qiskit

•

24.4k23993 months ago

Comprehensive quantum computing toolkit for building, optimizing, and executing quantum circuits. Use when working with quantum algorithms, simulations, or quantum hardware including (1) Building quantum circuits with gates and measurements, (2) Running quantum algorithms (VQE, QAOA, Grover), (3) Transpiling/optimizing circuits for hardware, (4) Executing on IBM Quantum or other providers, (5) Quantum chemistry and materials science, (6) Quantum machine learning, (7) Visualizing circuits and results, or (8) Any quantum computing development task.

Quick Install

npxskills add davila7/claude-code-templates--skill qiskit

Instructions

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

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:

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

Best Practices

Development Workflow

Start with simulators: Test locally before using hardware

from qiskit.primitives import StatevectorSampler
sampler = StatevectorSampler()

Always transpile: Optimize circuits before execution

from qiskit import transpile
qc_optimized = transpile(qc, backend=backend, optimization_level=3)

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

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

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)

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

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Qiskit

Overview

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:

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

Best Practices

Development Workflow

Start with simulators: Test locally before using hardware

from qiskit.primitives import StatevectorSampler
sampler = StatevectorSampler()

Always transpile: Optimize circuits before execution

from qiskit import transpile
qc_optimized = transpile(qc, backend=backend, optimization_level=3)

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

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

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)

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