Understanding Quantum Computing for Machine Learning

by June 22, 2020
Quantum Computing

Image Credit: Singularity Hub

Innovation in machine learning is far from complete, much is still left to conquer, and Quantum Computing aims to achieve just that.

Technology, in fact, is just about to take a quantum leap for the good. Excitement brews when the subfields of machine learning and quantum physics come together to solve the problems of intelligent computing. This amalgamation between machine learning and quantum computing has caught the enterprise’s attention for good with the likes of NASA, IBM, and Microsoft getting on board with this fascinating new technology.


Quantum Computing Concepts

To understand in easy parlance, quantum machine learning is an interdisciplinary approach that combines machine learning with the principles of quantum physics. Using the principles of superposition and entanglement, devices with quantum power pack an incredible amount of computational power. To solve complex problems, quantum chips can be immensely helpful to map out phenomenal computer algorithms.

As a data science expert, if you are already in awe of hardware such as ASICs (application-specific integrated circuits) and FPGAs (field-programmable gate arrays) to facilitate machine learning, prepare to experience more computational speed with quantum machine learning.

Credit to its phenomenal computational power, quantum machine learning can accelerate a number of machine learning processes. These include–


Deep Learning

Deep learning is one of the most impactful applications of machine learning and artificial intelligence in recent times. Quantum computers hold immense potential to make deep learning a whole lot more profound by solving complex problems that are intractable on classical computers.



Be it, physicists, chemists, or data scientists, everyone is trying to find a way to the point of lowest energy in a high-dimensional energy landscape. In the world of adiabatic quantum computing and quantum annealing, optimization is everyone’s priority.


Kernel Evaluation

While the training and inferencing of the model will have to be done in the standard support vector machine, special-purpose quantum support vector machines could help accelerate the process at an enormous pace. This is where quantum algorithms step in. Quantum properties like entanglement and interference help to create a massive quantum state space that can hugely improve kernel evaluation.


Linear Algebra

When it comes to executing linear algebra computations, quantum computers can exponentially speed up the prospects. A quantum gate can execute an exponentially large matrix with an equally large vector at advanced speed in a single operation, helping build machine learning models out of quantum algorithms. This significantly brings down the costs as well as times associated with linear algebra computations.

There is a lot to achieve in the field of Quantum machine learning, an exciting beginning has been made. There is still more to come in this promising space.