🔥 removed all benchmarks relying on qiskit_optimization, qiskit_natur…

…e, and qiskit_algorithm dependencies

.pre-commit-config.yaml

@@ -86,9 +86,6 @@ repos:
         args: []
         additional_dependencies:
           - pytket_qiskit
-          - qiskit_optimization
-          - qiskit_nature
-          - qiskit_finance
           - importlib_resources
           - pytest
           - types-setuptools

pyproject.toml

@@ -26,9 +26,6 @@ dependencies = [
     # there is a bug in 1.2.0 that causes an error some benchmarks, see https://github.com/Qiskit/qiskit/issues/12969
     "qiskit!=1.2.0",
     "qiskit_qasm3_import>=0.5.0",
-    "qiskit_optimization>=0.6",
-    "qiskit_nature[pyscf]>=0.7",
-    "qiskit_finance>=0.4.1",
     "networkx>=2.8.8",
     "joblib>=1.3.0",
     "numpy>=1.26; python_version >= '3.12'",

src/mqt/bench/benchmark_generator.py

@@ -128,15 +128,6 @@ def define_benchmark_instances(self, benchmark: Benchmark) -> None:
             elif benchmark["name"] == "shor":
                 instances = [lib.get_instance(choice) for choice in benchmark["instances"]]
 
-            elif benchmark["name"] in ("routing", "tsp"):
-                instances = range(benchmark["min_nodes"], benchmark["max_nodes"])
-
-            elif benchmark["name"] == "groundstate":
-                instances = benchmark["instances"]
-
-            elif benchmark["name"] in ("pricingcall", "pricingput"):
-                instances = range(benchmark["min_uncertainty"], benchmark["max_uncertainty"])
-
             else:
                 instances = range(
                     benchmark["min_qubits"],
@@ -344,7 +335,7 @@ def get_benchmark(
         benchmark_name: name of the to be generated benchmark
         level: Choice of level, either as a string ("alg", "indep", "nativegates" or "mapped") or as a number between 0-3 where 0 corresponds to "alg" level and 3 to "mapped" level
         circuit_size: Input for the benchmark creation, in most cases this is equal to the qubit number
-        benchmark_instance_name: Input selection for some benchmarks, namely "groundstate" and "shor"
+        benchmark_instance_name: Input selection for some benchmarks, namely and "shor"
         compiler: "qiskit" or "tket"
         compiler_settings: Data class containing the respective compiler settings for the specified compiler (e.g., optimization level for Qiskit)
         gateset: Name of the gateset or tuple containing the name of the gateset and the gateset itself (required for "nativegates" level)
@@ -362,11 +353,11 @@ def get_benchmark(
         msg = f"Selected level must be in {get_supported_levels()}."
         raise ValueError(msg)
 
-    if benchmark_name not in ["shor", "groundstate"] and not (isinstance(circuit_size, int) and circuit_size > 0):
+    if benchmark_name != "shor" and not (isinstance(circuit_size, int) and circuit_size > 0):
         msg = "circuit_size must be None or int for this benchmark."
         raise ValueError(msg)
 
-    if benchmark_name in ["shor", "groundstate"] and not isinstance(benchmark_instance_name, str):
+    if benchmark_name == "shor" and not isinstance(benchmark_instance_name, str):
         msg = "benchmark_instance_name must be defined for this benchmark."
         raise ValueError(msg)
 
@@ -386,9 +377,6 @@ def get_benchmark(
         to_be_factored_number, a_value = lib.get_instance(benchmark_instance_name)
         qc = lib.create_circuit(to_be_factored_number, a_value)
 
-    elif benchmark_name == "groundstate":
-        qc = lib.create_circuit(benchmark_instance_name)
-
     else:
         qc = lib.create_circuit(circuit_size)
 

src/mqt/bench/benchmarks/__init__.py

@@ -6,23 +6,8 @@
 
 from __future__ import annotations
 
-from mqt.bench.benchmarks.qiskit_application_finance import (
-    portfolioqaoa,
-    portfoliovqe,
-    pricingcall,
-    pricingput,
-)
 from mqt.bench.benchmarks.qiskit_application_ml import qnn
-from mqt.bench.benchmarks.qiskit_application_nature import groundstate
-from mqt.bench.benchmarks.qiskit_application_optimization import routing, tsp
 
 __all__ = [
-    "groundstate",
-    "portfolioqaoa",
-    "portfoliovqe",
-    "pricingcall",
-    "pricingput",
     "qnn",
-    "routing",
-    "tsp",
 ]

src/mqt/bench/benchmarks/qaoa.py

@@ -2,34 +2,49 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+import numpy as np
+from qiskit import QuantumCircuit
 
-from qiskit.primitives import Sampler
-from qiskit_algorithms.minimum_eigensolvers import QAOA
-from qiskit_algorithms.optimizers import SLSQP
-from qiskit_optimization import QuadraticProgram
 
-from mqt.bench.utils import get_examplary_max_cut_qp
-
-if TYPE_CHECKING:  # pragma: no cover
-    from qiskit import QuantumCircuit
-
-
-def create_circuit(num_qubits: int) -> QuantumCircuit:
-    """Returns a quantum circuit implementing the Quantum Approximation Optimization Algorithm for a specific max-cut example.
+def create_circuit(num_qubits: int, repetitions: int = 2, seed: int = 42) -> QuantumCircuit:
+    """Constructs a quantum circuit implementing QAOA for a Max-Cut example with random parameters.
 
     Arguments:
-        num_qubits: number of qubits of the returned quantum circuit
+        num_qubits: Number of qubits in the circuit (equal to the number of graph nodes).
+        repetitions: Number of QAOA layers (repetitions of the ansatz).
+        seed: Random seed for reproducibility.
 
     Returns:
-        QuantumCircuit: quantum circuit implementing the Quantum Approximation Optimization Algorithm
+        QuantumCircuit: Quantum circuit implementing QAOA.
     """
-    qp = get_examplary_max_cut_qp(num_qubits)
-    assert isinstance(qp, QuadraticProgram)
-
-    qaoa = QAOA(sampler=Sampler(), reps=2, optimizer=SLSQP(maxiter=25))
-    qaoa_result = qaoa.compute_minimum_eigenvalue(qp.to_ising()[0])
-    qc = qaoa.ansatz.assign_parameters(qaoa_result.optimal_point)
+    # Set the random number generator
+    rng = np.random.default_rng(seed)
+
+    # Example adjacency matrix for Max-Cut (toy problem)
+    adjacency_matrix = rng.integers(0, 2, size=(num_qubits, num_qubits))
+    adjacency_matrix = np.triu(adjacency_matrix, 1)  # Upper triangular part for undirected graph
+
+    # Random initialization of parameters
+    gamma_values = rng.uniform(0, np.pi, repetitions)
+    beta_values = rng.uniform(0, np.pi, repetitions)
+
+    # Initialize QAOA circuit
+    qc = QuantumCircuit(num_qubits)
+
+    # Start in uniform superposition
+    qc.h(range(num_qubits))
+
+    # Define cost and mixer operators for each layer
+    for layer in range(repetitions):
+        # Cost Hamiltonian
+        for i in range(num_qubits):
+            for j in range(i + 1, num_qubits):
+                if adjacency_matrix[i, j] != 0:
+                    qc.rzz(2 * gamma_values[layer], i, j)
+
+        # Mixer Hamiltonian
+        for i in range(num_qubits):
+            qc.rx(2 * beta_values[layer], i)
 
     qc.name = "qaoa"