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Getting Started

This guide covers installation and basic usage of the Enhanced Quantum Backend Selector.

Installation

Requirements

  • Python 3.10 or higher
  • Qiskit 2.2 or higher

Install from PyPI

pip install enhanced-quantum-backend-selector

Install with Poetry

poetry add enhanced-quantum-backend-selector

Install from GitHub

pip install git+https://github.com/sevetseh28/enhanced_quantum_backend_selector.git#subdirectory=enhanced-quantum-backend-selector

Install from Source (Development)

git clone https://github.com/sevetseh28/enhanced_quantum_backend_selector.git
cd enhanced-quantum-backend-selector/enhanced-quantum-backend-selector
poetry install

Basic Usage

1. Get Available Backends

from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService()
backends = service.backends()

Using Fake Backends for Testing

Generic backends (Qiskit): 

from qiskit.providers.fake_provider import GenericBackendV2

backends = [
    GenericBackendV2(num_qubits=5),
    GenericBackendV2(num_qubits=27),
]

Fake IBM backends (qiskit-ibm-runtime): 

from qiskit_ibm_runtime.fake_provider import FakeManilaV2, FakeSherbrooke

backends = [FakeManilaV2(), FakeSherbrooke()]

2. Create a Circuit

from qiskit import QuantumCircuit

qc = QuantumCircuit(3)
qc.h(0)
qc.cx(0, 1)
qc.cx(1, 2)
qc.measure_all()

3. Select the Best Backend

from enhanced_quantum_backend_selector import BackendSelector

selector = BackendSelector(backends)
rec = selector.select_backend(qc)

print(f"Best backend: {rec.backend.name}")
print(f"Score: {rec.score_data.score:.3f}")
print(f"Expected error: {1 - rec.score_data.score:.2%}")

4. Understanding Scores

  • Score is the estimated success probability (ESP): the chance the circuit runs without error, between 0 and 1. It is the product of (1 - error) over every gate, every used qubit's readout, and the time each qubit spends idle
  • Higher scores are better: 0.99 (1% error) is better than 0.95 (5% error)

The score is calculated by:

  1. Transpiling your circuit to the backend (optimization level 2)
  2. Multiplying (1 - error) for every gate, readout and idle period in the transpiled circuit
  3. Averaging over multiple transpilation runs (default: 3)

Complete Example

from qiskit import QuantumCircuit
from qiskit.providers.fake_provider import GenericBackendV2
from enhanced_quantum_backend_selector import BackendSelector

# Setup backends
backends = [
    GenericBackendV2(num_qubits=5, seed=42),
    GenericBackendV2(num_qubits=7, seed=43),
    GenericBackendV2(num_qubits=27, seed=44),
]

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

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

print(f"✓ Selected: {rec.backend.name}")
print(f"  Score: {rec.score_data.score:.3f}")
print(f"  Expected error: {1 - rec.score_data.score:.2%}")

Troubleshooting

ModuleNotFoundError: No module named 'qiskit_ibm_runtime'

pip install qiskit-ibm-runtime

Authentication Error

from qiskit_ibm_runtime import QiskitRuntimeService

QiskitRuntimeService.save_account(
    channel="ibm_quantum",
    token="YOUR_API_TOKEN",
    overwrite=True
)

No Compatible Backends Found

# Get all rankings to see what's available
rankings = selector.rank_backends(qc)
for rec in rankings:
    print(f"{rec.backend.name}: {rec.score_data.score:.3f}")

Next Steps