().__init__()
 
         self._in_k = k

qiskit/algorithms/minimum_eigensolvers/adapt_vqe.py

@@ -18,6 +18,7 @@
 
 import re
 import logging
+import warnings
 from typing import Any
 
 import numpy as np
@@ -129,8 +130,10 @@ def __init__(
             threshold: once all gradients have an absolute value smaller than this threshold, the
                 algorithm has converged and terminates. Defaults to ``1e-5``.
         """
-        validate_min("gradient_threshold", gradient_threshold, 1e-15)
-        validate_min("eigenvalue_threshold", eigenvalue_threshold, 1e-15)
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            validate_min("gradient_threshold", gradient_threshold, 1e-15)
+            validate_min("eigenvalue_threshold", eigenvalue_threshold, 1e-15)
 
         self.solver = solver
         self.gradient_threshold = gradient_threshold

qiskit/algorithms/minimum_eigensolvers/qaoa.py

@@ -14,6 +14,7 @@
 
 from __future__ import annotations
 
+import warnings
 from typing import Callable, Any
 import numpy as np
 
@@ -118,7 +119,9 @@ def __init__(
                 These data are: the evaluation count, the optimizer parameters for the ansatz, the
                 evaluated value, the metadata dictionary.
         """
-        validate_min("reps", reps, 1)
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            validate_min("reps", reps, 1)
 
         self.reps = reps
         self.mixer = mixer

qiskit/algorithms/optimizers/aqgd.py

@@ -16,6 +16,7 @@
 import logging
 from collections.abc import Callable
 from typing import Any
+import warnings
 
 import numpy as np
 from qiskit.utils.validation import validate_range_exclusive_max
@@ -89,7 +90,9 @@ def __init__(
                 "`eta`, and `momentum` must have the same length."
             )
         for m in momentum:
-            validate_range_exclusive_max("momentum", m, 0, 1)
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=DeprecationWarning)
+                validate_range_exclusive_max("momentum", m, 0, 1)
 
         self._eta = eta
         self._maxiter = maxiter

qiskit/algorithms/optimizers/gsls.py

@@ -14,6 +14,8 @@
 
 from __future__ import annotations
 
+import warnings
+
 from collections.abc import Callable
 from typing import Any, SupportsFloat
 import numpy as np
@@ -262,7 +264,9 @@ def sample_points(
         Returns:
             A tuple containing the sampling points and the directions.
         """
-        normal_samples = algorithm_globals.random.normal(size=(num_points, n))
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            normal_samples = algorithm_globals.random.normal(size=(num_points, n))
         row_norms = np.linalg.norm(normal_samples, axis=1, keepdims=True)
         directions = normal_samples / row_norms
         points = x + self._options["sampling_radius"] * directions

qiskit/algorithms/optimizers/p_bfgs.py

@@ -16,6 +16,7 @@
 import logging
 import multiprocessing
 import platform
+import warnings
 from collections.abc import Callable
 from typing import SupportsFloat
 
@@ -73,7 +74,9 @@ def __init__(
             kwargs: additional kwargs for scipy.optimize.minimize.
         """
         if max_processes:
-            validate_min("max_processes", max_processes, 1)
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=DeprecationWarning)
+                validate_min("max_processes", max_processes, 1)
 
         if options is None:
             options = {}
@@ -138,7 +141,9 @@ def optimize_runner(_queue, _i_pt):  # Multi-process sampling
         # Start off as many other processes running the optimize (can be 0)
         processes = []
         for _ in range(num_procs):
-            i_pt = algorithm_globals.random.uniform(low, high)  # Another random point in bounds
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=DeprecationWarning)
+                i_pt = algorithm_globals.random.uniform(low, high)  # Another random point in bounds
             proc = multiprocessing.Process(target=optimize_runner, args=(queue, i_pt))
             processes.append(proc)
             proc.start()

qiskit/algorithms/optimizers/scipy_optimizer.py

@@ -13,6 +13,7 @@
 """Wrapper class of scipy.optimize.minimize."""
 from __future__ import annotations
 
+import warnings
 from collections.abc import Callable
 from typing import Any
 
@@ -73,7 +74,11 @@ def __init__(
         self._initial_point_support_level = OptimizerSupportLevel.required
 
         self._options = options if options is not None else {}
-        validate_min("max_evals_grouped", max_evals_grouped, 1)
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            validate_min("max_evals_grouped", max_evals_grouped, 1)
+
         self._max_evals_grouped = max_evals_grouped
         self._kwargs = kwargs
 

qiskit/algorithms/optimizers/spsa.py

@@ -687,12 +687,16 @@ def optimize(
 def bernoulli_perturbation(dim, perturbation_dims=None):
     """Get a Bernoulli random perturbation."""
     if perturbation_dims is None:
-        return 1 - 2 * algorithm_globals.random.binomial(1, 0.5, size=dim)
-
-    pert = 1 - 2 * algorithm_globals.random.binomial(1, 0.5, size=perturbation_dims)
-    indices = algorithm_globals.random.choice(
-        list(range(dim)), size=perturbation_dims, replace=False
-    )
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            return 1 - 2 * algorithm_globals.random.binomial(1, 0.5, size=dim)
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=DeprecationWarning)
+        pert = 1 - 2 * algorithm_globals.random.binomial(1, 0.5, size=perturbation_dims)
+        indices = algorithm_globals.random.choice(
+            list(range(dim)), size=perturbation_dims, replace=False
+        )
     result = np.zeros(dim)
     result[indices] = pert
 

qiskit/algorithms/optimizers/umda.py

@@ -14,6 +14,8 @@
 
 from __future__ import annotations
 
+import warnings
+
 from collections.abc import Callable
 from typing import Any
 import numpy as np
@@ -173,10 +175,11 @@ def _new_generation(self):
         """Build a new generation sampled from the vector of probabilities.
         Updates the generation pandas dataframe
         """
-
-        gen = algorithm_globals.random.normal(
-            self._vector[0, :], self._vector[1, :], [self._size_gen, self._n_variables]
-        )
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            gen = algorithm_globals.random.normal(
+                self._vector[0, :], self._vector[1, :], [self._size_gen, self._n_variables]
+            )
 
         self._generation = self._generation[: int(self.ELITE_FACTOR * len(self._generation))]
         self._generation = np.vstack((self._generation, gen))
@@ -228,10 +231,12 @@ def minimize(
 
         self._vector = self._initialization()
 
-        # initialization of generation
-        self._generation = algorithm_globals.random.normal(
-            self._vector[0, :], self._vector[1, :], [self._size_gen, self._n_variables]
-        )
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            # initialization of generation
+            self._generation = algorithm_globals.random.normal(
+                self._vector[0, :], self._vector[1, :], [self._size_gen, self._n_variables]
+            )
 
         for _ in range(self._maxiter):
             self._check_generation(fun)

qiskit/algorithms/time_evolvers/pvqd/pvqd.py

@@ -14,6 +14,7 @@
 from __future__ import annotations
 
 import logging
+import warnings
 from collections.abc import Callable
 
 import numpy as np
@@ -197,7 +198,9 @@ def step(
         loss, gradient = self.get_loss(hamiltonian, ansatz, dt, theta)
 
         if initial_guess is None:
-            initial_guess = algorithm_globals.random.random(self.initial_parameters.size) * 0.01
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=DeprecationWarning)
+                initial_guess = algorithm_globals.random.random(self.initial_parameters.size) * 0.01
 
         if isinstance(self.optimizer, Optimizer):
             optimizer_result = self.optimizer.minimize(loss, initial_guess, gradient)

qiskit/algorithms/utils/validate_initial_point.py

@@ -14,6 +14,7 @@
 
 from __future__ import annotations
 
+import warnings
 from collections.abc import Sequence
 
 import numpy as np
@@ -58,7 +59,9 @@ def validate_initial_point(
             upper_bounds.append(upper if upper is not None else 2 * np.pi)
 
         # sample from within bounds
-        point = algorithm_globals.random.uniform(lower_bounds, upper_bounds)
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            point = algorithm_globals.random.uniform(lower_bounds, upper_bounds)
 
     elif len(point) != expected_size:
         raise ValueError(

qiskit/opflow/evolutions/trotterizations/qdrift.py

@@ -15,6 +15,7 @@
 
 """
 
+import warnings
 from typing import List, Union, cast
 
 import numpy as np
@@ -77,8 +78,10 @@ def convert(self, operator: OperatorBase) -> OperatorBase:
         # The protocol calls for the removal of the individual coefficients,
         # and multiplication by a constant factor.
         scaled_ops = [(op * (factor / op.coeff)).exp_i() for op in operator_iter]
-        sampled_ops = algorithm_globals.random.choice(
-            scaled_ops, size=(int(N * self.reps),), p=weights / lambd
-        )
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            sampled_ops = algorithm_globals.random.choice(
+                scaled_ops, size=(int(N * self.reps),), p=weights / lambd
+            )
 
         return ComposedOp(sampled_ops).reduce()

qiskit/opflow/operator_base.py

@@ -13,6 +13,7 @@
 """OperatorBase Class"""
 
 import itertools
+import warnings
 from abc import ABC, abstractmethod
 from copy import deepcopy
 from typing import Dict, List, Optional, Set, Tuple, Union, cast
@@ -490,20 +491,22 @@ def _check_massive(method: str, matrix: bool, num_qubits: int, massive: bool) ->
         Raises:
             ValueError: Massive is False and number of qubits is greater than 16
         """
-        if num_qubits > 16 and not massive and not algorithm_globals.massive:
-            dim = 2**num_qubits
-            if matrix:
-                obj_type = "matrix"
-                dimensions = f"{dim}x{dim}"
-            else:
-                obj_type = "vector"
-                dimensions = f"{dim}"
-            raise ValueError(
-                f"'{method}' will return an exponentially large {obj_type}, "
-                f"in this case '{dimensions}' elements. "
-                "Set algorithm_globals.massive=True or the method argument massive=True "
-                "if you want to proceed."
-            )
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            if num_qubits > 16 and not massive and not algorithm_globals.massive:
+                dim = 2**num_qubits
+                if matrix:
+                    obj_type = "matrix"
+                    dimensions = f"{dim}x{dim}"
+                else:
+                    obj_type = "vector"
+                    dimensions = f"{dim}"
+                raise ValueError(
+                    f"'{method}' will return an exponentially large {obj_type}, "
+                    f"in this case '{dimensions}' elements. "
+                    "Set algorithm_globals.massive=True or the method argument massive=True "
+                    "if you want to proceed."
+                )
 
     # Printing
 

qiskit/opflow/state_fns/dict_state_fn.py

@@ -13,6 +13,7 @@
 """DictStateFn Class"""
 
 import itertools
+import warnings
 from typing import Dict, List, Optional, Set, Union, cast
 
 import numpy as np
@@ -328,12 +329,14 @@ def sample(
         self, shots: int = 1024, massive: bool = False, reverse_endianness: bool = False
     ) -> Dict[str, float]:
         probs = np.square(np.abs(np.array(list(self.primitive.values()))))
-        unique, counts = np.unique(
-            algorithm_globals.random.choice(
-                list(self.primitive.keys()), size=shots, p=(probs / sum(probs))
-            ),
-            return_counts=True,
-        )
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            unique, counts = np.unique(
+                algorithm_globals.random.choice(
+                    list(self.primitive.keys()), size=shots, p=(probs / sum(probs))
+                ),
+                return_counts=True,
+            )
         counts = dict(zip(unique, counts))
         if reverse_endianness:
             scaled_dict = {bstr[::-1]: (prob / shots) for (bstr, prob) in counts.items()}

qiskit/opflow/state_fns/vector_state_fn.py

@@ -12,7 +12,7 @@
 
 """VectorStateFn Class"""
 
-
+import warnings
 from typing import Dict, List, Optional, Set, Union, cast
 
 import numpy as np
@@ -243,12 +243,14 @@ def sample(
         deterministic_counts = self.primitive.probabilities_dict()
         # Don't need to square because probabilities_dict already does.
         probs = np.array(list(deterministic_counts.values()))
-        unique, counts = np.unique(
-            algorithm_globals.random.choice(
-                list(deterministic_counts.keys()), size=shots, p=(probs / sum(probs))
-            ),
-            return_counts=True,
-        )
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            unique, counts = np.unique(
+                algorithm_globals.random.choice(
+                    list(deterministic_counts.keys()), size=shots, p=(probs / sum(probs))
+                ),
+                return_counts=True,
+            )
         counts = dict(zip(unique, counts))
         if reverse_endianness:
             scaled_dict = {bstr[::-1]: (prob / shots) for (bstr, prob) in counts.items()}