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By Betony Adams, Francesco Petruccione, and Maria Schuld
Image Attribute: Pixabay.com
Take image recognition. Humans excel at this task, but it’s proved difficult to simulate artificially. Training a machine to recognize a cat doesn’t mean inputting a set definition of what a cat looks like. Instead, many different images of cats are inputted; the aim is that the computer learns to distill the underlying “cat-like” pattern of pixels.
Most of the progress in machine learning so far has been classical: the techniques that machines use to learn to follow the laws of classical physics. The data they learn from has a classical form. The machines on which the algorithms run are also classical.
We work in the emerging field of quantum machine learning, which is exploring whether the branch of physics called quantum mechanics might improve machine learning. Quantum mechanics is different from classical physics on a fundamental level: it deals in probabilities and makes a principle out of uncertainty. Quantum mechanics also expands physics to include interesting phenomena which cannot be explained using classical intuition.
From classical to quantum
Quantum mechanics is a branch of physics that attempts to understand and apply mathematical, verifiable rules to the behavior of nature at the smallest end of the spectrum – on the scale of atoms, electrons, and photons. It was first developed at the beginning of the 20th century and has been very successful in describing systems on the microscopic level.
Quantum mechanics allows the atom to be described as simultaneously decayed or undecayed until a measurement forces it into an exact state. But it then should follow that the cat can be described as both dead and alive at the same time until the box is opened and the state of the cat made certain. The paradox illustrates the difficulty of applying quantum rules to classical objects.
Quantum computers’ value would lie in their ability to process information and perform computational tasks differently, and in some instances more quickly, than classical computers.
Despite the commercial interest, none of the contenders are an outright success yet. That’s because the phenomena they’re drawing from in quantum mechanics, such as superposition states, are delicate and prone to destruction.
Other branches of quantum machine learning focus on how quantum theory might inform the methods that computers use to learn, or the data they learn from, as well as fine-tuning the tools and techniques of classical machine learning in a quantum framework.
Quantum machine learning in Africa
Quantum machine learning is an exciting, rapidly growing field. A number of start-ups have been established that aim to perfect the process and deliver scalable quantum devices.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
About the Authors:
Ph.D. student in Physics, University of KwaZulu-Natal
Professor, University of KwaZulu-Natal
Researcher, University of KwaZulu-Natal
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