Use of Quantum Machine Learning in Data Analytics

Jul 22, 2021
4 min read

Introduction

Imagine sitting in front of your computer and finishing off a certain work by staying awake the whole night. The following day, when you wake up and open your tech news feed, you come across an article telling you how a particular new technology can help you do the same thing in half the time that you took to finish it off. You regret staying awake the whole night and not knowing about the new technology and its application in your life. The article you read might not be such a big thing, but it surely made you acquainted with something that can stay with you in the long run. Consider this as one such article which can make your life easier.

Today, our area of focus is quantum machine learning. QML is a new thing for the IT industry but a thing that can change the dimensions of the industry effectively. Let us first try to know what quantum machine learning is.

What is Quantum Machine Learning?

Traditional computers struggle to handle massive computations when data points are projected in greater and higher dimensions. Even if a traditional computer could do it, it would take too long.

Simply said, traditional machine learning techniques might be too demanding for traditional computers at times. Quantum computers, fortunately, have the computing capacity to tackle these difficult algorithms. They solve problems quicker than their classical equivalents by utilising strong principles such as superposition and entanglement.

Scientists can convert a conventional machine learning algorithm into a quantum circuit that can be performed effectively on a quantum computer using quantum machine learning.

So, in simple words, Quantum Machine Learning (Quantum ML) is an interdisciplinary field that brings together quantum physics and machine learning (ML). It's a symbiotic relationship, with quantum computing being used to create quantum versions of ML algorithms and traditional ML algorithms being used to study quantum systems.

Now that we know what quantum machine learning is, let us try to look at its applications.

Applications of Quantum Machine Learning

Quantum machine learning is a very new area with a lot of room for advancement. However, we can already begin to forecast how it will affect our future!

Here are some of the areas where QML will have an impact:

  • Understanding Nanoparticles
  • Molecular and atomic mapping is used to create novel materials
  • Medical research and molecular modeling to identify new medicines
  • Understanding the human body's inner workings
  • Pattern recognition and categorization have been improved
  • Expanding the scope of space exploration
  • By combining IoT and blockchain, we can achieve total linked security.
  • In the analysis of data

QML will address more issues than we could have anticipated as more remarkable discoveries occur every day.

After looking at the applications, we can be assured of one thing, and i.e., if QML can help in such kinds of things in the early stages of its discovery, in a few years it can be the next big thing. In the applications above we came across a point that mentioned “data analysis”. Watching the things that Big Data can do and how fast the data science industry is growing, anyone will be curious about this very application. So let us dive deeper into the ocean of quantum machine learning and search for the desired fish, data analysis. For this, we must start with what data analytics is.

What is Data Analytics?

The phrase "data analytics" refers to the act of analyzing datasets to make conclusions about the information contained within them. Data analysis techniques allow you to take raw data and derive important insights from it by uncovering patterns.

In their research, data scientists and analysts employ data analytics methodologies, and companies use them to influence their judgments. Data analysis may assist businesses in better understanding their consumers, evaluating their advertising efforts, personalizing content, developing content strategies, and developing new goods. Finally, firms may utilize data analytics to improve their bottom line and raise their performance.

Quantum Machine Learning in Data Analytics

While solving real-world problems, quantum machine learning algorithms work with existing datasets and provide a simple and better understanding of them. It makes the work easier for a data scientist who spends hours analyzing data. The rise of QML will benefit the ones in the industry and will also make them shed a lot of load from their already crowded head.

Researchers examine the topic of quantum advantage in machine learning in a recent paper published in Nature Communications titled "Power of data in quantum machine learning." Researchers attempted to quantify how the difficulty of an issue varies formally with the availability of data in the article, and how this might occasionally improve the competitiveness of classical learning models using quantum techniques. The data can raise classical models to rival quantum models, despite the fact that the quantum circuits generating the data are difficult to calculate classically.

To begin, they demonstrated how data-driven classical algorithms might match quantum output. They then established rigorous prediction error limits for training conventional and quantum machine learning algorithms to learn quantum mechanical models using kernel functions. Kernel techniques give verifiable guarantees and are also quite flexible in terms of the functions they can learn, therefore they took these steps.

Computational complexity classes are occasionally used to represent the idea of quantum advantage over a conventional computer. Bounded Quantum Polynomial (BQP) time tasks, such as factoring large numbers and modeling quantum systems, are predicted to be easier for quantum computers to handle than classical systems. Bounded probabilistic polynomial (BPP) problems, on the other hand, are simple to answer on conventional computers.

The researchers working on these showed that learning algorithms with input from a quantum process, such as fusion or chemical processes, generate a new class of problems known as BPP/Samp, which can effectively execute tasks that standard algorithms without data can't. It's a type of problem that can be solved quickly with polynomial-sized advice. This shows that understanding the quantum advantage for different machine learning tasks needs a data analysis as well.

Going deeper into this, we will realize that all the applications of QML are somewhere related to data analytics. From analyzing human behavior to pattern recognition and space exploration, QML uses data analytics to function smoothly and vice versa.

Conclusion

In the near future when many new quantum machine learning algorithms will come up with the exploration of this field, more and more applications of it will show up too. Data analytics with time will get easier and easier for an industry in so much demand.

For the data science experts sitting out there, this was one such article that can make your lives a bit easier, all it will take is a pinch of quantum physics combined with machine learning.