qiskit

name	qiskit
description	Qiskit is the world's most popular open-source quantum computing framework with 13M+ downloads. Build quantum circuits, optimize for hardware, execute on simulators or real quantum computers, and analyze results. Supports IBM Quantum (100+ qubit systems), IonQ, Amazon Braket, and other providers.
type	skill
created	2026-02-27T00:00:00.000Z
domain	software-development
category	devtools
risk	unknown
source	community
tags	["skill","software-development","devtools","qiskit"]

Qiskit

When to Use

• You are building or optimizing quantum circuits with Qiskit for simulators or real hardware.
• You need IBM Quantum-style tooling for transpilation, execution, visualization, or algorithm libraries.
• You want guidance on moving from a simple circuit prototype to backend-aware execution.

Overview

Qiskit is the world's most popular open-source quantum computing framework with 13M+ downloads. Build quantum circuits, optimize for hardware, execute on simulators or real quantum computers, and analyze results. Supports IBM Quantum (100+ qubit systems), IonQ, Amazon Braket, and other providers.

Key Features:

• 83x faster transpilation than competitors
• 29% fewer two-qubit gates in optimized circuits
• Backend-agnostic execution (local simulators or cloud hardware)
• Comprehensive algorithm libraries for optimization, chemistry, and ML

Quick Start

Installation

uv pip install qiskit
uv pip install "qiskit[visualization]" matplotlib

First Circuit

from qiskit import QuantumCircuit
from qiskit.primitives import StatevectorSampler

# Create Bell state (entangled qubits)
qc = QuantumCircuit(2)
qc.h(0)           # Hadamard on qubit 0
qc.cx(0, 1)       # CNOT from qubit 0 to 1
qc.measure_all()  # Measure both qubits

# Run locally
sampler = StatevectorSampler()
result = sampler.run([qc], shots=1024).result()
counts = result[0].data.meas.get_counts()
print(counts)  # {'00': ~512, '11': ~512}

Visualization

from qiskit.visualization import plot_histogram

qc.draw('mpl')           # Circuit diagram
plot_histogram(counts)   # Results histogram