Quantum computing has rapidly advanced in both theory and practice in recent years, and with it the hope for the potential impact in real applications. One key area of interest is how quantum computers might affect machine learning. CIO Insights brought this interesting information to us in their article, “What is Quantum Machine Learning? Beginner’s Guide to QML.”

When and if quantum computers become more feasible and accessible, they will greatly increase the speed of machine learning processing and open a lot of possibilities for new types of machine learning.

The idea of quantum advantage over a classical computer is often framed in terms of computational complexity classes. Examples such as factoring large numbers and simulating quantum systems are classified as bounded quantum polynomial time problems, which are those thought to be handled more easily by quantum computers than by classical systems. Quantum computing offer great promise, but only time will tell what can be realized in practice. 

Most organizations have little knowledge on how artificial intelligence (AI) systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning are being applied. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.