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Description

KEEL (Knowledge Extraction based on Evolutionary Learning) is a Java-based open-source software tool licensed under GPLv3 that facilitates a diverse array of knowledge data discovery tasks. Featuring an intuitive graphical user interface that emphasizes data flow, KEEL enables users to design experiments incorporating various datasets and computational intelligence algorithms, with a particular focus on evolutionary algorithms, to evaluate their effectiveness. The software encompasses an extensive range of traditional knowledge extraction techniques, data preprocessing methods—including training set selection, feature selection, discretization, and imputation for missing values—as well as computational intelligence learning algorithms, hybrid models, and statistical methods for experiment comparison. This comprehensive suite allows researchers to conduct thorough analyses of innovative computational intelligence approaches in relation to established methods. Furthermore, KEEL has been specifically crafted to serve dual purposes: advancing research and enhancing educational outcomes in the field. Its versatility makes it an invaluable resource for both academic and practical applications in knowledge discovery.

Description

The development of large-scale physical quantum computers is proving to be a formidable task, and in parallel with efforts to create these machines, considerable attention is being directed towards crafting effective quantum algorithms. Without a fully realized large quantum computer, it becomes essential to utilize precise software simulations on classical systems to replicate the execution of these quantum algorithms, allowing researchers to analyze quantum computer behavior and refine their designs. In addition to simulating ideal, error-free quantum circuits on a faultless quantum computer, the QX simulator offers the capability to model realistic noisy executions by incorporating various error models, such as depolarizing noise. Users have the option to activate specific error models and set a physical error probability tailored to mimic a particular target quantum computer. This defined error rate can be based on factors like gate fidelity and qubit decoherence characteristics of the intended platform, ultimately aiding in the realistic assessment of quantum computation capabilities. Thus, these simulations not only inform the design of future quantum computers but also enhance our understanding of the complexities involved in quantum processing.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Keel

Website

www.keel.es/

Vendor Details

Company Name

Quantum Computing Simulation

Website

quantum-studio.net

Product Features

Data Mining

Data Extraction
Data Visualization
Fraud Detection
Linked Data Management
Machine Learning
Predictive Modeling
Semantic Search
Statistical Analysis
Text Mining

Product Features

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