For the first time, physicists have performed machine learning on a photonic quantum computer, demonstrating that quantum computers may be able to exponentially speed up the rate at which certain machine learning tasks are performed—in some cases, reducing the time from hundreds of thousands of years to mere seconds. The new method takes advantage of quantum entanglement, in which two or more objects are so strongly related that paradoxical effects often arise since a measurement on one object instantaneously affects the other. Here, quantum entanglement provides a very fast way to classify vectors into one of two categories, a task that is at the core of machine learning.
The physicists, Chao-Yang Lu, Nai-Le Liu, Li Li and colleagues at the University of Science and Technology of China in Hefei, have published a paper on the entanglement-based machine learning method in a recent issue of Physical Review Letters.
As the researchers explain, machine learning has many different uses in everyday life. One example is a spam filter that sorts email into spam and nonspam messages by comparing the incoming email with old email labeled by the user. This is an example of supervised machine learning, as the system is provided with a set of examples. In unsupervised machine learning, the system does not receive prior information. An example of unsupervised machine learning is photo editing software that attempts to classify pixels into two groups: the object and the background.
For both supervised and unsupervised machine learning, the new items to be classified (e.g., emails, pixels, etc.) are represented by vectors. The system assigns each vector to one of two categories by analyzing the vector's length and comparing it to a reference vector in each category. The new vector is assigned to the category containing the most similar reference vector.
Classifying a small number of vectors in this way can be done very quickly. However, as the amount of data in the world rapidly increases, so does the time required for machines to process it. Researchers expect that this "big data" problem could one day pose a challenge even for the fastest supercomputers.