a

Lorem ipsum dolor sit amet, consectetur adicing elit ut ullamcorper. leo, eget euismod orci. Cum sociis natoque penati bus et magnis dis.Proin gravida nibh vel velit auctor aliquet. Leo, eget euismod orci. Cum sociis natoque penati bus et magnis dis.Proin gravida nibh vel velit auctor aliquet.

  /  Project   /  Blog: How Quantum Computers and Machine Learning Will Revolutionize Big Data

Blog: How Quantum Computers and Machine Learning Will Revolutionize Big Data


In this article, I clarify how quantum computing, machine learning, deep learning, and AI can revolutionize the future data science. With the exciting new features of quantum machine learning business leaders across the world want to capitalize the power of quantum computing very fast.

Quantum Machine Learning

Currently the top leaders in quantum machine learning technology are Dr. Amit Ray of Compassionate AI Lab, Dr. Maria Schuld of Xanadu, D-Wave Systems Inc, Canada, and NASA Quantum Artificial Intelligence Laboratory.

The big advantage of quantum computing is that it allows an exponential increase in the number of dimensions it can process. While a classical perceptron can process an input of N dimensions, a quantum perceptron can process 2^N dimensions.

Today’s quantum computers can, in principle, be used to learn from data — by mapping data into the space in which only quantum states exist. The development of quantum circuits that can solve a problems that are unsolvable using any equivalent classical circuit. The main advantage of quantum computing is it can execute any task very faster when compared to the classical computer. In quantum computing qubit is the conventional superposition state and so there is an advantage of exponential speedup which is resulted by handle number of calculations.

However, we need more that 1000 qubits of extremely high quality qubits with low error rates and long coherence time for this to work.

Machine learning is a set of algorithms that train on a data set to make predictions or take actions in order to optimize some systems. For instance, supervised classification algorithms are used to classify potential clients into good or bad prospects, for loan purposes, based on historical data. The techniques involved, for a given task (e.g. supervised clustering), are varied: naive Bayes, SVM, neural nets, ensembles, association rules, decision trees, logistic regression, or a combination of many.

The ML algorithms that attempt to translate classical machine learning to the quantum are Quantum Bayesian Nets, and Quantum Boltzmann Machines, which naturally generalize fairly easily to the quantum realm.

Machine Learning and Big Data

The search for automated approaches in computer science is not new. Machine Learning is the most in-demand technology in today’s world market. Its applications range from self-driving cars to predicting deadly diseases.

In my case, over the last 10 years, I specialized in machine-to-machine and device-to-device communications, developing systems to automatically process large data sets, to perform automated transactions.

Finding hidden patterns and extracting key insights from data is the most essential part of Machine Learning. By building predictive models and using statistical techniques, Machine Learning allows you to dig beneath the surface and explore the data at a minute scale. Understanding data and extracting patterns manually will take days, whereas Machine Learning algorithms can perform such computations in less than a second.

Due to excessive production of data, we need a method that can be used to structure, analyze and draw useful insights from data. Amazon Machine Learning services, Azure Machine Learning, Google Cloud AI, and IBM Watson are four leading cloud ML services that allow for fast model training and deployment.

As in any scientific discipline, data scientists may borrow techniques from related disciplines, though we have developed our own arsenal, especially techniques and algorithms to handle very large unstructured data sets in automated ways, even without human interactions, to perform transactions in real-time or to make predictions.

Nature of Big Data

Most of the organizations in today’s world are dealing with huge amount of data. The big nature of data raises several issues. Big data is in the form of Structured, un-structured, massive homogeneous and heterogeneous data. In 2014, Data Science Central, Kirk Born has defined big data in 10 V’s i.e. Volume, Variety, Velocity, Veracity, Validity, Value, Variability, Venue, Vocabulary, and Vagueness.

IoT and Big Data

Though IoT and Big data evolved independently, they have become interrelated over the period. Internet of Things (IoT) refers to a system of connected physical objects via the internet. The ‘thing’ in IoT can refer to a person or any device which is assigned through an IP address. A ‘thing’ collects and transfers data over the Internet without any manual intervention with the help of embedded technology. It helps them to interact with the external environment or internal states to take the decisions. As per the study, around 4.4 trillion GB of data will be generated by the year 2020 through the Internet of Things.

A large amount of unstructured data is generated by IoT devices which are collected in the big data system. This IoT generated big data largely depends on their 3V factors that are volume, velocity, and variety.

Quantum computer, machine learning, IoT and Big data are first growing technology. The importance of merging and deployment of these technologies is very high.

Final Thoughts:

The implementation of Big Data and AI technologies for quantum computing is complex, and this transformation will not happen overnight. It is a journey: many enterprise organizations first embarked on this journey with machine learning and then quantum computing.

Source: Artificial Intelligence on Medium

(Visited 30 times, 1 visits today)
Post a Comment

Newsletter