Drop Python 3.7 support

.github/workflows/cron-staging.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9, "3.10", "3.11"]
+        python-version: [3.8, 3.9, "3.10", "3.11"]
         os: ["ubuntu-latest", "macOS-latest", "windows-latest"]
     steps:
       - name: Print Concurrency Group

.github/workflows/main.yml

@@ -15,7 +15,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9, "3.10", "3.11"]
+        python-version: [3.8, 3.9, "3.10", "3.11"]
         os: ["ubuntu-latest", "macOS-latest", "windows-latest"]
     steps:
       - name: Print Concurrency Group

README.md

@@ -1,6 +1,9 @@
 # Qiskit Experiments
 
 [![License](https://img.shields.io/github/license/Qiskit/qiskit-experiments.svg?style=popout-square)](https://opensource.org/licenses/Apache-2.0)
+[![Release](https://img.shields.io/github/release/Qiskit/qiskit-experiments.svg?style=popout-square)](https://github.com/Qiskit/qiskit-experiments/releases)
+![Python](https://img.shields.io/pypi/pyversions/qiskit-experiments.svg?style=popout-square)
+
 
 **Qiskit Experiments** is a repository that builds tools for building, running,
 and analyzing experiments on noisy quantum computers using Qiskit.

docs/tutorials/getting_started.rst

@@ -7,8 +7,8 @@ Installation
 
 Qiskit Experiments is built on top of Qiskit, so we recommend that you first install
 Qiskit following its :external+qiskit:doc:`installation guide <getting_started>`. Qiskit
-Experiments supports the same platforms and Python versions (currently **3.7+**) as
-Qiskit itself.
+Experiments supports the same platforms as Qiskit itself and Python versions 3.8,
+3.9, 3.10, and 3.11.
 
 Qiskit Experiments releases can be installed via the Python package manager ``pip``:
 

pyproject.toml

@@ -4,4 +4,4 @@ build-backend = "setuptools.build_meta"
 
 [tool.black]
 line-length = 100
-target-version = ['py37', 'py38', 'py39', 'py310', 'py311']
+target-version = ['py38', 'py39', 'py310', 'py311']

qiskit_experiments/curve_analysis/base_curve_analysis.py

@@ -162,10 +162,11 @@ def _default_options(cls) -> Options:
                 instance that defines the `self.__call__` method.
             normalization (bool): Set ``True`` to normalize y values within range [-1, 1].
                 Default to ``False``.
-            average_method (str): Method to average the y values when the same x values
-                appear multiple times. One of "sample", "iwv" (i.e. inverse weighted variance),
-                "shots_weighted". See :func:`.mean_xy_data` for details. Default to
-                "shots_weighted".
+            average_method (Literal["sample", "iwv", "shots_weighted"]): Method
+                to average the y values when the same x values
+                appear multiple times. One of "sample", "iwv" (i.e. inverse
+                weighted variance), "shots_weighted". See :func:`.mean_xy_data`
+                for details. Default to "shots_weighted".
             p0 (Dict[str, float]): Initial guesses for the fit parameters.
                 The dictionary is keyed on the fit parameter names.
             bounds (Dict[str, Tuple[float, float]]): Boundary of fit parameters.

qiskit_experiments/framework/__init__.py

@@ -146,3 +146,4 @@
 )
 from .json import ExperimentEncoder, ExperimentDecoder
 from .restless_mixin import RestlessMixin
+from .package_deps import numpy_version

qiskit_experiments/framework/package_deps.py

@@ -0,0 +1,21 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 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
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+"""
+Functions for checking dependency versions.
+"""
+
+from importlib.metadata import version
+
+
+def numpy_version():
+    """Returns the current numpy version in (major, minor) form."""
+    return tuple(map(int, version("numpy").split(".")[:2]))

qiskit_experiments/library/randomized_benchmarking/standard_rb.py

@@ -386,11 +386,7 @@ def _transpiled_circuits(self) -> List[QuantumCircuit]:
                     inst_prop = self.backend.target[op_name].get(qargs, None)
                     if inst_prop is None:
                         continue
-                    try:
-                        schedule = inst_prop.calibration
-                    except AttributeError:
-                        # TODO remove after qiskit-terra/#9681 is in stable release.
-                        schedule = None
+                    schedule = inst_prop.calibration
                     if schedule is None:
                         continue
                     publisher = schedule.metadata.get("publisher", CalibrationPublisher.QISKIT)

qiskit_experiments/library/tomography/qpt_analysis.py

@@ -92,7 +92,8 @@ def _default_options(cls) -> Options:
             preparation_qubits (Sequence[int]): Optional, the physical qubits with tomographic
                 preparations. If not specified will be set to ``[0, ..., N-1]`` for N-qubit
                 tomographic preparations.
-                This can  be a string to select one of the built-in fitters, or a callable to
+            fitter (str or Callable): The fitter function to use for reconstruction.
+                This can be a string to select one of the built-in fitters, or a callable to
                 supply a custom fitter function. See the `Fitter Functions` section for
                 additional information.
             target (str or

qiskit_experiments/library/tomography/qst_analysis.py

@@ -70,7 +70,7 @@ def _default_options(cls) -> Options:
                 :class:`~qiskit_experiments.library.tomography.basis.MeasurementBasis`
                 to use for tomographic state reconstruction.
             fitter (str or Callable): The fitter function to use for reconstruction.
-                This can  be a string to select one of the built-in fitters, or a callable to
+                This can be a string to select one of the built-in fitters, or a callable to
                 supply a custom fitter function. See the `Fitter Functions` section for
                 additional information.
             fitter_options (dict): Any addition kwarg options to be supplied to the fitter

qiskit_experiments/library/tomography/tomography_analysis.py

@@ -26,7 +26,7 @@
 from qiskit.quantum_info.operators.channel.quantum_channel import QuantumChannel
 
 from qiskit_experiments.exceptions import AnalysisError
-from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData, Options
+from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData, Options, numpy_version
 from .fitters import (
     tomography_fitter_data,
     postprocess_fitter,
@@ -326,13 +326,14 @@ def _fidelity_result(
         prob_data = outcome_data / shot_data[None, :, None]
         bs_fidelities = []
         for _ in range(self.options.target_bootstrap_samples):
-            # Once python 3.7 support is dropped and minimum NumPy
-            # version can be set to 1.22 this can be replaced with
-            # `sampled_data = rng.multinomial(shot_data, probs)`
-            sampled_data = np.zeros_like(outcome_data)
-            for i in range(prob_data.shape[0]):
-                for j in range(prob_data.shape[1]):
-                    sampled_data[i, j] = rng.multinomial(shot_data[j], prob_data[i, j])
+            # TODO: remove conditional once numpy is pinned at 1.22 and above
+            if numpy_version() >= (1, 22):
+                sampled_data = rng.multinomial(shot_data, prob_data)
+            else:
+                sampled_data = np.zeros_like(outcome_data)
+                for i in range(prob_data.shape[0]):
+                    for j in range(prob_data.shape[1]):
+                        sampled_data[i, j] = rng.multinomial(shot_data[j], prob_data[i, j])
 
             try:
                 state_results = self._fit_state_results(

releasenotes/notes/drop_python_3_7_support-0529a7122e94b004.yaml

@@ -0,0 +1,4 @@
+---
+upgrade:
+  - |
+    Dropped support for Python 3.7.

requirements-dev.txt

@@ -16,12 +16,9 @@ qiskit-aer>=0.11.0
 pandas>=1.1.5
 cvxpy>=1.1.15
 pylatexenc
-# Pin `importlib-metadata` because of a bug relating to version 5.0.0. See #931 for more.
-importlib-metadata==4.13.0;python_version<'3.8'
 scikit-learn
 sphinx-copybutton
 # Pin versions below because of build errors
 ipykernel<=6.21.3
 jupyter-client<=8.0.3
-# Pin symengine because there are no wheels for 0.10 for Python 3.7
-symengine<=0.9.2;python_version<'3.8'
+

setup.py

@@ -50,7 +50,6 @@
         "Operating System :: MacOS",
         "Operating System :: POSIX :: Linux",
         "Programming Language :: Python :: 3 :: Only",
-        "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
@@ -61,7 +60,7 @@
     packages=find_packages(exclude=["test*"]),
     install_requires=REQUIREMENTS,
     include_package_data=True,
-    python_requires=">=3.7",
+    python_requires=">=3.8",
     project_urls={
         "Bug Tracker": "https://github.com/Qiskit/qiskit-experiments/issues",
         "Documentation": "https://qiskit.org/documentation/",

test/visualization/test_utils.py

@@ -22,6 +22,7 @@
 from qiskit.exceptions import QiskitError
 
 from qiskit_experiments.visualization.utils import DataExtentCalculator
+from qiskit_experiments.framework.package_deps import numpy_version
 
 
 @ddt
@@ -42,22 +43,34 @@ def _dummy_data(
         ]
 
         # Iterate over pairs of adjacent bin edges, which define the maximum and minimum for the region.
-        # This is done by zipping over shifted subarrays of bin_edges as follows:
+        # This is done by generating sliding windows of bin_edges as follows:
         #      [[a], [b], [c], [d], [e], [f]], g]
         #  [a, [[b], [c], [d], [e], [f], [g]]
         # The result is a list of pairs representing a moving window of size 2.
-        # TODO: Replace this with numpy.[...].sliding_window_view when Qiskit requires numpy>=0.20.0
+        # TODO: remove the old code once numpy is above 1.20.
         dummy_data = []
-        for (x_min, x_max), (y_min, y_max) in it.product(
-            *tuple(list(zip(b[0:-1], b[1:])) for b in bin_edges)
-        ):
-            _dummy_data = np.asarray(
-                [
-                    np.linspace(x_min, x_max, n_points),
-                    np.linspace(y_min, y_max, n_points),
-                ]
-            )
-            dummy_data.append(_dummy_data.swapaxes(-1, -2))
+        if numpy_version() >= (1, 20):
+            for (x_min, x_max), (y_min, y_max) in it.product(
+                *np.lib.stride_tricks.sliding_window_view(bin_edges, 2, 1)
+            ):
+                _dummy_data = np.asarray(
+                    [
+                        np.linspace(x_min, x_max, n_points),
+                        np.linspace(y_min, y_max, n_points),
+                    ]
+                )
+                dummy_data.append(_dummy_data.swapaxes(-1, -2))
+        else:
+            for (x_min, x_max), (y_min, y_max) in it.product(
+                *tuple(list(zip(b[0:-1], b[1:])) for b in bin_edges)
+            ):
+                _dummy_data = np.asarray(
+                    [
+                        np.linspace(x_min, x_max, n_points),
+                        np.linspace(y_min, y_max, n_points),
+                    ]
+                )
+                dummy_data.append(_dummy_data.swapaxes(-1, -2))
         return dummy_data
 
     @data(*list(it.product([1.0, 1.1, 2.0], [None, 1.0, np.sqrt(2)])))

tox.ini

@@ -1,6 +1,6 @@
 [tox]
 minversion = 3.3.0
-envlist = py311,py310,py39,py38,py37,lint
+envlist = py311,py310,py39,py38,lint
 isolated_build = true
 
 [testenv]