Update dependencies

.github/workflows/build_package.yml

@@ -13,7 +13,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: [3.9]
+        python-version: ['3.10']
     env:
       OS: ${{ matrix.os }}
       PYTHON: ${{ matrix.python-version }}

.github/workflows/coverage.yml

@@ -13,10 +13,10 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.9]
+        python-version: ['3.10']
     env:
       OS: 'ubuntu-latest'
-      PYTHON: '3.9'
+      PYTHON: '3.10'
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}

.github/workflows/format_lint.yml

@@ -13,10 +13,10 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.9]
+        python-version: ['3.10']
     env:
       OS: 'ubuntu-latest'
-      PYTHON: '3.9'
+      PYTHON: '3.10'
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}

.github/workflows/nightly_pypi.yml

@@ -11,10 +11,10 @@ jobs:
     - uses: actions/checkout@master
       with:
         ref: 'dev'
-    - name: Set up Python 3.9
+    - name: Set up Python 3.10
       uses: actions/setup-python@v1
       with:
-        python-version: 3.9
+        python-version: 3.10
     - name : Install Prerequisites
       run : |
         sudo apt-add-repository universe
@@ -49,7 +49,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: [3.9]
+        python-version: ['3.10']
     env:
       OS: ${{ matrix.os }}
       PYTHON: ${{ matrix.python-version }}

.github/workflows/notebook_test.yml

@@ -13,7 +13,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python-version: [3.9]
+        python-version: ['3.10']
     env:
       OS: ${{ matrix.os }}
       PYTHON: ${{ matrix.python-version }}

.github/workflows/publish_pypi.yml

@@ -10,10 +10,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@master
-    - name: Set up Python 3.9
+    - name: Set up Python '3.10'
       uses: actions/setup-python@v1
       with:
-        python-version: 3.9
+        python-version: '3.10'
     - name : Install Prerequisites
       run : |
         sudo apt-add-repository universe
@@ -45,7 +45,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: [3.9]
+        python-version: ['3.10']
     env:
       OS: ${{ matrix.os }}
       PYTHON: ${{ matrix.python-version }}

c3/c3objs.py

@@ -37,10 +37,10 @@ def __init__(self, name, desc="", comment="", params=None):
         if params:
             for pname, par in params.items():
                 # TODO params here should be the dict representation only
-                if isinstance(par, Quantity):
-                    self.params[pname] = par
-                else:
+                if isinstance(par, dict):
                     self.params[pname] = Quantity(**par)
+                else:
+                    self.params[pname] = par
 
     def __str__(self) -> str:
         return hjson.dumps(self.asdict(), default=hjson_encode)
@@ -276,10 +276,7 @@ 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
-        if isinstance(val, ops.EagerTensor) or isinstance(val, ops.Tensor):
-            val = tf.cast(val, tf.float64)
-        else:
-            val = tf.constant(val, tf.float64)
+        val = tf.constant(val, tf.float64)
 
         tmp = (
             2 * (tf.reshape(val, self.shape) * self.pref - self.offset) / self.scale - 1

c3/generator/devices.py

@@ -1020,7 +1020,7 @@ def __init__(self, **props):
 
     def get_noise(self, sig):
         noise_amp = self.params["noise_amp"].get_value().numpy()
-        bfl_num = np.int(self.params["bfl_num"].get_value().numpy())
+        bfl_num = int(self.params["bfl_num"].get_value().numpy())
         noise = []
         bfls = 2 * np.random.randint(2, size=bfl_num) - 1
         num_steps = len(sig)

c3/libraries/algorithms.py

@@ -284,9 +284,9 @@ def tf_fun():
 
     opt_sgd = tf.keras.optimizers.SGD(learning_rate=learning_rate, momentum=momentum)
 
-    for _ in range(iters):
-        step_count = opt_sgd.minimize(tf_fun, [var])
-        print(f"epoch {step_count.numpy()}: func_value: {tf_fun()}")
+    for ii in range(iters):
+        opt_sgd.minimize(tf_fun, [var])
+        print(f"iter {ii}: func_value: {tf_fun()}")
 
     result = OptimizeResult(x=var.numpy(), success=True)
     return result
@@ -524,7 +524,6 @@ def cmaes(x_init, fun=None, fun_grad=None, grad_lookup=None, options={}):
     es = cma.CMAEvolutionStrategy(x_init, spread, settings)
     iter = 0
     while not es.stop():
-
         if shrunk_check:
             sigmas.append(es.sigma)
             if iter > sigma_conv:
@@ -627,7 +626,6 @@ def gcmaes(x_init, fun=None, fun_grad=None, grad_lookup=None, options={}):
     es = cma.CMAEvolutionStrategy(x_init, spread, settings)
     iter = 0
     while not es.stop():
-
         if shrinked_check:
             sigmas.append(es.sigma)
             if iter > sigma_conv:

c3/libraries/propagation.py

@@ -688,7 +688,6 @@ def tf_expm_dynamic(A, acc=1e-5):
 def ode_solver(
     model: Model, gen: Generator, instr: Instruction, init_state, solver, step_function
 ) -> Dict:
-
     signal = gen.generate_signals(instr)
 
     if model.lindbladian:
@@ -726,7 +725,6 @@ def ode_solver(
 def ode_solver_final_state(
     model: Model, gen: Generator, instr: Instruction, init_state, solver, step_function
 ) -> Dict:
-
     signal = gen.generate_signals(instr)
 
     if model.lindbladian:

c3/main.py

@@ -21,6 +21,7 @@
 
 logging.getLogger("tensorflow").disabled = True
 
+
 # flake8: noqa: C901
 def run_cfg(cfg, opt_config_filename, debug=False):
     """Execute an optimization problem described in the cfg file.

c3/optimizers/modellearning.py

@@ -86,7 +86,7 @@ def __init__(
 
         super().__init__(pmap=pmap, algorithm=algorithm, logger=logger)
 
-        self.state_labels = {"all": None}
+        self.state_labels: Dict[str, List] = {"all": []}
         for target, labels in state_labels.items():
             self.state_labels[target] = [tuple(lab) for lab in labels]
 
@@ -106,7 +106,7 @@ def __init__(
         self.inverse = False
         self.options = options
 
-        self.learn_data = {}
+        self.learn_data: Dict[str, np.ndarray] = {}
         self.read_data(datafiles)
         self.sampling = sampling
         self.batch_sizes = batch_sizes
@@ -298,13 +298,11 @@ def goal_run(self, current_params: tf.constant) -> tf.float64:
         # TODO: seq per point is not constant. Remove.
 
         for target, data in self.learn_data.items():
-
             self.learn_from = data["seqs_grouped_by_param_set"]
             self.gateset_opt_map = data["opt_map"]
             indeces = self.select_from_data(self.batch_sizes[target])
 
             for ipar in indeces:
-
                 count += 1
                 data_set = self.learn_from[ipar]
                 m_vals = data_set["results"][:seqs_pp]
@@ -376,12 +374,10 @@ def goal_run_with_grad(self, current_params):
         count = 0
 
         for target, data in self.learn_data.items():
-
             self.learn_from = data["seqs_grouped_by_param_set"]
             self.gateset_opt_map = data["opt_map"]
             indeces = self.select_from_data(self.batch_sizes[target])
             for ipar in indeces:
-
                 count += 1
                 data_set = self.learn_from[ipar]
 

c3/optimizers/sensitivity.py

@@ -64,7 +64,6 @@ def __init__(
         run_name: str = None,
         options={},
     ) -> None:
-
         super().__init__(
             sampling,
             batch_sizes,

c3/qiskit/c3_backend.py

@@ -215,7 +215,7 @@ def generate_shot_readout(self):
         # TODO a sophisticated readout/measurement routine (w/ SPAM)
         return (np.round(self.pops_array * self._shots)).astype("int32").tolist()
 
-    def run(self, qobj: qobj.Qobj, **backend_options) -> C3Job:
+    def run(self, qobj: qobj.QasmQobj, **backend_options) -> C3Job:
         """Parse and run a Qobj
 
         Parameters

c3/utils/tf_utils.py

@@ -235,6 +235,7 @@ def tf_diff(l):  # noqa
 
 # MATRIX FUNCTIONS
 
+
 # TODO - change A.shape[: length-2] to tf.shape
 def Id_like(A):
     """Identity of the same size as A."""

requirements.txt

@@ -8,11 +8,12 @@ pytest-cov>=2.11.1
 pytest-xdist>=2.2.1
 python-dateutil>=2.8.1
 qiskit>=0.25.0
+qiskit-aer>=0.13.2
 rich>=9.2.0
 scipy>=1.5.2
 six>=1.15.0
 Sphinx>=3.2.1
 sphinx-autoapi>=1.4.0
-tensorflow>=2.4.1,<=2.14.1
+tensorflow>=2.15.0
 tensorflow-estimator>=2.4.0
 tensorflow-probability>=0.12.1

setup.py

@@ -25,7 +25,7 @@
         "Operating System :: MacOS",
         "Operating System :: Microsoft :: Windows :: Windows 10",
         "Operating System :: Unix",
-        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Topic :: Scientific/Engineering :: Physics",
     ],
@@ -39,9 +39,9 @@
         "rich>=9.2.0",
         "numpy>=1.23.1",
         "scipy>=1.5.2",
-        "tensorflow>=2.4.1,<=2.14.1",
+        "tensorflow>=2.15.0",
         "tensorflow-estimator>=2.4.0",
         "tensorflow-probability>=0.12.1",
     ],
-    python_requires="~=3.9",
+    python_requires="~=3.10",
 )

test/test_instruction.py

@@ -189,16 +189,13 @@ def test_set_name_ideal():
     assert (instr.ideal == GATES["ry90p"]).all()
     instr.set_name("crzp")
     assert (instr.ideal == GATES["crzp"]).all()
-    instr.name = 'ry90p'
+    instr.name = "ry90p"
     assert (instr.ideal == GATES["ry90p"]).all()
 
 
 @pytest.mark.unit
 def test_correct_ideal_assignment() -> None:
-    custom_gate = np.array([[1, 0, 0, 0],
-                            [0, 1, 0, 0],
-                            [0, 0, 0, 1],
-                            [0, 0, 1, 0]], dtype=np.complex)
+    custom_gate = np.array([[1.0, 0j, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]])
     propagators = {"custom": custom_gate}
     instructions = {"custom": Instruction("custom", ideal=custom_gate)}
     psi_0 = np.array([[1], [0], [0], [0]])
@@ -216,7 +213,7 @@ def test_correct_ideal_assignment() -> None:
 @pytest.mark.unit
 def test_correct_bloch_rotation_direction():
     # makes sure that the rotations on the bloch sphere are in the right direction
-    GATE_NAME = 'ry90p[0]'
+    GATE_NAME = "ry90p[0]"
     exp = create_experiment()
     exp.compute_propagators()
     # TODO - remove this line after the ideal updating bug gets fixed...
@@ -225,4 +222,6 @@ def test_correct_bloch_rotation_direction():
     ideal_gate = exp.pmap.instructions[GATE_NAME].get_ideal_gate(dims=[3])
     propagator = exp.propagators[GATE_NAME].numpy()
     # not equal to one because of imperfections in the propagation
-    np.testing.assert_array_less(unitary_infid(ideal_gate, propagator, dims=[3]).numpy()[0], 0.05)
+    np.testing.assert_array_less(
+        unitary_infid(ideal_gate, propagator, dims=[3]).numpy()[0], 0.05
+    )

test/test_parameter_map.py

@@ -256,7 +256,7 @@ def test_parameter_extend() -> None:
     Test the setting in optimizer format. Parameter is out of bounds for the
     original pmap and should be extended.
     """
-    pmap.load_values("test/sample_optim_log.c3log",extend_bounds=True)
+    pmap.load_values("test/sample_optim_log.c3log", extend_bounds=True)
     np.testing.assert_almost_equal(
         pmap.get_parameter(("rx90p[0]", "d1", "gauss", "freq_offset")).numpy(),
         -82997604.24565414,

test/test_qt_utils.py

@@ -38,7 +38,7 @@ def test_basis(get_test_dimensions) -> None:
     """Testing orthonormality of basis vectors."""
     for dim in get_test_dimensions:
         pairs = [(i, j) for i in range(dim) for j in range(dim)]
-        for (i, j) in pairs:
+        for i, j in pairs:
             vi = basis(dim, i)
             vj = basis(dim, j)
             almost_equal(vi.T @ vj, 1 if i == j else 0)
@@ -60,7 +60,7 @@ def test_xy_basis(get_test_dimensions) -> None:
 
         # overlap
         pairs = [(a, b) for a in names for b in names if b is not a]
-        for (a, b) in pairs:
+        for a, b in pairs:
             va_p = xy_basis(dim, a + "p")
             va_m = xy_basis(dim, a + "m")
             vb_p = xy_basis(dim, b + "p")
@@ -79,7 +79,7 @@ def test_basis_matrices(get_test_dimensions) -> None:
 
         # orthogonality
         pairs = [(a, b) for a in matrices for b in matrices if b is not a]
-        for (a, b) in pairs:
+        for a, b in pairs:
             almost_equal(np.linalg.norm(np.multiply(a, b)), 0)
 
         # normalisation