Quantum computing & data science will be a profitable duo

(AnalyticsInsight) Nasreen Parvez discusses the applications of quantum computing in Data Science and Machine Learning fields by using AI tools and Algorithms. Quantum computing offers much easier handling and computing of a huge volume of data. Inside Quantum Technology summarizes here.

In the scientific literature, there is widespread agreement that quantum computers will aid in the solving of previously impossible issues, particularly in the disciplines of data science and artificial intelligence. However, no flawless quantum computers are currently accessible. Noisy Intermediate-Scale Quantum (NISQ) is the name given to the present generation. These computers have a restricted amount of bits and are susceptible to interference as well as noise. IBM and QuEra Computing were among the first businesses to construct quantum computers with more than 100 qubits in 2021.
But what is this generation’s practical benefit? This can be understood in a practical test, in which the authors used the Qiskit and PennyLane frameworks to construct three use cases and confirmed their practical usefulness. In comparison to competitors such as Google’s Cirq and Microsoft’s Q#, IBM’s Qiskit framework has excellent documentation and has the added benefit of being able to execute circuits on a genuine quantum computer for free.
A true data scientist will most certainly work with much more data, yet in general, people are incapable of analyzing massive databases on their own. To assess these big datasets, ML algorithms are deployed. So every time fresh data is given, they figure out how to interpret the changes and look for patterns in the data. As a data scientist continue to add additional data, the amount of time it takes to analyze and compute grows.
The processing capacity of traditional computers limits the computational capability of machine learning algorithms today. Quantum computing can process enormous data sets at much quicker speeds and feed data to AI technologies, which can analyze data at a finer level to find patterns and abnormalities. One advantage of using quantum computers is that we can do more advanced analysis and construct machine learning models. It also makes it so much easier to use more data, allowing data scientists to have a better understanding of the data they are working with.