qedclib 2.0.1

Quantum program execution engine with built-in performance monitoring

qedclib

A quantum program execution engine with built-in performance monitoring. Execute quantum circuits at scale across multiple backends with automatic metrics collection, parallel GPU execution, and detailed per-circuit timing.

Install

pip install qedclib

Quick Example

import qedclib

# Select the quantum computing API
qedclib.set_api("qiskit")

# Initialize and get the execution module
qedclib.initialize()
import execute as ex

# Execute circuits and collect metrics
circuits = [{"circuit": qc, "num_qubits": n, "secret_int": s} for qc, n, s in my_circuits]
ex.submit_circuits(circuits, num_shots=1000, backend_id="qasm_simulator")

# Plot collected metrics
qedclib.metrics.plot_metrics("My Benchmark")

Features

• Multi-backend support — Qiskit (Aer, IBM hardware, IonQ, IQM) and CUDA-Q (simulator, multi-GPU, NVIDIA Quantum Cloud)
• Automatic metrics — execution time, fidelity, circuit depth, gate counts, and volumetric benchmarking plots
• Per-circuit timing — accurate individual circuit timing on all backends
• Batched execution — memory-efficient execution for large parameter sweeps on GPU backends
• Multi-GPU parallel execution — distribute circuits across GPUs with MPI
• Robust job management — automatic retry, status polling, and result validation for hardware backends

Documentation

Full documentation is available at sri-international.github.io/QC-App-Oriented-Benchmarks, including the User Guide with standalone library usage examples.

Part of the QED-C Benchmarks

qedclib is the execution engine behind the QED-C Application-Oriented Benchmarks suite. If you want the full benchmark suite (17 benchmarks, notebooks, and examples), clone the repository instead:

git clone https://github.com/SRI-International/QC-App-Oriented-Benchmarks.git
cd QC-App-Oriented-Benchmarks
pip install -e .

License

Apache 2.0