Thank you for following my venture in Quantum ML. The Quantum era is now and this is a collection of my QML (Quantum Machine Learning) and my learning paths, including some of my current Doctoral research in Quantum Finance. I'll continue to address post-Doctoral research too. In this repo I learn to solve Quantum Katas Online with Jupyter Notebooks in Q#, analyse the Titanic dataset from PyQML, solve real world problems using Cirq, Qiskit, PennyLane and other open source Quantum ML libraries available on Github. Enjoy!
A big thank you go towards the team at qiskit.org for providing their course that takes you through key concepts in quantum machine learning, such as parameterized quantum circuits, training these circuits, and applying them to basic problems. By the end of the course, you'll understand the state of the field, and you'll be familiar with recent developments in both supervised and unsupervised learning such as quantum kernels and general adversarial networks. This course finishes with a project that you can use to showcase what you've learnt.
Quantum hardware platforms are available from Google, Rigetti, Microsoft, and IBM. They implement quantum computational models to perform quantum computations. The approaches used for building the quantum computer are trapped ions, photonics systems, and superconducting quantum bits. Quantum bits are the current limitation and the differentiator between these platforms.