Build and Optimize Quantum Circuits with Qiskit

This skill provides comprehensive guidance for Qiskit, the open-source quantum computing framework with 13M+ downloads that supports IBM Quantum (100+ qubit systems), IonQ, Amazon Braket, and other providers. It covers building quantum circuits with QuantumCircuit (single- and multi-qubit gates, measurements, parameterized circuits for variational algorithms), executing via Sampler and Estimator primitives (bitstring measurements vs. expectation values, V2 interfaces, IBM Quantum Runtime Sessions and Batch modes), transpilation and optimization (six-stage pipeline, optimization levels 0-3, minimizing two-qubit gates), visualization (circuit diagrams, result histograms, Bloch sphere/state city/QSphere state visualization), hardware backend selection and job management (local simulators, IBM Quantum hardware, third-party providers, error mitigation), the four-step Qiskit Patterns workflow (Map, Optimize, Execute, Post-process), and specific quantum algorithms (VQE, QAOA, Grover's for optimization; molecular ground/excited states for chemistry; quantum kernels/VQC/QNN for machine learning via Qiskit Nature, Qiskit ML, and Qiskit Optimization).

Use it when building or optimizing quantum circuits with Qiskit for simulators or real hardware, when you need IBM Quantum-style tooling for transpilation, execution, visualization, or algorithm libraries, or when moving from a simple circuit prototype to backend-aware execution.

Always start development with local simulators before using real hardware, and always transpile circuits with an appropriate optimization level before execution - skipping transpilation or testing directly on hardware wastes queue time and quota. Treat its output as a starting implementation to validate in your own environment, not a substitute for testing or expert review - use it only when the task clearly matches Qiskit-based quantum development, and stop to ask for clarification if required inputs or success criteria are missing.

Input: a description of the quantum circuit, algorithm, or hardware execution task needed. Output: runnable Qiskit Python code for circuit construction, transpilation, execution, and result analysis.

from qiskit import QuantumCircuit
from qiskit.primitives import StatevectorSampler

qc = QuantumCircuit(2)
qc.h(0)           # Hadamard on qubit 0
qc.cx(0, 1)        # CNOT from qubit 0 to 1
qc.measure_all()

sampler = StatevectorSampler()
result = sampler.run([qc], shots=1024).result()
counts = result[0].data.meas.get_counts()

Connects to IBM Quantum hardware (100+ qubit systems) via qiskit_ibm_runtime, plus third-party providers IonQ and Amazon Braket, and algorithm libraries Qiskit Nature, Qiskit ML, and Qiskit Optimization. Bundles eight detailed reference files (setup, circuits, primitives, transpilation, visualization, backends, patterns, algorithms) loaded as needed for specific topics.

Quantum computing researchers and developers building circuits, running variational algorithms like VQE/QAOA, or moving from local simulation to real IBM Quantum hardware execution, who want correct primitive/transpilation usage rather than assembling Qiskit calls from scattered documentation.