Merge pull request #163 from q-optimize/release/1.4

Release/1.4

Maintenance updates to code quality and implementation along with some useful utility functions and example notebooks.

.github/workflows/build_package.yml

@@ -1,4 +1,4 @@
-name: Python Package Build
+name: Build and Test
 
 on:
   push:
@@ -8,12 +8,12 @@ on:
   workflow_dispatch:
 
 jobs:
-  build:
+  build-and-test:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
         os: [ubuntu-20.04, ubuntu-18.04, macos-latest, windows-latest]
-        python-version: [3.6, 3.7, 3.8, 3.9]
+        python-version: [3.7, 3.8, 3.9]
     env:
       OS: ${{ matrix.os }}
       PYTHON: ${{ matrix.python-version }}

.github/workflows/nightly_pypi.yml

@@ -49,7 +49,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-20.04, ubuntu-18.04, macos-latest, windows-latest]
-        python-version: [3.6, 3.7, 3.8, 3.9]
+        python-version: [3.7, 3.8, 3.9]
     env:
       OS: ${{ matrix.os }}
       PYTHON: ${{ matrix.python-version }}

.github/workflows/publish_pypi.yml

@@ -45,7 +45,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-20.04, ubuntu-18.04, macos-latest, windows-latest]
-        python-version: [3.6, 3.7, 3.8, 3.9]
+        python-version: [3.7, 3.8, 3.9]
     env:
       OS: ${{ matrix.os }}
       PYTHON: ${{ matrix.python-version }}

CHANGELOG.md

@@ -9,6 +9,25 @@ This Changelog tracks all past changes to this project as well as details about
 - `fixed` for any bug fixes.
 - `security` in case of vulnerabilities.
 
+## Version `1.4` - 23 Dec 2021
+
+### Summary
+
+Maintenance updates to code quality and implementation along with some useful utility functions and example notebooks.
+
+### Details
+
+- `added` experimental support for Runge Kutta 4 solving of EOM #144
+- `changed` the experiment and model classes for handling different propagation methods #144
+- `changed` The generator now can handle any list of devices that forms a directed graph #129
+- `removed` official support for Python 3.6 #156
+- `added` a method to HJSON dump current parameter values #149
+- `added` example for the log reader CLI #137
+- `added` human readable saving of current best point for the optimizer #140 
+- `fixed` handling of anharmonicity in transmons with two levels #146
+- `added` an example notebook with entangling two-qubit gates #154
+- `fixed` Broken State Fidelity #135
+
 ## Version `1.3` - 20 Jul 2021
 
 ### Summary
@@ -117,4 +136,4 @@ This is the first major and properly packaged release of the `c3-toolset` librar
 - `added`, `fixed` Compatibility with Windows and MacOS
 - `added` Better Examples and Docs
 - `added` pip installation configs
-- `added` pip-test deployment github actions
+- `added` pip-test deployment github actions

CONTRIBUTING.md

@@ -261,7 +261,7 @@ We use the [CHANGELOG.md](CHANGELOG.md) as a living document that not only recor
 
 ## Continuous Integration Checks
 
-As previously discussed, tests are an inherent part of good code. All Pull Requests made to the repository are automatically tested on Windows, MacOS and Ubuntu with Python 3.6, 3.7, 3.8 and 3.9, which come up in the PR as `Checks`, notifying you whenever a particular check is failing on any platform. Except the `Code Complexity`, all checks should be passing for your PR to be ready for review/merge. The `Codecov` bot will also comment on the PR with a report on the change in the test coverage for that particular PR. If your PR reduces the overall test coverage of the codebase, it is not yet ready and you need to add more tests.
+As previously discussed, tests are an inherent part of good code. All Pull Requests made to the repository are automatically tested on Windows, MacOS and Ubuntu with Python 3.7, 3.8 and 3.9, which come up in the PR as `Checks`, notifying you whenever a particular check is failing on any platform. Except the `Code Complexity`, all checks should be passing for your PR to be ready for review/merge. The `Codecov` bot will also comment on the PR with a report on the change in the test coverage for that particular PR. If your PR reduces the overall test coverage of the codebase, it is not yet ready and you need to add more tests.
 
 ## Contributor License Agreement
 

README.md

@@ -3,6 +3,7 @@
 [![codecov](https://codecov.io/gh/q-optimize/c3/branch/dev/graph/badge.svg)](https://codecov.io/gh/q-optimize/c3)
 [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/q-optimize/c3.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/q-optimize/c3/context:python)
 <a href="https://codeclimate.com/github/q-optimize/c3/maintainability"><img src="https://api.codeclimate.com/v1/badges/a090831b106f863dc223/maintainability" /></a>
+[![Build and Test](https://github.com/q-optimize/c3/actions/workflows/build_package.yml/badge.svg)](https://github.com/q-optimize/c3/actions/workflows/build_package.yml)
 [![Documentation Status](https://readthedocs.org/projects/c3-toolset/badge/?version=latest)](https://c3-toolset.readthedocs.io/en/latest/?badge=latest)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![PyPI version fury.io](https://badge.fury.io/py/c3-toolset.svg)](https://pypi.python.org/pypi/c3-toolset/)
@@ -38,3 +39,20 @@ Examples are available in the `examples/` directory and can also be run online u
 If you wish to contribute, please check out the issues tab and also the `CONTRIBUTING.md` for useful resources.
 
 The source code is available on Github at [https://github.com/q-optimize/c3](https://github.com/q-optimize/c3).
+
+## Citation
+
+If you use `c3-toolset` in your research, please cite it as below:
+
+```
+@article{Wittler2021,
+   title={Integrated Tool Set for Control, Calibration, and Characterization of Quantum Devices Applied to Superconducting Qubits},
+   volume={15},
+   DOI={10.1103/physrevapplied.15.034080},
+   number={3},
+   journal={Physical Review Applied},
+   author={Wittler, Nicolas and Roy, Federico and Pack, Kevin and Werninghaus, Max and Saha Roy, Anurag and Egger, Daniel J. and Filipp, Stefan and Wilhelm, Frank K. and Machnes, Shai},
+   year={2021},
+   month={Mar}
+}
+```

c3/__init__.py

@@ -1 +1 @@
-__version__ = "1.3"
+__version__ = "1.4"

c3/c3objs.py

@@ -123,57 +123,57 @@ def asdict(self) -> dict:
 
     def __add__(self, other):
         out_val = copy.deepcopy(self)
-        out_val.set_value(self.get_value() + other, extend_bounds=True)
+        out_val._set_value_extend(self.get_value() + other)
         return out_val
 
     def __radd__(self, other):
         out_val = copy.deepcopy(self)
-        out_val.set_value(self.get_value() + other, extend_bounds=True)
+        out_val._set_value_extend(self.get_value() + other)
         return out_val
 
     def __sub__(self, other):
         out_val = copy.deepcopy(self)
-        out_val.set_value(self.get_value() - other, extend_bounds=True)
+        out_val._set_value_extend(self.get_value() - other)
         return out_val
 
     def __rsub__(self, other):
         out_val = copy.deepcopy(self)
-        out_val.set_value(other - self.get_value(), extend_bounds=True)
+        out_val._set_value_extend(other - self.get_value())
         return out_val
 
     def __mul__(self, other):
         out_val = copy.deepcopy(self)
-        out_val.set_value(self.get_value() * other, extend_bounds=True)
+        out_val._set_value_extend(self.get_value() * other)
         return out_val
 
     def __rmul__(self, other):
         out_val = copy.deepcopy(self)
-        out_val.set_value(self.get_value() * other, extend_bounds=True)
+        out_val._set_value_extend(self.get_value() * other)
         return out_val
 
     def __pow__(self, other):
         out_val = copy.deepcopy(self)
-        out_val.set_value(self.get_value() ** other, extend_bounds=True)
+        out_val._set_value_extend(self.get_value() ** other)
         return out_val
 
     def __rpow__(self, other):
         out_val = copy.deepcopy(self)
-        out_val.set_value(other ** self.get_value(), extend_bounds=True)
+        out_val._set_value_extend(other ** self.get_value())
         return out_val
 
     def __truediv__(self, other):
         out_val = copy.deepcopy(self)
-        out_val.set_value(self.get_value() / other, extend_bounds=True)
+        out_val._set_value_extend(self.get_value() / other)
         return out_val
 
     def __rtruediv__(self, other):
         out_val = copy.deepcopy(self)
-        out_val.set_value(other / self.get_value(), extend_bounds=True)
+        out_val._set_value_extend(other / self.get_value())
         return out_val
 
     def __mod__(self, other):
         out_val = copy.deepcopy(self)
-        out_val.set_value(self.get_value() % other, extend_bounds=True)
+        out_val._set_value_extend(self.get_value() % other)
         return out_val
 
     def __lt__(self, other):
@@ -253,7 +253,13 @@ def get_value(self, val: tf.float64 = None, dtype: tf.dtypes = None) -> tf.Tenso
         value = self.scale * (val + 1) / 2 + self.offset
         return tf.cast(value, dtype)
 
-    def set_value(self, val, extend_bounds=False) -> None:
+    def set_value(self, val, extend_bounds=False):
+        if extend_bounds:
+            self._set_value_extend(val)
+        else:
+            self._set_value(val)
+
+    def _set_value(self, val) -> None:
         """Set the value of this quantity as tensorflow. Value needs to be
         within specified min and max."""
         # setting can be numpyish
@@ -264,25 +270,25 @@ def set_value(self, val, extend_bounds=False) -> None:
 
         tmp = 2 * (val * self.pref - self.offset) / self.scale - 1
 
-        if extend_bounds and tf.math.abs(tmp) > 1:
-            min_val, max_val = self.get_limits()
-            # Extra bounds included to not be directly at border due to differentiability
-            minmax = [val * 0.9, val * 1.1, min_val, max_val]
-            min_val = tf.math.reduce_min(minmax)
-            max_val = tf.math.reduce_max(minmax)
-            self.set_limits(min_val, max_val)
-            tmp = 2 * (val * self.pref - self.offset) / self.scale - 1
-
-        tf.debugging.assert_less_equal(
-            tf.math.abs(tmp),
-            tf.constant(1.0, tf.float64),
-            f"Value {val.numpy()}{self.unit} out of bounds for quantity with "
-            f"min_val: {num3str(self.get_limits()[0])}{self.unit} and "
-            f"max_val: {num3str(self.get_limits()[1])}{self.unit}",
-        )
+        if np.any(tf.math.abs(tmp) > tf.constant(1.0, tf.float64)):
+            raise Exception(
+                f"Value {val.numpy()}{self.unit} out of bounds for quantity with "
+                f"min_val: {num3str(self.get_limits()[0])}{self.unit} and "
+                f"max_val: {num3str(self.get_limits()[1])}{self.unit}",
+            )
 
         self.value = tf.cast(tmp, tf.float64)
 
+    def _set_value_extend(self, val) -> None:
+        """Set the value of this quantity as tensorflow. If needed, limits will be extended."""
+        min_val, max_val = self.get_limits()
+        # Extra bounds included to not be directly at border due to differentiability
+        minmax = [val * 0.9, val * 1.1, min_val, max_val]
+        min_val = tf.math.reduce_min(minmax)
+        max_val = tf.math.reduce_max(minmax)
+        self.set_limits(min_val, max_val)
+        self._set_value(val)
+
     def get_opt_value(self) -> np.ndarray:
         """Get an optimizer friendly representation of the value."""
         return self.value.numpy().flatten()