我正在尝试在 IBM Quantum 后端上使用 QAOA 解决顶点覆盖问题。我已成功将图表转换为 QUBO 公式，然后转换为伊辛哈密顿量格式。但是，当我尝试在后端运行我的量子电路时，它无法按预期工作。
这是我的代码：
from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler, Session
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit_algorithms import QAOA
from qiskit_algorithms.optimizers import COBYLA
from qiskit_algorithms.utils import algorithm_globals
service = QiskitRuntimeService(channel="ibm_quantum", token="mytoken")
backend = service.backend("ibm_sherbrooke")
# QAOA configuration
algorithm_globals.random_seed = 123
sampler = Sampler(mode=backend)
qaoa = QAOA(sampler=sampler, optimizer=COBYLA(), reps=3)
# Use QAOA as the optimizer
optimizer = MinimumEigenOptimizer(qaoa)
# Solve the problem
result = optimizer.solve(qubo)
# Display results
print(result)
这是我运行代码后收到的错误消息：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[25], line 19
16 optimizer = MinimumEigenOptimizer(qaoa)
18 # Problem lösen
---> 19 result = optimizer.solve(qubo)
21 # Ergebnisse anzeigen
22 print(result)
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\qiskit_optimization\algorithms\minimum_eigen_optimizer.py:205, in MinimumEigenOptimizer.solve(self, problem)
202 # construct operator and offset
203 operator, offset = problem_.to_ising()
--> 205 return self._solve_internal(operator, offset, problem_, problem)
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\qiskit_optimization\algorithms\minimum_eigen_optimizer.py:218, in MinimumEigenOptimizer._solve_internal(self, operator, offset, converted_problem, original_problem)
215 eigen_result: Optional[MinimumEigensolverResult] = None
216 if operator.num_qubits > 0:
217 # approximate ground state of operator using min eigen solver
--> 218 eigen_result = self._min_eigen_solver.compute_minimum_eigenvalue(operator)
219 # analyze results
220 raw_samples = None
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\qiskit_algorithms\minimum_eigensolvers\sampling_vqe.py:225, in SamplingVQE.compute_minimum_eigenvalue(self, operator, aux_operators)
220 else:
221 # we always want to submit as many estimations per job as possible for minimal
222 # overhead on the hardware
223 was_updated = _set_default_batchsize(self.optimizer)
--> 225 optimizer_result = self.optimizer.minimize(
226 fun=evaluate_energy, # type: ignore[arg-type]
227 x0=initial_point,
228 bounds=bounds,
229 )
231 # reset to original value
232 if was_updated:
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\qiskit_algorithms\optimizers\scipy_optimizer.py:148, in SciPyOptimizer.minimize(self, fun, x0, jac, bounds)
145 swapped_deprecated_args = True
146 self._options["maxfun"] = self._options.pop("maxiter")
--> 148 raw_result = minimize(
149 fun=fun,
150 x0=x0,
151 method=self._method,
152 jac=jac,
153 bounds=bounds,
154 options=self._options,
155 **self._kwargs,
156 )
157 if swapped_deprecated_args:
158 self._options["maxiter"] = self._options.pop("maxfun")
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\scipy\optimize\_minimize.py:737, in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
734 res = _minimize_tnc(fun, x0, args, jac, bounds, callback=callback,
735 **options)
736 elif meth == 'cobyla':
--> 737 res = _minimize_cobyla(fun, x0, args, constraints, callback=callback,
738 bounds=bounds, **options)
739 elif meth == 'cobyqa':
740 res = _minimize_cobyqa(fun, x0, args, bounds, constraints, callback,
741 **options)
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\scipy\optimize\_cobyla_py.py:35, in synchronized.<locals>.wrapper(*args, **kwargs)
32 @functools.wraps(func)
33 def wrapper(*args, **kwargs):
34 with _module_lock:
---> 35 return func(*args, **kwargs)
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\scipy\optimize\_cobyla_py.py:278, in _minimize_cobyla(fun, x0, args, constraints, rhobeg, tol, maxiter, disp, catol, callback, bounds, **unknown_options)
275 def _jac(x, *args):
276 return None
--> 278 sf = _prepare_scalar_function(fun, x0, args=args, jac=_jac)
280 def calcfc(x, con):
281 f = sf.fun(x)
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\scipy\optimize\_optimize.py:288, in _prepare_scalar_function(fun, x0, jac, args, bounds, epsilon, finite_diff_rel_step, hess)
284 bounds = (-np.inf, np.inf)
286 # ScalarFunction caches. Reuse of fun(x) during grad
287 # calculation reduces overall function evaluations.
--> 288 sf = ScalarFunction(fun, x0, args, grad, hess,
289 finite_diff_rel_step, bounds, epsilon=epsilon)
291 return sf
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\scipy\optimize\_differentiable_functions.py:222, in ScalarFunction.__init__(self, fun, x0, args, grad, hess, finite_diff_rel_step, finite_diff_bounds, epsilon)
219 finite_diff_options["as_linear_operator"] = True
221 # Initial function evaluation
--> 222 self._update_fun()
224 # Initial gradient evaluation
225 self._wrapped_grad, self._ngev = _wrapper_grad(
226 grad,
227 fun=self._wrapped_fun,
228 args=args,
229 finite_diff_options=finite_diff_options
230 )
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\scipy\optimize\_differentiable_functions.py:294, in ScalarFunction._update_fun(self)
292 def _update_fun(self):
293 if not self.f_updated:
--> 294 fx = self._wrapped_fun(self.x)
295 if fx < self._lowest_f:
296 self._lowest_x = self.x
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\scipy\optimize\_differentiable_functions.py:20, in _wrapper_fun.<locals>.wrapped(x)
16 ncalls[0] += 1
17 # Send a copy because the user may overwrite it.
18 # Overwriting results in undefined behaviour because
19 # fun(self.x) will change self.x, with the two no longer linked.
---> 20 fx = fun(np.copy(x), *args)
21 # Make sure the function returns a true scalar
22 if not np.isscalar(fx):
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\qiskit_algorithms\minimum_eigensolvers\sampling_vqe.py:320, in SamplingVQE._get_evaluate_energy.<locals>.evaluate_energy(parameters)
315 parameters = np.reshape(parameters, (-1, num_parameters)).tolist()
316 batch_size = len(parameters)
318 estimator_result = estimator.run(
319 batch_size * [ansatz], batch_size * [operator], parameters
--> 320 ).result()
321 values = estimator_result.values
323 if self.callback is not None:
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\qiskit\primitives\primitive_job.py:51, in PrimitiveJob.result(self)
49 def result(self) -> ResultT:
50 self._check_submitted()
---> 51 return self._future.result()
File ~\AppData\Local\Programs\Python\Python312\Lib\concurrent\futures\_base.py:449, in Future.result(self, timeout)
447 raise CancelledError()
448 elif self._state == FINISHED:
--> 449 return self.__get_result()
451 self._condition.wait(timeout)
453 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
File ~\AppData\Local\Programs\Python\Python312\Lib\concurrent\futures\_base.py:401, in Future.__get_result(self)
399 if self._exception:
400 try:
--> 401 raise self._exception
402 finally:
403 # Break a reference cycle with the exception in self._exception
404 self = None
File ~\AppData\Local\Programs\Python\Python312\Lib\concurrent\futures\thread.py:58, in _WorkItem.run(self)
55 return
57 try:
---> 58 result = self.fn(*self.args, **self.kwargs)
59 except BaseException as exc:
60 self.future.set_exception(exc)
File f:\QuantumComputingProjects\IBM_Qiskit\Lib\site-packages\qiskit_algorithms\minimum_eigensolvers\diagonal_estimator.py:117, in _DiagonalEstimator._call(self, circuits, observables, parameter_values, **run_options)
110 def _call(
111 self,
112 circuits: Sequence[int],
(...)
115 **run_options,
116 ) -> _DiagonalEstimatorResult:
--> 117 job = self.sampler.run(
118 [self._circuits[i] for i in circuits],
119 parameter_values,
120 **run_options,
121 )
122 sampler_result = job.result()
123 samples = sampler_result.quasi_dists
TypeError: SamplerV2.run() takes 2 positional arguments but 3 were given
我有几个问题：
任何帮助将不胜感激！
Qiskit Optimization 不支持现在在真实设备（IBM 后端）上运行所需的 V2 版本原语。尽管有一些工作正在进行中，但有一个未解决的问题https://github.com/qiskit-community/qiskit-optimization/issues/587，您可以在存储库的拉取请求中看到。 Qiskit 优化发布版本所依赖的 Qiskit 算法也是如此。