quantum-algorithms-benchmarks

KO | EN

gitlite — search

#python #java #python3 #arduino #golang #machine-learning #rust #html #flask #javascript #seismology #nodejs

quantum-algorithms-benchmarks

★ 15 Open GitHub ↗

No description available.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Quantum Algorithms Benchmarks ($\color{red}{work \ in \ progress}$)

Welcome to this quantum algorithms benchmark repository!
The goal of which is the collection of benchmarks that enable comparability between existing and future quantum algorithms and/or methods-directly provided by the quantum community.

This repository is made of three sub-modules each reflecting an area where quantum comuting has the potential to improve a computational task:
- [Machine Learning](/machine_learning),
- [Optimization](/optimization/), 
- [Monte Carlo Simulation](/monte_carlo_simulation/).

## Vision

The repository tracks the progress of quantum computing algorithms and applications.
It is made of libraries that include community-proposed benchmarking problems and corresponding solutions to these problems.
Solutions to the benchmark problems may be run on any type of quantum hardware or simulator, e.g., superconducting qubits, annealers, or trapped ions.
The progress of quantum computing towards larger problems is tracked by performance metrics defined in each sub-module.
Everybody is welcome to propose benchmark problems that they believe are relevant as well as new solutions to existing benchmarks.

## How to contribute

The proposed solutions to a benchmark problem may either be executed on simulators or hardware.
The platform on which the solution was obtained and the corresponding settings must be clearly indicated.
Refer to the READMEs of the sub-modules for detailed instructions on how to contribute: 
[Machine Learning](/machine_learning), 
[Optimization](/optimization/), and
[Monte Carlo Simulation](/simulation/algorithms/).

quantum-algorithms-benchmarks

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README