Comprehensive tests, version bump, bug fixes, and repo consistency (#45)

* feat: upgrade to qiskit 2.3.0 - update imports, fix bugs, add tests

Co-authored-by: fgfuchs <2428162+fgfuchs@users.noreply.github.com>

* feat: comprehensive tests, version bump, bug fixes, and repo consistency improvements

Co-authored-by: fgfuchs <2428162+fgfuchs@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: fgfuchs <2428162+fgfuchs@users.noreply.github.com>

Author: Copilot

README.md

@@ -40,7 +40,7 @@ This library already contains several standard implementations.
 - The following [problem](qaoa/problems/base_problem.py) cases are already available:
 	- [Max k-CUT binary power of two](qaoa/problems/maxkcut_binary_powertwo.py) *
 	- [Max k-CUT binary full H](qaoa/problems/maxkcut_binary_fullH.py)
-	- [Max k-CUT binary one hot](qaoa/problems/maxkcut_binary_one_hot.py)
+	- [Max k-CUT binary one hot](qaoa/problems/maxkcut_one_hot_problem.py)
 	- [QUBO](qaoa/problems/qubo_problem.py)
 	- [Exact cover](qaoa/problems/exactcover_problem.py)
 	- [Portfolio](qaoa/problems/portfolio_problem.py)
@@ -66,12 +66,12 @@ It is **very easy to extend this list** by providing  an implementation of a cir
 To make an ansatz for the MaxCut problem, the X-mixer and the initial state $|+\rangle^{\otimes n}$  one can create an instance like this: 
 
 	qaoa = QAOA(
-		initialstate=initialstates.Plus(),
 		problem=problems.MaxKCutBinaryPowerOfTwo(G="some networkx instance", k_cuts=2),
-		mixer=mixers.X()
+		mixer=mixers.X(),
+		initialstate=initialstates.Plus()
 	)
 
-*(can be used for the standard MaxCut with argument k_cuts=2)
+*(can be used for the standard MaxCut with argument k_cuts=2, or use `problems.MaxCut(G)` for convenience)*
 ***
 ### Run optimization at depth $p$
 
@@ -106,7 +106,7 @@ QAOA supports the following keywords:
 		cvar=
 	)
 
-- `backend`: the backend to be used, defaults to `Aer.get_backend("qasm_simulator")`
+- `backend`: the backend to be used, defaults to `AerSimulator()` (from `qiskit_aer`)
 - `noisemodel`: the noise model to be used, default to `None`,
 - `optimizer`: a list of the optimizer to be used from qiskit-algorithms together with options, defaults to `[COBYLA, {}]`,
 - `precision`: sampel until a certain precision of the expectation value is reached based on $\text{error}=\frac{\text{variance}}{\sqrt{\text{shots}}}$, defaults to `None`,
@@ -136,9 +136,9 @@ Multi-angle QAOA allows components to use multiple parameters per layer, increas
 - **Parameterized initial state** (`PlusParameterized`): The initial state |+⟩ with optimizable per-qubit phase rotations
 
 	qaoa = QAOA(
-		initialstate=initialstates.Plus(),
 		problem=problems.MaxKCutBinaryPowerOfTwo(G="some networkx instance", k_cuts=2),
-		mixer=mixers.XMultiAngle()  # N_qubits beta parameters per layer
+		mixer=mixers.XMultiAngle(),  # N_qubits beta parameters per layer
+		initialstate=initialstates.Plus()
 	)
 
 The flat angle array format used by `hist()`, `getParametersToBind()`, and `interp()` is:

qaoa/mixers/grover_mixer.py

@@ -32,7 +32,7 @@ def __init__(self, subcircuit: InitialState) -> None:
         self.mixer_param = Parameter("x_beta")
 
     def create_circuit(self):
-        """
+        r"""
         Constructs the Grover mixer circuit using the subcircuit.
 
         Given feasible states f \in F,

qaoa/mixers/maxkcut_grover_mixer.py

@@ -130,6 +130,7 @@ def create_circuit(self) -> None:
             tensor_feas = Tensor(circ_one_node, self.num_V)
 
             gm = Grover(tensor_feas)
+            gm.setNumQubits(tensor_feas.N_qubits)
 
             gm.create_circuit()
             self.circuit = gm.circuit

qaoa/mixers/xy_mixer.py

@@ -56,7 +56,7 @@ def create_circuit(self):
 
     @staticmethod
     def generate_pairs(n, case="ring"):
-        """_summary_
+        """Generates pairs of adjacent qubit indices for the given topology.
 
         Args:
             n (int): The number of qubits.

qaoa/problems/portfolio_problem.py

@@ -75,9 +75,9 @@ def cost_nonQUBO(self, string, penalize=True):
         # penalty = self.params.get("penalty", 0.0)
 
         x = np.array(list(map(int, string)))
-        cost = risk * (x.T @ cov_matrix @ x) - exp_return.T @ x
+        cost = self.risk * (x.T @ self.cov_matrix @ x) - self.exp_return.T @ x
         if penalize:
-            cost += penalty * (x.sum() - budget) ** 2
+            cost += self.penalty * (x.sum() - self.budget) ** 2
 
         return -cost
 

qaoa/problems/qubo_problem.py

@@ -30,13 +30,11 @@ class QUBO(Problem):
         createParameterizedCostCircuitTril(): Creates a parameterized circuit of the triangularized QUBO problem.
     """
     def __init__(self, Q=None, c=None, b=None) -> None:
-        super().__init__()
         """
         Implements the mapping from the parameters in params to the QUBO problem.
         Is expected to be called by the child class.
 
-        # The QUBO will be on this form:
-        # min x^T Q x + c^T x + b
+        The QUBO will be on this form: min x^T Q x + c^T x + b
 
         Args:
             Q (np.ndarray): A 2-dimensional numpy ndarray representing the quadratic coefficients.
@@ -48,6 +46,7 @@ def __init__(self, Q=None, c=None, b=None) -> None:
             AssertionError: If c is not a 1D numpy ndarray of compatible size.
             AssertionError: If b is not a scalar.
         """
+        super().__init__()
         assert type(Q) is np.ndarray, "Q needs to be a numpy ndarray, but is " + str(
             type(Q)
         )

qaoa/qaoa.py

@@ -13,12 +13,12 @@
 )
 from qiskit.circuit import Parameter
 try:
-    from qiskit.primitives import Sampler
+    from qiskit_aer.primitives import SamplerV2 as _SamplerV2
 except ImportError:
-    from qiskit_aer.primitives import Sampler
+    from qiskit.primitives import StatevectorSampler as _SamplerV2
 from qiskit_algorithms.optimizers import COBYLA
 
-from qiskit_aer import Aer
+from qiskit_aer import AerSimulator, Aer
 
 from qaoa.initialstates import InitialState
 from qaoa.mixers import Mixer
@@ -198,7 +198,7 @@ def __init__(
         problem,
         mixer,
         initialstate,
-        backend=Aer.get_backend("qasm_simulator"),
+        backend=AerSimulator(),
         noisemodel=None,
         optimizer=[COBYLA, {}],  # optimizer, options
         precision=None,
@@ -869,7 +869,7 @@ def local_opt(self, angles0):
         except TypeError as e:  ### QNSPSA needs fidelity
             self.isQNSPSA = True
             self.optimizer[1]["fidelity"] = self.optimizer[0].get_fidelity(
-                self.parameterized_circuit, sampler=Sampler()
+                self.parameterized_circuit, sampler=_SamplerV2()
             )
             opt = self.optimizer[0](**self.optimizer[1])
         res = opt.minimize(self.loss, x0=angles0)

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name="qaoa",
-    version="1.2.1",
+    version="1.2.2",
     license="GNU General Public License v3.0",
     author="Franz Georg Fuchs",
     author_email="franzgeorgfuchs@gmail.com",
@@ -11,17 +11,25 @@
     long_description_content_type="text/markdown",
     packages=find_packages(exclude=["examples", "images"]),
     keywords="quantum computing, qaoa, qiskit",
+    python_requires=">=3.9",
     install_requires=[
         "numpy",
         "scipy",
         "structlog",
         "matplotlib",
         "networkx",
         "jupyter",
-        "qiskit==1.1.1",
-        "qiskit-aer",
-        "qiskit-algorithms",
+        "qiskit>=2.3.0",
+        "qiskit-aer>=0.17.0",
+        "qiskit-algorithms>=0.4.0",
         "qiskit-finance",
         "pylatexenc",
     ],
+    extras_require={
+        "dev": [
+            "twine>=6.0.0",
+            "build>=1.0.0",
+            "pytest>=7.0.0",
+        ],
+    },
 )