Skip to content

Fix unit test of GroverOptimizer #96

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
manoelmarques merged 7 commits into from
Apr 23, 2021
Merged

Fix unit test of GroverOptimizer #96

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
3945892
fix unittest of grover optimizer
a-matsuo Apr 22, 2021
784cf7c
fix
a-matsuo Apr 22, 2021
546265a
fix lint
a-matsuo Apr 22, 2021
066f54a
fix unittest and timing of updating samples
a-matsuo Apr 23, 2021
8781488
clean up
a-matsuo Apr 23, 2021
e9c4f05
fix lint
a-matsuo Apr 23, 2021
cdbc8d4
Merge branch 'main' into fix_grover_opt_test
t-imamichi Apr 23, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
34 changes: 17 additions & 17 deletions qiskit_optimization/algorithms/grover_optimizer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -161,8 +161,7 @@ def solve(self, problem: QuadraticProgram) -> OptimizationResult:
problem_init = deepcopy(problem_)

# convert to minimization problem
sense = problem_.objective.sense
if sense == problem_.objective.Sense.MAXIMIZE:
if problem_.objective.sense == problem_.objective.Sense.MAXIMIZE:
problem_.objective.sense = problem_.objective.Sense.MINIMIZE
problem_.objective.constant = -problem_.objective.constant
for i, val in problem_.objective.linear.to_dict().items():
Expand All @@ -176,7 +175,7 @@ def solve(self, problem: QuadraticProgram) -> OptimizationResult:
optimum_key = math.inf
optimum_value = math.inf
threshold = 0
n_key = len(problem_.variables)
n_key = self._num_key_qubits
n_value = self._num_value_qubits

# Variables for tracking the solutions encountered.
Expand Down Expand Up @@ -236,6 +235,20 @@ def solve(self, problem: QuadraticProgram) -> OptimizationResult:
logger.info('Current Optimum Value: %s', optimum_value)
improvement_found = True
threshold = optimum_value

# trace out work qubits and store samples
if self._quantum_instance.is_statevector: # type: ignore
indices = list(range(n_key, len(outcome)))
rho = partial_trace(self._circuit_results, indices)
self._circuit_results = np.diag(rho.data) ** 0.5
else:
self._circuit_results = {i[0:n_key]: v for i,
v in self._circuit_results.items()}

raw_samples = self._eigenvector_to_solutions(self._circuit_results,
problem_init)
raw_samples.sort(key=lambda x: problem_.objective.sense.value * x.fval)
samples = self._interpret_samples(problem, raw_samples, self._converters)
else:
# Using Durr and Hoyer method, increase m.
m = int(np.ceil(min(m * 8 / 7, 2 ** (n_key / 2))))
Expand All @@ -251,19 +264,6 @@ def solve(self, problem: QuadraticProgram) -> OptimizationResult:
improvement_found = True
optimum_found = True

# trace out work qubits
if self._quantum_instance.is_statevector: # type: ignore
indices = list(range(n_key, len(outcome)))
rho = partial_trace(self._circuit_results, indices)
self._circuit_results = np.diag(rho.data) ** 0.5
else:
self._circuit_results = {i[0:n_key]: v for i,
v in self._circuit_results.items()}

raw_samples = self._eigenvector_to_solutions(self._circuit_results, problem_init)
raw_samples.sort(key=lambda x: problem_.objective.sense.value * x.fval)
samples = self._interpret_samples(problem, raw_samples, self._converters)

# Track the operation count.
operations = circuit.count_ops()
operation_count[iteration] = operations
Expand Down Expand Up @@ -313,7 +313,7 @@ def _get_probs(self, qc: QuantumCircuit) -> Dict[str, float]:
state = result.get_counts(qc)
shots = self.quantum_instance.run_config.shots
hist = {key[::-1]: val / shots for key, val in state.items() if val > 0}
self._circuit_results = {b[::-1]: (v / shots) ** 0.5 for (b, v) in state.items()}
self._circuit_results = {b[::-1]: np.sqrt(v / shots) for (b, v) in state.items()}
return hist

@staticmethod
Expand Down
8 changes: 4 additions & 4 deletions test/algorithms/test_grover_optimizer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -179,7 +179,7 @@ def test_samples_and_raw_samples(self):
success = OptimizationResultStatus.SUCCESS
algorithm_globals.random_seed = 1
grover_optimizer = GroverOptimizer(
5, num_iterations=2, quantum_instance=self.qasm_simulator)
8, num_iterations=5, quantum_instance=self.qasm_simulator)
result = grover_optimizer.solve(op)
self.assertEqual(len(result.samples), 8)
self.assertEqual(len(result.raw_samples), 32)
Expand All @@ -190,9 +190,9 @@ def test_samples_and_raw_samples(self):
self.assertAlmostEqual(min(s.fval for s in result.raw_samples), opt_sol)
for sample in result.raw_samples:
self.assertEqual(sample.status, success)
np.testing.assert_array_almost_equal(result.x, result.samples[0].x)
self.assertAlmostEqual(result.fval, result.samples[0].fval)
self.assertEqual(result.status, result.samples[0].status)
np.testing.assert_array_almost_equal(result.x, result.raw_samples[0].x[0:2])
self.assertAlmostEqual(result.fval, result.raw_samples[0].fval)
self.assertEqual(result.status, result.raw_samples[0].status)


if __name__ == '__main__':
Expand Down