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

python

from qiskit import QuantumCircuit
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()
print(counts)  # {'00': ~512, '11': ~512}

Integration with QWARD

python

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.md - Installation, IBM Quantum account, authentication
•references/circuits.md - QuantumCircuit building, gates, measurements, composition
•references/primitives.md - Sampler and Estimator (V2), parameter binding, sessions
•references/transpilation.md - Optimization levels 0-3, layout, routing, basis gates
•references/visualization.md - Circuit drawings, histograms, Bloch spheres, state plots
•references/backends.md - IBM Quantum, IonQ, Aer, Rigetti via qBraid, Rigetti via AWS Braket direct, error mitigation
•references/patterns.md - Map/Optimize/Execute/Post-process workflow
•references/algorithms.md - VQE, QAOA, Grover, quantum chemistry, ML, optimization
•references/qward-executor.md - QWARD's QuantumCircuitExecutor: simulate, run_ibm, run_qbraid, direct AWS Braket, async job retrieval, noise presets, experiment framework

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

python

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()

# Ideal simulation with automatic QWARD metrics
result = executor.simulate(qc, show_results=True)
print(result["counts"])
print(result["qward_metrics"])

# With noise model
result = executor.simulate(qc, noise_model="depolarizing", noise_level=0.05)

Pattern 2: QWARD Executor - IBM Quantum Hardware (Batch Mode)

python

from qward.algorithms import QuantumCircuitExecutor

executor = QuantumCircuitExecutor(shots=1024)

# One-time setup:
# QuantumCircuitExecutor.configure_ibm_account(token="YOUR_TOKEN")

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

python

from qward.algorithms import QuantumCircuitExecutor

executor = QuantumCircuitExecutor(shots=1024, timeout=300)

# Run on Rigetti Aspen-M3 through qBraid
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)

python

import os
from qiskit_braket_provider import BraketProvider

os.environ['AWS_DEFAULT_REGION'] = 'us-west-1'
provider = BraketProvider()
backend = provider.get_backend("Ankaa-3")

# Remove barriers (AWS incompatible) and submit
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()}")

# Later: retrieve results (big-endian -> little-endian conversion)
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

python

from qward.algorithms import NoiseModelGenerator, get_preset_noise_config

# Use hardware-calibrated presets (IBM Heron R1-R3, Rigetti Ankaa-3)
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)

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')

Pattern 7: Experiment Campaign (Systematic Multi-Config)

python

from qward.algorithms import BaseExperimentRunner

# Subclass for your algorithm, then run campaign across
# multiple circuit configs and noise models:
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 hardware
•Always transpile: Use optimization_level=3 for production
•Use QWARD executor: Unified interface for simulate, IBM QPU, Rigetti
•Use appropriate primitives: Sampler for bitstrings, Estimator for expectation values
•Choose execution mode: Session for iterative (VQE/QAOA), Batch for parallel, qBraid for Rigetti
•Use hardware-calibrated noise: Preset noise models for IBM Heron and Rigetti Ankaa
•Minimize two-qubit gates: Major error source on hardware
•Save job IDs: For later retrieval of hardware results
•Apply error mitigation: Use resilience_level in runtime options
•Run experiment campaigns: Systematic comparison across configs and noise models