Entropica Labs Resources

Training with the Iris dataset on IBMq

Using the polyadic QML Library we trained a qmodel for the ternary classification of the Iris flower dataset on IBM quantum computers. We got the accuracy level of classical ML.

Medium post: News in Quantum Machine Learning

Watch the 15-min video presentation describing the experiment

Explore the training data: https://iris.entropicalabs.io/

The Polyadic QML Library

A Python library to define, train and deploy quantum models

The original ideas behind this library are described in a research paper: Polyadic Quantum Classifier — arXiv:2007.14044


ManyQ quantum computer simulator

A fast quantum computer simulator optimized for Quantum Machine Learning. It uses SIMD, multicore and GPU to parallize and speedup computations

ManyQ is the underlying quantum computer simulator of PolyadicQML


Entropica's QAOA Library

An implementation of the Quantum Approximate Optimization Algorithm

Medium post (2019): Optimising with near-term quantum computers