How quantum computing will transform analytics

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Google’s recent quantum breakthrough could blow the doors wide open for data science. Here are just some of the ways it could transform our industry.

 

In October 2019, Google announced that after years of theorising, it had finally achieved the milestone of quantum supremacy – carrying out a calculation in just three minutes that would take up to 100,000 years for a conventional computer.

Ask any analytics expert what they’re excited to see in the future and you’re more than likely going to hear the words ‘quantum computing’. We know this for a fact, actually, because we did ask them. So, for the data scientists in our office this is huge news.

To help you understand why, we’ve put together this blog explaining what quantum computing is and what it could help us achieve.


 

What is quantum computing?

Quantum computing signifies the next big step change in IT technology. Put simply, today there are some challenges that are so complex there simply isn’t enough computational power in the world to tackle them. Most of these challenges relate to complex networks, like social networks and protein-protein interaction networks. But with quantum, this is likely to change.

The difference between quantum computing and classical computing isn’t so easy to explain, but here it is in its simplest form:

Traditionally, classical computing processes bits that are either in a 0 state or a 1 state, they cannot be both at the same time. Quantum computing uses something called quibits that can be in both a 0 state and a 1 state (Schrodinger’s bits, anyone?)

A lot of quantum machine learning algorithms, such as Grover’s search algorithm, are based on core concepts of quantum physics, and quantum computing provides the ideal environment to run them. 

You can read a more in-depth explanation in this excellent New York Times article. But the upshot is, this ability to look at things not just in binary states, but using the principle of superposition, allows quantum computers to look at many variables at the same time.

Which means much more data can be stored. Much, much more.


 

So what?

In short, this means we can process far more complex equations. In fact, as Google demonstrated, we should be able to tackle equations in a matter of seconds that would take today’s computers thousands of years to solve.

As the complexity and sheer size of our data sets continue to grow year after year, this ability could be vital in helping us put the world’s data to good use. It could result in breakthroughs in almost every industry. And for our data scientists, who live and breathe data, it’s especially exciting.

Quantum computing will enable the rapid detection, analysis and integration of large, unstructured data sets, and will allow us to quickly uncover patterns that we simply wouldn’t have been able to see before. 

In practice, this single discovery could see us supercharge the work we do today.


 

What will the future of data science look like?

Trying to pinpoint what quantum computing could mean for our future is like someone in the 1930s speculating how the first personal computer might impact the world. There is simply no end to its potential.

However, here are just a handful of examples of how our work could change.

  • Transforming the future of healthcare

I began my data science career working in healthcare, and for me this is one area where quantum computing could have a major impact.

Most notably, it could increase the accuracy and speed of diagnosis, optimise the selection of clinical trial candidates, and make the much talked about personalised healthcare industry a reality – with treatments created to meet the needs of a patient’s specific genetic makeup.

Essentially, it’s likely every area of healthcare – from diagnosis to drug development and treatment – will be much improved, and require less human effort.

  • Exploring the final frontier

It may come as no surprise that NASA has its own laboratory dedicated to quantum computing. After all, it is trying to answer some of the most complex questions there are, and in doing so it collects an enormous amount of data about the universe.  

In fact, most of what we know about our universe now is down to complex mathematical equations and data science – it’s not like we can explore it all ourselves.

With quantum computing NASA scientists can process this data more quickly, answer questions that would previously have been impossible, and gain insights that could propel our species to the next level of understanding.

There is also scope for using quantum computing to get more advanced insights about our own planet, processing data from the thousands of satellites orbiting the earth.

  • Seeing the future (sort of)

Predictive analytics is used in all sorts of industries to help inform business-decisions. However, with our current capabilities there are only so many variables you can calculate before simulations take too long to be useful.

With quantum computing, an almost infinite number of variables can be added to simulations, providing far more specific insights than we’ve seen thus far.

This is especially useful for solving complex optimisation problems – things like figuring out the best way to schedule flights or determining the most efficient delivery routes.

We can do this already, but with quantum capabilities we can make sure every possibility is accounted for.

  • Helping computers to see

In 2017 a sciencemag.org article reported on a breakthrough in quantum technology that saw a computer recognise trees in a series of images.

This may not sound like the most amazing achievement. Or even one that’s all that useful. But as a proof of concept it shows that we’ll soon be able to use computer vision to look at more complex problems – including those that require heavy data crunching.

Again, there are almost endless potential uses for quantum computer vision. One possible application is the ability to track weather patterns and predict major events months in advance. But it could also be used to identify health conditions from photos, direct traffic in the cities of the future, or help the visually challenged navigate their world.

  • Making machines smarter

We already have computers that can make themselves smarter, but once the quantum breakthrough is made, we’re likely to see machine learning capabilities accelerate exponentially – reducing problem-solving times from hundreds of thousands of years to mere seconds.

To put this in context, when IBM’s Deep Blue computer defeated chess champion Garry Kasparov in 1997 it was able to examine a possible 200 million moves a second. With quantum capabilities it will be possible to calculate a trillion moves per second. And that’s a lot of chess.

 

At The Smart Cube, we combine advanced analytics, data science and technology to solve our customers’ most pressing problems, helping them to thrive in today’s competitive environment. Find out more about our analytics solutions. To learn about the latest data science trends and the analytics projects we’re working on, explore more of our blog posts. 

 

Prasad Kothari

Prasad Kothari

Prasad is an analytics and data science leader who has worked extensively building high-performing teams for various organisations and has provided consulting to many fortune 500 clients. At The Smart Cube, he focuses on helping clients realise the value of data science to solve priority business problems, including customer analytics, marketing analytics, RWE/RWD, and supply chain analytics. He has been able to help clients leverage data science to drive innovation, and top-line and bottom-line impact. Prasad has published healthcare data science research papers across leading journals, such as the American Journal of Public Health and American Journal of Psychology, as well as published books in AI. Further, he is part of the editorial board of leading research journals. Prasad has collaborated with several universities in the US and given guest lectures on Quantum Machine Learning, NLP/NLU, topological data analysis and computer vision research. He spends his weekends reading AI books and listening to Indian classical music.

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