qiskit

Qiskit quantum circuits are built by generating a QuantumCircuit object, transpiling it for a specific backend, and executing it via Sampler or Estimator primitives to extract measurable outputs like bitstring probabilities or expectation values.

Qiskit Sampler primitives execute quantum circuits to return bitstring probabilities, whereas Estimator primitives compute expectation values or energies, making each suited for different quantum algorithm workflows and analysis types.

Quantum circuit transpilation for IBM Quantum hardware involves optimizing the circuit operations to match the target backend's constraints, ensuring executable primitive runs and accurate measurable outputs on the runtime system.

Yes, Qiskit supports variational algorithms like VQE and QAOA by translating problem goals into QuantumCircuit objects and executing them via Estimator primitives to obtain expectation values for optimization workflows.

Qiskit supports quantum machine learning and quantum chemistry or material simulations by mapping application patterns into QuantumCircuit objects and executing them across local simulators and IBM Quantum hardware.

Local simulators face scaling constraints with large quantum circuits, whereas IBM Quantum runtime execution requires proper transpilation targeting and primitive execution to handle hardware-specific noise and structural limitations.