Quantum Machine Learning and the Deep Intelligence Frameworks – The 10 Key Properties
Dr. Amit Ray, Compassionate AI Lab
In this article, we discussed the 10 properties and characteristics of hybrid classical-quantum machine learning approaches for our Compassionate AI Lab projects. Quantum computers with the power of machine learning will disrupt every industry. They will change the way we live in this world and the way we fight diseases, care for old people and blind people, invent new medicines and new materials, and solve health, climate and social issues. Similar to the 10 V’s of big data we have identified 10 M’s of quantum machine learning (QML). These 10 properties of quantum machine learning can be argued, debated and fine tuned for further refinements.
The field of hybrid classical-quantum machine learning has been maturing rapidly with the availability of the prototype universal gate-model quantum processors of IBM, Google, and Rigetti as well as more sophisticated quantum annealers of D-Wave systems. Moreover, the availability of various high-performance computing (HPC) simulation of quantum circuits facilities improving the possibilities of exploring the power of QML in various application areas. Quantum hardware dedicated to machine learning are also becoming reality. They too can provide much faster processing power than a general-purpose quantum computer.
The compassionate AI lab is currently developing a hybrid classical-quantum machine learning (HQML) framework – a quantum computing virtual plugin to build a bridge between the available quantum computing facilities with the classical machine learning software like Tensor flow, Scikit-learn, Keras, XGBoost, LightGBM, and cuDNN.
Presently the hybrid classical-quantum machine learning (HQML) framework includes the quantum learning algorithms like: Quantum Neural Networks, Quantum Boltzmann Machine, Quantum Principal Component Analysis, Quantum k-means algorithm, Quantum k-medians algorithm, Quantum Bayesian Networks and Quantum Support Vector Machines.