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Quantum Computing vs Engineering Analytics

October 15, 2021

Quantum Computing vs Engineering Analytics

When it comes to solving complex problems, engineering analytics and quantum computing are two powerful tools that are often compared. Both have their own strengths and weaknesses, and in this post, we’ll compare the two, with facts, figures and even a few jokes thrown in for good measure.

What is Engineering Analytics?

Engineering Analytics involves the use of data, mathematical models and algorithms to analyse and solve complex engineering problems. It leverages statistics, machine learning, and optimization techniques to make predictions, identify patterns, and optimise designs.

What is Quantum Computing?

Quantum computing is a revolutionizing technology that exploits quantum phenomena to perform computations that are impossible to achieve with classical computing. It enables the simulation of complex, multi-particle interactions that traditional computers find impossible to solve in a reasonable time.

Comparison

When it comes to comparison, the main difference between engineering analytics and quantum computing is the type of problems each can solve. Engineering analytics is efficient in solving problems that involve big data sets — huge amounts of data that need to be processed to gain insights. But, its limitation lies in its ability to solve complex problems that involve non-linear interactions between multiple parameters.

Quantum computing, on the other hand, is highly efficient in solving complex optimization problems and quantum simulations. However, it requires high precision and is only effective when it has a limited number of parameters to play with.

Despite their limitations, both have been used widely in industry and research to achieve impressive results. Here are a couple of examples:

  • Engineering Analytics: GE used engineering analytics to optimize the performance of its wind turbines. Using predictive analytics, GE was able to reduce the downtime by 20% and increase the annual energy production of the turbines by 5%.

  • Quantum Computing: Volkswagen and Google collaborated to use quantum computing to optimize traffic flow in a city. The simulations showed that with the right combination of quantum algorithms, large cities with millions of people could solve traffic issues up to ten times faster than using classical algorithms.

Conclusion

In conclusion, engineering analytics and quantum computing are two powerful tools that have their own strengths and limitations. While engineering analytics is good for large data sets, quantum computing is more effective in solving complex optimization problems and quantum simulations.

Understanding the strengths and limitations of each tool can help organizations choose the best tool for the job. Or, like a well-dressed executive would say, "Let’s not reinnovate the wheel - let’s reinvent the road ahead."

References

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