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qiskit-development IBM Qiskit quantum computing framework for circuit design, transpilation, execution, and analysis. Use when building quantum circuits, running on IBM Quantum hardware or simulators, running on Rigetti hardware via qBraid or AWS Braket directly, working with Qiskit Runtime primitives (Sampler/Estimator), optimizing transpilation, implementing quantum algorithms (VQE, QAOA, Grover), using QWARD's QuantumCircuitExecutor for simulate/run_ibm/run_qbraid workflows, direct AWS Braket submission with qiskit-braket-provider, noise model generation (IBM Heron R1-R3, Rigetti Ankaa-3), experiment campaigns with async job retrieval, or integrating with the QWARD metrics library. Covers Qiskit v2 primitives, session/batch execution modes, error mitigation, qBraid transpilation, AWS Braket integration, and visualization.
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npx skills add https://github.com/xthecapx/qiskit-qward --skill qiskit-developmentコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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Zipをダウンロード ダウンロード中... Expert Python development skill for scientific library design and best practices. Use when writing Python code, designing APIs, creating Pydantic schemas, building abstract base classes, implementing design patterns (Strategy, Factory, Observer), writing type-safe code with type hints, structuring Python packages, creating tests with pytest, managing dependencies, optimizing performance, or following PEP conventions. Tailored for the QWARD Qiskit extension library.
QWARD library for quantum circuit analysis, metrics extraction, performance evaluation, and visualization. Use when analyzing quantum circuits with Scanner, extracting metrics (QiskitMetrics, ComplexityMetrics, FidelityMetrics, ElementMetrics, StructuralMetrics, BehavioralMetrics, QuantumSpecificMetrics), visualizing results with the Visualizer API, implementing custom metric strategies, running experiments with BaseExperimentRunner, using noise model presets (IBM Heron, Rigetti Ankaa), or extending QWARD with custom metrics. Covers the Strategy pattern architecture, Pydantic schema validation, fluent API chaining, and the type-safe visualization system.
Trigger when: (1) User mentions "manim" or "Manim Community" or "ManimCE", (2) Code contains `from manim import *`, (3) User runs `manim` CLI commands, (4) Working with Scene, MathTex, Create(), or ManimCE-specific classes.
Best practices for Manim Community Edition - the community-maintained Python animation engine. Covers Scene structure, animations, LaTeX/MathTex, 3D with ThreeDScene, camera control, styling, and CLI usage.
NOT for ManimGL/3b1b version (which uses `manimlib` imports and `manimgl` CLI).
name qiskit-development description IBM Qiskit quantum computing framework for circuit design, transpilation, execution, and analysis. Use when building quantum circuits, running on IBM Quantum hardware or simulators, running on Rigetti hardware via qBraid or AWS Braket directly, working with Qiskit Runtime primitives (Sampler/Estimator), optimizing transpilation, implementing quantum algorithms (VQE, QAOA, Grover), using QWARD's QuantumCircuitExecutor for simulate/run_ibm/run_qbraid workflows, direct AWS Braket submission with qiskit-braket-provider, noise model generation (IBM Heron R1-R3, Rigetti Ankaa-3), experiment campaigns with async job retrieval, or integrating with the QWARD metrics library. Covers Qiskit v2 primitives, session/batch execution modes, error mitigation, qBraid transpilation, AWS Braket integration, and visualization.
Qiskit Development
Overview
Qiskit is the world's most popular open-source quantum computing framework (13M+ downloads). Build quantum circuits, optimize for hardware, execute on simulators or real quantum computers, and analyze results with QWARD metrics.
Key capabilities:
Backend-agnostic execution (local simulators, IBM Quantum cloud, or Rigetti via qBraid)
V2 primitives: StatevectorSampler, StatevectorEstimator
83x faster transpilation, 29% fewer two-qubit gates
Algorithm libraries for optimization, chemistry, and ML
QWARD's QuantumCircuitExecutor: unified simulate(), run_ibm(), run_qbraid() interface
Research-justified noise presets: IBM Heron R1/R2/R3, Rigetti Ankaa-3
Quick Start
from qiskit import QuantumCircuit
from qiskit.primitives import StatevectorSampler
qc = QuantumCircuit(2 )
qc.h(0
0
1
1024
0
print
Integration with QWARD from qiskit import QuantumCircuit
from qward import Scanner
from qward.metrics import QiskitMetrics, ComplexityMetrics
circuit = QuantumCircuit(2 )
circuit.h(0 )
circuit.cx(0 , 1 )
scanner = Scanner(circuit=circuit)
scanner.add_strategy(QiskitMetrics(circuit))
scanner.add_strategy(ComplexityMetrics(circuit))
results = scanner.calculate_metrics()
Reference Documentation Load these as needed based on your task:
references/setup.mdreferences/circuits.mdreferences/primitives.mdreferences/transpilation.mdreferences/visualization.mdreferences/backends.mdreferences/patterns.mdreferences/algorithms.mdreferences/qward-executor.md
Workflow Decision Guide
Install Qiskit or set up IBM Quantum account -> references/setup.md
Build a new quantum circuit -> references/circuits.md
Run circuits and get measurements -> 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
Execute on Rigetti via qBraid -> references/qward-executor.md (Pattern 3)
Execute on Rigetti via AWS Braket direct -> references/qward-executor.md (Pattern 4)
Run experiments with QWARD executor (simulate, IBM, Rigetti) -> references/qward-executor.md
Retrieve async AWS Braket jobs, CSV batch workflows -> references/qward-executor.md
Configure noise models (IBM Heron, Rigetti Ankaa) -> references/qward-executor.md
Implement end-to-end quantum workflow -> references/patterns.md
Build specific algorithm (VQE, QAOA, etc.) -> references/algorithms.md
Common Patterns
Pattern 1: QWARD Executor - Local Simulation from qiskit import QuantumCircuit
from qward.algorithms import QuantumCircuitExecutor
executor = QuantumCircuitExecutor(shots=1024 )
qc = QuantumCircuit(2 )
qc.h(0 )
qc.cx(0 , 1 )
qc.measure_all()
result = executor.simulate(qc, show_results=True )
print (result["counts" ])
print (result["qward_metrics" ])
result = executor.simulate(qc, noise_model="depolarizing" , noise_level=0.05 )
Pattern 2: QWARD Executor - IBM Quantum Hardware (Batch Mode) from qward.algorithms import QuantumCircuitExecutor
executor = QuantumCircuitExecutor(shots=1024 )
result = executor.run_ibm(
qc,
optimization_levels=[0 , 2 , 3 ],
success_criteria=lambda bs: bs.replace(" " , "" ) in ["00" , "11" ],
)
for job in result.jobs:
print (f"Opt {job.optimization_level} : depth={job.circuit_depth} , success={job.success_rate:.2 %} " )
Pattern 3: QWARD Executor - Rigetti via qBraid from qward.algorithms import QuantumCircuitExecutor
executor = QuantumCircuitExecutor(shots=1024 , timeout=300 )
result = executor.run_qbraid(
qc,
device_id="rigetti_aspen_m_3" ,
success_criteria=lambda bs: bs.replace(" " , "" ) in ["00" , "11" ],
)
print (f"Status: {result['status' ]} " )
print (f"QWARD metrics: {result['qward_metrics' ]} " )
Pattern 4: Rigetti via AWS Braket Direct (qiskit-braket-provider) import os
from qiskit_braket_provider import BraketProvider
os.environ['AWS_DEFAULT_REGION' ] = 'us-west-1'
provider = BraketProvider()
backend = provider.get_backend("Ankaa-3" )
from qiskit.circuit.library import Barrier
circuit_clean = qc.copy()
circuit_clean.data = [
(g, q, c) for g, q, c in qc.data if not isinstance (g, Barrier)
]
job = backend.run(circuit_clean, shots=10 )
print (f"Job ARN: {job.job_id()} " )
job = backend.retrieve_job(job.job_id())
raw_counts = dict (job._tasks[0 ].result().entries[0 ].entries[0 ].counts)
counts = {k[::-1 ]: v for k, v in raw_counts.items()}
Pattern 5: Research-Justified Noise Models from qward.algorithms import NoiseModelGenerator, get_preset_noise_config
noise = NoiseModelGenerator.create_from_config(get_preset_noise_config("IBM-HERON-R2" ))
result = executor.simulate(qc, noise_model=noise)
noise_rigetti = NoiseModelGenerator.create_from_config(get_preset_noise_config("RIGETTI-ANKAA3" ))
result = executor.simulate(qc, noise_model=noise_rigetti)
Pattern 6: 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' )
Pattern 7: Experiment Campaign (Systematic Multi-Config) from qward.algorithms import BaseExperimentRunner
runner = MyAlgorithmRunner()
results = runner.run_campaign(
config_ids=["S2-1" , "S3-1" , "S4-1" ],
noise_ids=["IDEAL" , "IBM-HERON-R2" , "RIGETTI-ANKAA3" ],
num_runs=10 ,
)
Best Practices
Start with simulators : Use executor.simulate() before hardwareAlways transpile : Use optimization_level=3 for productionUse QWARD executor : Unified interface for simulate, IBM QPU, RigettiUse appropriate primitives : Sampler for bitstrings, Estimator for expectation valuesChoose execution mode : Session for iterative (VQE/QAOA), Batch for parallel, qBraid for RigettiUse hardware-calibrated noise : Preset noise models for IBM Heron and Rigetti AnkaaMinimize two-qubit gates : Major error source on hardwareSave job IDs : For later retrieval of hardware resultsApply error mitigation : Use resilience_level in runtime optionsRun experiment campaigns : Systematic comparison across configs and noise models 