From 55e6ca693136290bdd297053d0af11d4bf4b3ab9 Mon Sep 17 00:00:00 2001
From: Declan Millar <declan.millar@ibm.com>
Date: Mon, 26 Sep 2022 22:48:04 +0100
Subject: [PATCH] dict -> dict[str, Any]

---
 qiskit/algorithms/minimum_eigensolvers/vqe.py | 21 ++++++++++---------
 qiskit/algorithms/observables_evaluator.py    |  5 +++--
 2 files changed, 14 insertions(+), 12 deletions(-)

diff --git a/qiskit/algorithms/minimum_eigensolvers/vqe.py b/qiskit/algorithms/minimum_eigensolvers/vqe.py
index 1cc7312f3926..26195b704072 100644
--- a/qiskit/algorithms/minimum_eigensolvers/vqe.py
+++ b/qiskit/algorithms/minimum_eigensolvers/vqe.py
@@ -17,6 +17,7 @@
 import logging
 from time import time
 from collections.abc import Callable, Sequence
+from typing import Any
 
 import numpy as np
 
@@ -95,10 +96,10 @@ def my_minimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
             :class:`.Minimizer` protocol.
         gradient (BaseEstimatorGradient | None): An optional estimator gradient to be used with the
             optimizer.
-        callback (Callable[[int, np.ndarray, float, dict], None] | None): A callback that can access
-            the intermediate data at each optimization step. These data are: the evaluation count,
-            the optimizer parameters for the ansatz, the evaluated mean, and the metadata
-            dictionary.
+        callback (Callable[[int, np.ndarray, float, dict[str, Any]], None] | None): A callback that
+            can access the intermediate data at each optimization step. These data are: the
+            evaluation count, the optimizer parameters for the ansatz, the evaluated mean, and the
+            metadata dictionary.
 
     References:
         [1] Peruzzo et al, "A variational eigenvalue solver on a quantum processor"
@@ -113,7 +114,7 @@ def __init__(
         *,
         gradient: BaseEstimatorGradient | None = None,
         initial_point: Sequence[float] | None = None,
-        callback: Callable[[int, np.ndarray, float, dict], None] | None = None,
+        callback: Callable[[int, np.ndarray, float, dict[str, Any]], None] | None = None,
     ) -> None:
         r"""
         Args:
@@ -124,11 +125,11 @@ def __init__(
                 can either be a Qiskit :class:`.Optimizer` or a callable implementing the
                 :class:`.Minimizer` protocol.
             gradient: An optional estimator gradient to be used with the optimizer.
-            initial_point: An optional initial point (i.e. initial parameter values) for the optimizer.
-                The length of the initial point must match the number of :attr:`ansatz` parameters.
-                If ``None``, a random point will be generated within certain parameter bounds.
-                ``VQE`` will look to the ansatz for these bounds. If the ansatz does not specify
-                bounds, bounds of :math:`-2\pi`, :math:`2\pi` will be used.
+            initial_point: An optional initial point (i.e. initial parameter values) for the
+                optimizer. The length of the initial point must match the number of :attr:`ansatz`
+                parameters. If ``None``, a random point will be generated within certain parameter
+                bounds. ``VQE`` will look to the ansatz for these bounds. If the ansatz does not
+                specify bounds, bounds of :math:`-2\pi`, :math:`2\pi` will be used.
             callback: A callback that can access the intermediate data at each optimization step.
                 These data are: the evaluation count, the optimizer parameters for the ansatz, the
                 estimated value, and the metadata dictionary.
diff --git a/qiskit/algorithms/observables_evaluator.py b/qiskit/algorithms/observables_evaluator.py
index 546e1562ddce..f1ca00c4b5d2 100644
--- a/qiskit/algorithms/observables_evaluator.py
+++ b/qiskit/algorithms/observables_evaluator.py
@@ -14,6 +14,7 @@
 
 from __future__ import annotations
 from collections.abc import Sequence
+from typing import Any
 
 import numpy as np
 
@@ -31,7 +32,7 @@ def estimate_observables(
     observables: ListOrDict[BaseOperator | PauliSumOp],
     parameter_values: Sequence[float] | None = None,
     threshold: float = 1e-12,
-) -> ListOrDict[tuple[complex, dict]]:
+) -> ListOrDict[tuple[complex, dict[str, Any]]]:
     """
     Accepts a sequence of operators and calculates their expectation values - means
     and metadata. They are calculated with respect to a quantum state provided. A user
@@ -94,7 +95,7 @@ def _handle_zero_ops(
 def _prepare_result(
     observables_results: list[tuple[complex, dict]],
     observables: ListOrDict[BaseOperator | PauliSumOp],
-) -> ListOrDict[tuple[complex, dict]]:
+) -> ListOrDict[tuple[complex, dict[str, Any]]]:
     """
     Prepares a list of tuples of eigenvalues and metadata tuples from
     ``observables_results`` and ``observables``.