Sampling

A SamplingProblem asks for samples from a circuit’s output distribution: |bitstring> -> probability.

Usage

from microquantum import QuantumCircuit, SamplingProblem

qc = QuantumCircuit(2).h(0).cx(0, 1)       # Bell state
problem = SamplingProblem(qc, num_samples=1024, name="bell")

print(problem.circuit)
print(problem.validate())                  # []

data = problem.to_dict()
restored = SamplingProblem.from_dict(data)  # JSON-safe round trip
print(restored.num_samples)                # 1024

What it maps to

• Solvers usually come from the runtime helpers — execute() / run_parameter_sweep() / ExecutionRuntime — which bind the circuit, dispatch it to a backend and return a BackendResult with counts.

• Analysis — SamplingAnalysis turns those counts into outcome probabilities, entropy and marginals (see Sampling).

Example through the runtime

from microquantum import ExecutionRuntime, StatevectorBackend

runtime = ExecutionRuntime(backend=StatevectorBackend())
result = runtime.execute(
    problem.circuit,
    shots=problem.num_samples,
    seed=1,
)
print(result.counts)