---
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."
license: Apache-2.0 license
metadata:
    skill-author: K-Dense Inc.
risk: unknown
source: community
---

# Qiskit

## 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

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

### First Circuit

```python
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

```python
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:**

- Install Qiskit or set up IBM Quantum account → `references/setup.md`
- Build a new quantum circuit → `references/circuits.md`
- Understand gates and circuit operations → `references/circuits.md`
- Run circuits and get measurements → `references/primitives.md`
- Compute expectation values → `references/primitives.md`
- Optimize circuits for hardware → `references/transpilation.md`
- Visualize circuits or results → `references/visualization.md`
- Execute on IBM Quantum hardware → `references/backends.md`
- Connect to third-party providers → `references/backends.md`
- Implement end-to-end quantum workflow → `references/patterns.md`
- Build specific algorithm (VQE, QAOA, etc.) → `references/algorithms.md`
- Solve chemistry or optimization problems → `references/algorithms.md`

## Best Practices

### Development Workflow

1. **Start with simulators**: Test locally before using hardware
   ```python
   from qiskit.primitives import StatevectorSampler
   sampler = StatevectorSampler()
   ```

2. **Always transpile**: Optimize circuits before execution
   ```python
   from qiskit import transpile
   qc_optimized = transpile(qc, backend=backend, optimization_level=3)
   ```

3. **Use appropriate primitives**:
   - Sampler for bitstrings (optimization algorithms)
   - Estimator for expectation values (chemistry, physics)

4. **Choose execution mode**:
   - Session: Iterative algorithms (VQE, QAOA)
   - Batch: Independent parallel jobs
   - Single job: One-off experiments

### Performance Optimization

- Use optimization_level=3 for production
- Minimize two-qubit gates (major error source)
- Test with noisy simulators before hardware
- Save and reuse transpiled circuits
- Monitor convergence in variational algorithms

### Hardware Execution

- Check backend status before submitting
- Use least_busy() for testing
- Save job IDs for later retrieval
- Apply error mitigation (resilience_level)
- Start with fewer shots, increase for final runs

## Common Patterns

### Pattern 1: Simple Circuit Execution

```python
from qiskit import QuantumCircuit, transpile
from qiskit.primitives import StatevectorSampler

qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
qc.measure_all()

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

### Pattern 2: Hardware Execution with Transpilation

```python
from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler
from qiskit import transpile

service = QiskitRuntimeService()
backend = service.backend("ibm_brisbane")

qc_optimized = transpile(qc, backend=backend, optimization_level=3)

sampler = Sampler(backend)
job = sampler.run([qc_optimized], shots=1024)
result = job.result()
```

### Pattern 3: Variational Algorithm (VQE)

```python
from qiskit_ibm_runtime import Session, EstimatorV2 as Estimator
from scipy.optimize import minimize

with Session(backend=backend) as session:
    estimator = Estimator(session=session)

    def cost_function(params):
        bound_qc = ansatz.assign_parameters(params)
        qc_isa = transpile(bound_qc, backend=backend)
        result = estimator.run([(qc_isa, hamiltonian)]).result()
        return result[0].data.evs

    result = minimize(cost_function, initial_params, method='COBYLA')
```

## Additional Resources

- **Official Docs**: https://quantum.ibm.com/docs
- **Qiskit Textbook**: https://qiskit.org/learn
- **API Reference**: https://docs.quantum.ibm.com/api/qiskit
- **Patterns Guide**: https://quantum.cloud.ibm.com/docs/en/guides/intro-to-patterns
