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 anal
category	Creative & Media
source	antigravity
tags	["python","api","ai","workflow"]
url	https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/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

Core Capabilities

1. Setup and Installation

For detailed installation, authentication, and IBM Quantum account setup:

• See references/setup.md

Topics covered:

• Installation with uv
• Python environment setup
• IBM Quantum account and API token configuration
• Local vs. cloud execution

2. Building Quantum Circuits

For constructing quantum circuits with gates, measurements, and composition:

• See references/circuits.md

Topics covered:

• Creating circuits with QuantumCircuit
• Single-qubit gates (H, X, Y, Z, rotations, phase gates)
• Multi-qubit gates (CNOT, SWAP, Toffoli)
• Measurements and barriers
• Circuit composition and properties
• Parameterized circuits for variational algorithms

3. Primitives (Sampler and Estimator)

For executing quantum circuits and computing results:

• See references/primitives.md

Topics covered:

• Sampler: Get bitstring measurements and probability distributions
• Estimator: Compute expectation values of observables
• V2 interface (StatevectorSampler, StatevectorEstimator)
• IBM Quantum Runtime primitives for hardware
• Sessions and Batch modes
• Parameter binding

4. Transpilation and Optimization

For optimizing circuits and preparing for hardware execution:

• See references/transpilation.md

Topics covered:

• Why transpilation is necessary
• Optimization levels (0-3)
• Six transpilation stages (init, layout, routing, translation, optimization, scheduling)
• Advanced features (virtual permutation elision, gate cancellation)
• Common parameters (initial_layout, approximation_degree, seed)
• Best practices for efficient circuits

5. Visualization

For displaying circuits, results, and quantum states:

• See references/visualization.md

Topics covered:

• Circuit drawings (text, matplotlib, LaTeX)
• Result histograms
• Quantum state visualization (Bloch sphere, state city, QSphere)
• Backend topology and error maps
• Customization and styling
• Saving publication-quality figures

6. Hardware Backends

For running on simulators and real quantum computers:

• See references/backends.md

Topics covered:

• IBM Quantum backends and authentication
• Backend properties and status
• Running on real hardware with Runtime primitives
• Job management and queuing
• Session mode (iterative algorithms)
• Batch mode (parallel jobs)
• Local simulators (StatevectorSampler, Aer)
• Third-party providers (IonQ, Amazon Braket)
• Error mitigation strategies

7. Qiskit Patterns Workflow

For implementing the four-step quantum computing workflow:

• See references/patterns.md

Topics covered:

• Map: Translate problems to quantum circuits
• Optimize: Transpile for hardware
• Execute: Run with primitives
• Post-process: Extract and analyze results
• Complete VQE example
• Session vs. Batch execution
• Common workflow patterns

8. Quantum Algorithms and Applications

For implementing specific quantum algorithms:

• See references/algorithms.md

Topics covered:

• Optimization: VQE, QAOA, Grover's algorithm
• Chemistry: Molecular ground states, excited states, Hamiltonians
• Machine Learning: Quantum kernels, VQC, QNN
• Algorithm libraries: Qiskit Nature, Qiskit ML, Qiskit Optimization
• Physics simulations and benchmarking

Workflow Decision Guide

If you need to: