Quantum Machine Learning: What It Is, How It Works, and More

Quantum machine learning uses the power of quantum mechanics and quantum computing to speed up and enhance machine learning on the “classical” computers we use daily. Quantum computers use the often counterintuitive laws of quantum physics and can store and process exponentially more information than tablets, smartphones, and supercomputers that power much of the modern world. 

But quantum computers have a long way to go before being used daily. According to McKinsey and Company, they likely won’t be operational until 2030, and even then, they’ll be in small numbers [1]. Nonetheless, several companies have already begun offering quantum devices accessible through the cloud, creating the opportunity for hybrid work that pairs quantum computing with classical machine learning. 

Discover more about quantum machine learning and quantum computers and what they can do in the future. Explore the cloud-based quantum computing tools available today and some courses to help you get started on your own machine-learning journey. 

What is quantum machine learning? 

Quantum machine learning uses algorithms run on quantum devices, such as quantum computers, to supplement, expedite, or support the work performed by a classical machine learning program. Also called quantum-enhanced machine learning, quantum machine learning leverages the information processing power of quantum technologies to enhance and speed up the work performed by a machine learning model.

While limited storage and processing capacities constrain classical computers, quantum-enabled ones exponentially increase storage and processing power. This ability to store and process huge amounts of information means that quantum computers can analyse massive data sets that would take classical methods significantly longer to perform. As a result, quantum machine learning leverages this out-sized processing power to expedite and improve the development of machine learning models, neural networks, and other forms of artificial intelligence (AI).

Quantum computing explained

Quantum computers use quantum mechanics to produce processing power that far outperforms even the most innovative supercomputers used today. While classical computers operate on the classical laws of physics and store information using binary bits (1s or 0s), quantum computers leverage the often confounding laws of quantum physics to store information on subatomic particles called quantum bits, or qubits, which can hold more data than their classical counterparts and can be used for more complex computations. 

That’s not to say that quantum computers will replace your laptop or tablet any time soon—or even at all. In the future, classical and quantum-enabled computers will be more likely to work side by side because each is better suited to different tasks. Furthermore, quantum computers are costly to build and maintain. They are also susceptible to decoherence, or the decaying of a qubit’s (a unit of information) quantum state, from such common environmental factors as temperature fluctuations and physical vibrations. 

Quantum computing can potentially improve computational processing power and turbocharge technological innovation vastly. However, there’s still work to do to make it reliable, cost-effective, and broadly applicable to our everyday lives. 

Quantum machine learning uses 

From crunching massive amounts of big data to powering transformative technological advances, quantum computing, and machine learning stand to make waves in the future. While quantum machine learning is still in its infancy, researchers and professionals use it in numerous ways. Some of these applications include: 

• Developing new machine learning algorithms.

• Speeding up existing machine learning algorithms.

• Employing quantum-enhanced reinforcement learning, a machine-learning algorithm learns based on interactions within a quantum environment.

• Creating quantum neural networks can operate in fewer steps and with greater processing speed than traditional neural networks.

Despite these intriguing applications, quantum computing and machine learning are still growing and changing. As a result, many other applications used to solve real-world problems are likely to be developed in the near and distant future. 

Get started: Cloud-based quantum machine learning tools 

Most quantum computers are large, finicky, and expensive. But that doesn’t mean you can’t start experimenting with them today. 

You will likely access a quantum computer through the cloud, which can connect you to a quantum-enabled device via the internet. If you want to start exploring this exciting new form of computing yourself, consider some of the cloud-based quantum computing platforms: 

• IBM Quantum

• Amazon Braket

• Microsoft Azure Quantum

• Google Quantum AI

Take the leap with Coursera 

Both quantum computing and machine learning have made impressive strides in the last few years—and they’re set to go even further in the future. Prepare for a future career in quantum machine learning with a flexible and cost-effective specialisation on Coursera. 

In the Stanford and DeepLearning.AI Machine Learning Specialisation, you’ll have the opportunity to learn fundamental AI concepts and develop practical machine learning skills in a three-course, beginner-friendly programme developed by AI visionary Andrew Ng. Best of all, you can learn that in as little as two months.

In DeepLearning.AI’s Deep Learning Specialisation, you’ll build and train neural network architectures, such as convolutional neural networks and recurrent neural networks, and learn how to make them better with strategies such as Dropout, BatchNorm, and more. Master the fundamentals of deep learning and break into AI in as little as three months.