Merge pull request #66 from Fujitsu-UTokyo-QDD/feature/add_tests

add tests using vqe and qaoa

test/python/qiskit_tutorials/algorithms/test_qaoa.py

@@ -0,0 +1,117 @@
+# The code in this file has been written using part of the code in the Qiskit tutorial:
+# https://learning.quantum.ibm.com/tutorial/quantum-approximate-optimization-algorithm
+import numpy as np
+from qiskit.circuit.library import QAOAAnsatz
+from qiskit.quantum_info import SparsePauliOp
+from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
+from scipy.optimize import minimize
+from typing import Sequence
+import rustworkx as rx
+
+from qdd import QddProvider
+from qdd.qdd_estimator import Estimator
+from qdd.qdd_sampler import Sampler
+
+
+def test_qaoa():
+    n = 5
+
+    graph = rx.PyGraph()
+    graph.add_nodes_from(np.arange(0, n, 1))
+    edge_list = [
+        (0, 1, 1.0),
+        (0, 2, 1.0),
+        (0, 4, 1.0),
+        (1, 2, 1.0),
+        (2, 3, 1.0),
+        (3, 4, 1.0),
+    ]
+    graph.add_edges_from(edge_list)
+
+    def build_max_cut_paulis(graph: rx.PyGraph):
+        """Convert the graph to Pauli list.
+
+        This function does the inverse of `build_max_cut_graph`
+        """
+        pauli_list = []
+        for edge in list(graph.edge_list()):
+            paulis = ["I"] * len(graph)
+            paulis[edge[0]], paulis[edge[1]] = "Z", "Z"
+
+            weight = graph.get_edge_data(edge[0], edge[1])
+
+            pauli_list.append(("".join(paulis)[::-1], weight))
+
+        return pauli_list
+
+    max_cut_paulis = build_max_cut_paulis(graph)
+
+    cost_hamiltonian = SparsePauliOp.from_list(max_cut_paulis)
+
+    circuit = QAOAAnsatz(cost_operator=cost_hamiltonian, reps=2)
+    circuit.measure_all()
+
+    backend = QddProvider().get_backend()
+
+    # Create pass manager for transpilation
+    pm = generate_preset_pass_manager(optimization_level=3, backend=backend)
+
+    candidate_circuit = pm.run(circuit)
+
+    initial_gamma = np.pi
+    initial_beta = np.pi / 2
+    init_params = [initial_gamma, initial_beta, initial_gamma, initial_beta]
+
+    def cost_func_estimator(params, ansatz, hamiltonian, estimator):
+
+        # transform the observable defined on virtual qubits to
+        # an observable defined on all physical qubits
+        isa_hamiltonian = hamiltonian.apply_layout(ansatz.layout)
+
+        job = estimator.run([ansatz], [isa_hamiltonian], [params])
+
+        results = job.result()
+        cost = results.values[0]
+
+        return cost
+
+    estimator = Estimator(run_options={"shots": None}, approximation=True)
+
+    result = minimize(
+        cost_func_estimator,
+        init_params,
+        args=(candidate_circuit, cost_hamiltonian, estimator),
+        method="COBYLA",
+        tol=1e-2,
+    )
+
+    optimized_circuit = candidate_circuit.assign_parameters(result.x)
+
+    sampler = Sampler(run_options={"shots": None})
+
+    job = sampler.run(optimized_circuit)
+    result = job.result()
+    final_distribution_int = result.quasi_dists[0]
+
+    # auxiliary functions to sample most likely bitstring
+    def to_bitstring(integer, num_bits):
+        result = np.binary_repr(integer, width=num_bits)
+        return [int(digit) for digit in result]
+
+    keys = list(final_distribution_int.keys())
+    values = list(final_distribution_int.values())
+    most_likely = keys[np.argmax(np.abs(values))]
+    most_likely_bitstring = to_bitstring(most_likely, len(graph))
+    most_likely_bitstring.reverse()
+
+    def evaluate_sample(x: Sequence[int], graph: rx.PyGraph) -> float:
+        assert len(x) == len(
+            list(graph.nodes())
+        ), "The length of x must coincide with the number of nodes in the graph."
+        return sum(
+            x[u] * (1 - x[v]) + x[v] * (1 - x[u]) for u, v in list(graph.edge_list())
+        )
+
+    cut_value = evaluate_sample(most_likely_bitstring, graph)
+
+    assert cut_value == 5

test/python/qiskit_tutorials/algorithms/test_vqe.py

@@ -0,0 +1,55 @@
+# The code in this file has been written using part of the code in the Qiskit tutorial:
+# https://learning.quantum.ibm.com/tutorial/variational-quantum-eigensolver
+
+import numpy as np
+import pytest
+from qiskit.circuit.library import EfficientSU2
+from qiskit.primitives import Estimator as QiskitEstimator
+from qiskit.quantum_info import SparsePauliOp
+from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
+from scipy.optimize import minimize
+
+from qdd import QddProvider
+from qdd.qdd_estimator import Estimator as QddEstimator
+
+
+def test_sampler():
+    backend = QddProvider().get_backend()
+    estimator_qdd = QddEstimator(run_options={"shots": None}, approximation=True)
+
+    estimator_qiskit = QiskitEstimator()
+
+    hamiltonian = SparsePauliOp.from_list(
+        [("YZ", 0.3980), ("ZI", -0.3980), ("ZZ", -0.0113), ("XX", 0.1810)]
+    )
+
+    ansatz = EfficientSU2(hamiltonian.num_qubits)
+    num_params = ansatz.num_parameters
+
+
+    pm = generate_preset_pass_manager(backend=backend, optimization_level=3)
+
+    ansatz_isa = pm.run(ansatz)
+
+    def cost_func(params, ansatz, hamiltonian, estimator):
+        result = estimator.run(ansatz, hamiltonian, params).result()
+        energy = result.values[0]
+
+        return energy
+
+    x0 = 2 * np.pi * np.random.random(num_params)
+    result_qdd = minimize(
+        cost_func, x0, args=(ansatz_isa, hamiltonian, estimator_qdd), method="cobyla"
+    )
+    energy_qdd = result_qdd.fun
+
+    x0 = 2 * np.pi * np.random.random(num_params)
+    result_qiskit = minimize(
+        cost_func,
+        x0,
+        args=(ansatz_isa, hamiltonian, estimator_qiskit),
+        method="cobyla",
+    )
+    energy_qiskit = result_qiskit.fun
+
+    assert pytest.approx(energy_qdd, abs=1e-2) == energy_qiskit