Qiskit

Apache-2.0 license metadata: skill-author: K-Dense Inc.

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
• 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

   from qiskit.primitives import StatevectorSampler
   sampler = StatevectorSampler()
   

2. Always transpile: Optimize circuits before execution

   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

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

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior error resolutions and debugging strategies. The hybrid search excels here — BM25 finds exact error codes/stack traces while vectors find semantically similar past issues.

# Check for prior debugging/diagnostics context before starting
python3 execution/memory_manager.py auto --query "error patterns and debugging solutions for Qiskit"

Storing Results

After completing work, store debugging/diagnostics decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Root cause: memory leak from unclosed DB connections in pool — fixed with context manager" \
  --type error --project <project> \
  --tags qiskit debugging

Multi-Agent Collaboration

Store error resolutions so any agent encountering the same issue retrieves the fix instantly instead of re-debugging.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Debugged and resolved critical issue — root cause documented for future reference" \
  --project <project>

Self-Annealing Loop

When this skill resolves an error, store the fix in memory AND update the relevant directive. The system gets stronger with each resolved issue.

BM25 Exact Match

Error codes, stack traces, and log messages are best found via BM25 keyword search. The hybrid system automatically uses exact matching for these patterns.