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Quantum ML

What Does Quantum ML Mean?

Quantum ML (quantum machine learning) is an area of research and development (R&D) that focuses on how to translate classical machine learning algorithms into quantum circuits that use qubits instead of binary bits.

An important goal of Quantum ML research is to explore ways that quantum computing can be used to speed up the execution of classical machine learning algorithms. Quantum ML may also be referred to as quantum-enhanced machine learning or quantum-assisted machine learning.


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Techopedia Explains Quantum ML

The use of quantum computing for machine learning and other processes is becoming more familiar to data scientists and others, as quantum computing goes beyond being a nascent and mainly theoretical technology. Now, we are seeing some pilot projects at big tech companies where quantum computing is being experimented with in practical ways. The result may create less theoretical and more practical quantum computing systems.

It's important to note that for a real understanding of quantum ML, individuals have to have a pretty good grasp of what machine learning algorithms do, and also a certain knowledge of how quantum computing works. These are two different knowledge base capabilities that each involve their own separate research and skill and experience building. As a result, it might be difficult to source talent for quantum ML, also because, as mentioned, it is a very new part of the industry. Only the most knowledgeable data scientists in this field can really talk about quantum ML in a practical capacity, although it's possible to imagine all sorts of applications coming our way in the future.

One way to understand the intersection of quantum computing with machine learning is to separate the two processes.

Machine learning involves using algorithms so that a computing system can learn over iterations or train itself. Machine learning involves using test or training data to get the machine started in this process, and the use of subsequent sets of data to help the machine learning program fine-tune its results in a way that experts call “convergence.”

Quantum computing, on the other hand, is a specific area of operational computing where new types of computing devices and systems use the principle of quantum mechanics for advanced computing capability and power.

These quantum computing systems will use something called a qubit. Where the traditional binary bit had two values, either zero or one, a qubit has three possible values, either a zero or a one, or a superposition of those two values, which people commonly think of as a “?”

The result of using this qubit technology as a base makes quantum computers extremely powerful. So the use of machine learning algorithms on quantum computers, talked about as “quantum ML” as mentioned above, would pair those learning systems with immensely capable and powerful computers that would do more with those classification systems. For example, machine learning professionals are now experimenting with what's called deep neural networks, where more sophisticated machine learning models imitate the activity of the human brain. Applying these to quantum computing could have uniquely powerful effects within the field of ML and artificial intelligence.

Some examples can be helpful in further explaining how quantum ML might work.

Suppose your machine learning project is based on using license plate data to track vehicles in order to monitor things like traffic congestion, carbon footprints, or where to install traffic improvements in a community. In that capacity, those machine learning programs would be valuable in improving the sorts of primitive traffic studies used in the past.

Where would quantum computing come in? In this type of example, running the ML model on a quantum computer would allow that computer to do much more operational volume in a given environment, for example, tracking any number of license plates through any number of traffic points and aggregating and refining that data due to the power of the quantum computing model itself. All of this will, understandably, raise the bar on what ML can do, regardless of where that effort is being focused.

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