Project QCweb

A Web Application for Training Quantum Classifiers via Evolutionary Algorithms

by Giovanni Acampora, Carlos Cano Gutiérrez, Angela Chiatto, José Manuel Soto-Hidalgo and Autilia Vitiello

Variational Quantum Classifiers (VQCs) stand out as one of the most popular classification approaches within the Quantum Machine Learning (QML) community. They serve as an accessible entry point for newcomers to QML world, particularly those with a background in classical machine learning, due to their workflow similarity to conventional neural networks. In essence, VQCs boast a layered structure conducive to training via a classical optimizer. Recently, Evolutionary Algorithms (EAs) have emerged as a suitable methodology for this training goal. However, current quantum tools provide implementations for various gradient-based optimizers and just one EA tailored for VQC training. Moreover, it is important to note that access to this toolset remains largely confined to expert users. This highlights the necessity for platforms that facilitate the utilization of EAs for VQC training by non-experts in quantum or evolutionary computing. Here, you can use this web application properly designed to implement quantum classifiers to be trained on your data by means of a set of EAs. Discover the capabilities of the proposed web application by uploading your own dataset!

Accepted and presented at the IEEE World Congress on Computational Intelligence 2024 held in Yokohama (Japan)

Due to some security restrictions of our institution, it is no longer possible to run the application online.
However, the QCweb application has been encapsulated in a docker image that you can download here and run by following the instructions reported in this file.