IBM Develops Machine Learning Algorithm Fit for Quantum ComputersIBM announced today that it has developed and tested quantum algorithms that have shown the potential to enable practical machine learning applications on future generations of quantum computers.
Machine Learning Accelerated by Quantum Computers
Complex Feature Mapping
According to IBM, both classical and quantum computing algorithms can break down a picture, for example, into its individual pixels and then place those pixels in a grid based on each pixel’s color value. This technique is called “feature mapping,” and the more precise this data can be classified, according to specific characteristics, the better the machine learning system will perform. The goal of matching quantum computers with machine learning is to create more sophisticated feature maps, which can allow the artificial intelligence (AI) system to identify patterns that could be invisible to classical computers.
The feature-mapping algorithms IBM developed for quantum computers was only tested on a simulation of a two-qubit quantum computer, but it still showed that there is a promising path forward for machine learning algorithms that run on quantum computers. IBM believes its machine learning algorithms could soon classify far more complex data sets than any classical computer could handle.
IBM’s algorithms demonstrating how entanglement can improve AI classification accuracy will be available as part of IBM’s Qiskit Aqua, an open-source library of quantum algorithms.