Application-Oriented Performance Benchmarks for Quantum Computing

This repository contains a collection of prototypical application- or algorithm-centric benchmark programs designed for characterizing the end-user perception of the performance of current-generation Quantum Computers.

The repository is maintained by members of the Quantum Economic Development Consortium (QED-C) Technical Advisory Committee on Standards and Performance Metrics (Standards TAC).

Important Note: The examples maintained in this repository are not intended to be viewed as "performance standards". Rather, they are offered as simple "prototypes", designed to make it as easy as possible for users to execute simple "reference applications" across multiple quantum computing APIs and platforms.

Quick Start

Install and run a standard set of benchmarks on a local simulator:

pip install qedcbench
python -m qedcbench.run_all

This runs 5 benchmarks (Hidden Shift, Bernstein-Vazirani, QFT, Phase Estimation, Amplitude Estimation) from 2 to 8 qubits and displays a combined volumetric positioning plot at the end.

Customize the run

python -m qedcbench.run_all -b ibm_sherbrooke -max 6 -s 100     # IBM hardware
python -m qedcbench.run_all -b ionq -max 6                       # IonQ (requires QISKIT_IONQ_API_TOKEN)
python -m qedcbench.run_all -b iqm -max 6                        # IQM (requires IQM_API_TOKEN)
python -m qedcbench.run_all --list                                # show all available benchmarks
python -m qedcbench.run_all --benchmarks hidden_shift,grovers     # run specific benchmarks

Common arguments:

Argument	Short	Description	Default
--api	-a	Quantum SDK (qiskit, cudaq)	qiskit
--backend_id	-b	Backend name	qasm_simulator
--min_qubits	-min	Minimum circuit width	2
--max_qubits	-max	Maximum circuit width	8
--max_circuits	-c	Circuits per qubit group	3
--num_shots	-s	Shots per circuit	100

Run individual benchmarks

After installing, you can also run benchmarks individually:

cd qedcbench/hidden_shift
python hs_benchmark.py --api qiskit --min_qubits 2 --max_qubits 8

Cloning the Repository

For full access to source code, notebooks, and the ability to modify benchmarks:

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

This installs both packages in editable mode — changes to .py files take effect immediately. The repository includes Jupyter notebooks for interactive exploration:

cd qedcbench
jupyter notebook benchmarks-qiskit.ipynb

Note: If you have existing code that depends on the v1.x repository structure, use branch master-260411-v1.2.2 for compatibility. See the User Guide for migration details.

Documentation

Full Documentation Site — User guide, benchmark descriptions, qedclib API reference, and setup guides.

Document	Description
User Guide	Complete reference for running benchmarks
Benchmarks	All 17 benchmarks with algorithm descriptions
qedclib Guide	Execution engine API, metrics, and backend configuration
Quick Start	First-time setup walkthrough
Release Notes	Version history and changes
PAL	Problems, Anomalies, and Limitations
About	Project background, structure, and credits
Setup Guides	Platform-specific installation (Qiskit, CUDA-Q, etc.)

Standalone execution engine: pip install qedclib — use the execution and metrics library in your own projects without the benchmarks. See qedclib on PyPI.

Benchmark Complexity Levels

Level 1: Deutsch-Jozsa, Bernstein-Vazirani, Hidden Shift
Level 2: Quantum Fourier Transform, Grover's Search
Level 3: Phase Estimation, Amplitude Estimation, HHL Linear Solver
Level 4: Monte Carlo, Hamiltonian Simulation, HamLib, VQE, Shor's Algorithm
Level 5: MaxCut, Hydrogen Lattice, Image Recognition

Publications

    Application-Oriented Performance Benchmarks for Quantum Computing (Oct 2021)

    Optimization Applications as Quantum Performance Benchmarks (Feb 2023)

    Quantum Algorithm Exploration using Application-Oriented Performance Benchmarks (Feb 2024)

    A Comprehensive Cross-Model Framework for Benchmarking the Performance of Quantum Hamiltonian Simulations (Sep 2024)

    A Practical Framework for Assessing the Performance of Observable Estimation in Quantum Simulation (Apr 2025)

    Platform-Agnostic Modular Architecture for Quantum Benchmarking (2025)

Implementation Status

© 2025 Quantum Economic Development Consortium (QED-C). All Rights Reserved.