enhanced-quantum-backend-selector 0.1.0

Intelligent quantum backend selection for Qiskit circuits

Enhanced Quantum Backend Selector

Intelligent quantum backend selection for Qiskit circuits using transpilation-based error analysis.

Overview

Quantum computing users typically rely on simplistic backend selection methods like least_busy(), which only considers queue depth. This library provides intelligent backend selection by transpiling your circuit to each backend and calculating the actual expected error rates:

• Transpilation-Based: Circuits are transpiled to each backend's native gate set
• Real Error Calculation: Sums actual error rates for the specific qubits and gates used
• Multiple Runs: Averages over multiple transpilation runs for stability (default: 3, configurable)
• Direct Quality Metric: Score = negative total error (higher is better)

Installation

pip install enhanced-quantum-backend-selector

Or with Poetry:

poetry add enhanced-quantum-backend-selector

Quick Start

from enhanced_quantum_backend_selector import BackendSelector
from qiskit import QuantumCircuit
from qiskit_ibm_runtime import QiskitRuntimeService

# Get available backends
service = QiskitRuntimeService()
backends = service.backends()

# Create your circuit
qc = QuantumCircuit(3)
qc.h(0)
qc.cx(0, 1)
qc.cx(1, 2)
qc.measure_all()

# Select the best backend
selector = BackendSelector(backends)
best = selector.select_backend(qc)

print(f"Recommended: {best.backend.name}")
print(f"Score: {best.score_data.score:.3f}")  # Negative error sum
print(f"Expected error: {-best.score_data.score:.2%}")  # As percentage
print(f"Reasons: {', '.join(best.score_data.reasons)}")

Features

• ✅ Transpilation-Based Scoring: Calculates actual error rates for your specific circuit
• ✅ Fractional Gates Support: Automatically evaluates with/without fractional gates for chemistry circuits
• ✅ Provider Agnostic: Works with any Qiskit BackendV2 provider (IBM, Aer, etc.)
• ✅ Multiple Run Averaging: Configurable transpilation runs (default: 3) for stable scoring
• ✅ Smart Caching: 1-hour TTL cache minimizes backend queries
• ✅ Parallel Processing: Scores multiple backends concurrently (up to 10 workers)
• ✅ Comprehensive Type Hints: Full mypy strict mode compliance
• ✅ Extensive Documentation: Detailed docstrings with examples

Rank All Backends

# Get full ranking
rankings = selector.rank_backends(qc, top_n=5)

for rec in rankings:
    error_pct = -rec.score_data.score * 100
    print(f"#{rec.rank}. {rec.backend.name}: {rec.score_data.score:.4f} ({error_pct:.2f}% error)")

Development

See CONTRIBUTING.md for development setup and guidelines.

# Clone repository
git clone https://github.com/sevetseh28/enhanced_quantum_backend_selector.git
cd enhanced-quantum-backend-selector/enhanced-quantum-backend-selector

# Install with development dependencies
poetry install --with dev,docs

# Run tests
poetry run pytest

# Run type checking
poetry run mypy src/

# Format code
poetry run ruff format .

Benchmarking

The library includes a comprehensive benchmarking suite with real-world quantum circuits to evaluate backend selector performance using mock backends that simulate real IBM Quantum processors (Heron R1, Heron R2, Heron R3, and Nighthawk):

# Run full benchmark suite
poetry run python benchmarks/run_benchmarks.py

# Run example benchmarks
poetry run python examples/run_benchmarks.py

# Run benchmark tests
poetry run pytest tests/test_benchmarks.py -v

Benchmark Circuits:

• GHZ: Multi-qubit entanglement testing
• Bernstein-Vazirani: Parallel operations and phase kickback
• Mirror_5k: Device benchmarking (circuit inversion)
• LUCJ Ansatz: Quantum chemistry applications
• QAOA: Combinatorial optimization

Mock Backends (based on real IBM processors):

• IBM Heron R1: 133 qubits, heavy-hex topology, 1,800 gate capacity
• IBM Heron R2: 156 qubits, heavy-hex topology, 5,000 gate capacity
• IBM Heron R3: 156 qubits, heavy-hex topology, improved error rates
• IBM Nighthawk: 120 qubits, square lattice, superior connectivity

See Benchmarking Guide for detailed methodology, metrics, and references.

Documentation

• User Guide
• API Reference
• Benchmarking Guide
• Examples
• Contributing Guidelines
• Release Process
• Changelog

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Citation

If you use this library in your research, please cite:

@software{enhanced_quantum_backend_selector,
  title = {Enhanced Quantum Backend Selector},
  author = {sevetseh28},
  year = {2025},
  url = {https://github.com/sevetseh28/enhanced_quantum_backend_selector}
}

Contributing

Contributions are welcome! Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.