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 the machine learning done on the “classical” computers we use every day. Quantum computers are designed using the often counter-intuitive laws of quantum physics and can store and process exponentially more information than the tablets, smartphones, and supercomputers that power much of the modern world. 

But quantum computers have a long way to go before being used in our everyday lives. According to McKinsey and Company, only about 2,000 to 5,000 quantum computers will likely be operational by 2030, and those capable of handling the most complex problems may not even exist until 2035 or later [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. 

In this article, you’ll learn more about quantum machine learning, quantum computers, and what they might be able to do in the future. At the end, you’ll also learn about 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 classical computers are constrained by limited storage and processing capacities, quantum-enabled ones allow for exponentially more storage and processing power. This ability to store and process huge amounts of information means that quantum computers can analyze 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).

Read more: Deep Learning vs. Machine Learning: Beginner’s Guide

Quantum computing explained

Quantum computers use quantum mechanics to produce processing power that far outperforms even the most cutting-edge 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 sub-atomic particles called quantum bits, or qubits, that can hold more data than their classical counter parts and 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 computers and quantum-enabled ones are more likely to work side by side due to the fact that each is better suited to different types of tasks. Furthermore, quantum computers are costly to build and maintain, and are susceptible to decoherence, or the decaying of a qubit’s quantum state from such common environmental factors as temperature fluctuations and physical vibrations. 

Quantum computing, then, has the potential to vastly improve computational processing power and turbocharge technological innovation. 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, both quantum computing and machine learning stand to make waves in the future. While quantum machine learning is still in its infancy, researchers and professionals are already using it in numerous ways. Some of these applications include to: 

• Develop new machine learning algorithms

• Speed up already existing machine learning algorithms 

• Employ quantum-enhanced reinforcement learning, in which a machine learning algorithm learns based on its interactions within a quantum environment 

• Create quantum neural networks, which can operate at fewer steps and with greater processing speed than traditional neural networks.

Despite these intriguing applications, though, the field of quantum computing and machine learning is still growing and changing. As a result, many other applications used to solve real-world problems will likely develop 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 playing around with them today. 

The primary way you will likely access a quantum computer is 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, here are some of the cloud-based quantum computing platforms you should consider: 

• IBM Quantum

• Amazon Braket

• Microsoft Azure Quantum

• Google Quantum AI

Read more: Machine Learning Skills: Your Guide to Getting Started

Take the leap with Coursera 

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

In Stanford and Deeplearning.AI’s Machine Learning Specialization, you’ll learn fundamental AI concepts and develop practical machine learning skills in a three-course, beginner-friendly program developed by AI visionary Andrew Ng. Best of all, you can learn all of that in as little as two months.

In Deeplearning.AI’s Deep Learning Specialization, meanwhile, 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 like Dropout, BatchNorm, and more. Master the fundamentals of deep learning and break into AI in as little as three months.