Qiskit Algorithms and Qiskit Optimization support for SamplerV2 (QiskitRuntimeService) (https://github.com/qiskit-community/qiskit-algorithms/issues/213)

[baniabde2020]

Environment

• Qiskit Algorithms version: 0.3.1
• Qiskit Optimization version: 0.6.1
• Python version: 3.9.13
• Operating system: windows 11

What is happening?

Hi everyone,
I am trying to run a small example of ADMMOptimizer on an ibm quantum machine using QiskitRuntimeService and SamplerV2, I am getting the error bellow.
Any help or suggestions to resolve this issue.
Thank you !

How can we reproduce the issue?

import matplotlib.pyplot as plt
from docplex.mp.model import Model
from qiskit_algorithms import QAOA, NumPyMinimumEigensolver
from qiskit_algorithms.optimizers import COBYLA
from qiskit_ibm_runtime import SamplerV2 as Sampler
from qiskit_optimization.algorithms import CobylaOptimizer, MinimumEigenOptimizer
from qiskit_optimization.algorithms.admm_optimizer import ADMMParameters, ADMMOptimizer
from qiskit_optimization.translators import from_docplex_mp
from qiskit_ibm_runtime import QiskitRuntimeService

# Initialize Qiskit Runtime Service and select backend
service = QiskitRuntimeService()
backend = service.least_busy(min_num_qubits=127)
print(backend)

# Define COBYLA optimizer to handle convex continuous problems
cobyla = CobylaOptimizer()

# Define QAOA via the minimum eigen optimizer
qaoa = MinimumEigenOptimizer(QAOA(sampler=Sampler(mode=backend), optimizer=COBYLA()))

# Exact QUBO solver as classical benchmark
exact = MinimumEigenOptimizer(NumPyMinimumEigensolver())  # to solve QUBOs

# Construct model using DOcplex
mdl = Model("ex6")
v = mdl.binary_var(name="v")
w = mdl.binary_var(name="w")
t = mdl.binary_var(name="t")
u = mdl.continuous_var(name="u")

mdl.minimize(v + w + t + 5 * (u - 2) ** 2)
mdl.add_constraint(v + 2 * w + t + u <= 3, "cons1")
mdl.add_constraint(v + w + t >= 1, "cons2")
mdl.add_constraint(v + w == 1, "cons3")

# Load quadratic program from DOcplex model
qp = from_docplex_mp(mdl)

# Define ADMM parameters
admm_params = ADMMParameters(
    rho_initial=1001, beta=1000, factor_c=900, maxiter=100, three_block=True, tol=1.0e-6
)

# Quantum Solution
# Define QUBO optimizer
qubo_optimizer = qaoa

# Define classical optimizer
convex_optimizer = cobyla

# Initialize ADMM with quantum QUBO optimizer and classical convex optimizer
admm_q = ADMMOptimizer(
    params=admm_params,
    qubo_optimizer=qubo_optimizer,
    continuous_optimizer=convex_optimizer,
)

# Run ADMM to solve problem
result_q = admm_q.solve(qp)
print(result_q.prettyprint())

# Plot residuals
plt.clf()
plt.plot(result_q.state.residuals)
plt.xlabel("Iterations")
plt.ylabel("Residuals")
plt.show()

What should happen?

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: run() takes 2 positional arguments but 3 were given

Any suggestions?

@jwoehr suggest that Qiskit-algorithms or Qiskit Optimization needs some work to support and be compatible with SamplerV2 and QiskitRuntimeService.