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After the success of its online course in BSc in Programming and Data Science, the top-ranking institute in India Indian Institute of Technology (IIT) Madras is now offering a BS Degree course option as well. The BSc in Programming and Data Science now comes with an option for a four-year BS Degree in Data Science and Applications. This is made available after strong demand from students all over the country, claims the IIT. As part of the BS level, students can do an 8-month apprenticeship or a project with companies or research institutes.

Students who are currently in class 12 can also apply and secure admission to the programme. Admitted students will start the program after successfully completing their class 12. Students from any stream can enrol. There is no age limit. Anyone who has studied English and Mathematics in Class 10 is eligible to apply. As the classes are conducted online, there is also no geographic limit. The last date to apply for the September 2022 term of this Data Science Program is August 19. Interested students can apply through the website – onlinedegree.iitm.ac.in.

Read | IIT Madras’ AI Courses in High Demand Abroad, Institute to Set-up Off-Shore Campuses

“This first-of-its-kind program makes it possible for students to study from IIT Madras without attempting the intensely-competitive Joint Entrance Examination (JEE). This directly benefits the students from rural areas and economically disadvantaged backgrounds for whom attending JEE coaching classes would be a significant hurdle. The program also offers up to 100% scholarships for deserving students in financial need,” the IIT said in official notice.

This course is designed to offer students multiple entry and exit options where the learner can earn a certificate, diploma, or degree – depending upon the level they are leaving the course at. This provides flexibility to learners and empowers them to choose what they want to accomplish through this programme. These multiple entries and exits are part of the flexibilities offered under the NEP.

In the course, students will learn how to manage data, visualize patterns to gain managerial insights, model uncertainties, and build models that assist in producing forecasts to make effective business decisions. Through extensive hands-on training and experiential learning, the students are also well trained to meet the industry standards, the IIT said adding that it will also facilitate internships and placements for the students who have completed the diploma level of the programme.

Currently, more than 13,000 students are enrolled in the programme, with the maximum number of students from Tamil Nadu, followed by Maharashtra and Uttar Pradesh. In-person exams are conducted in 116 examination centers across 111 cities in India. The examination centers have also been opened in the UAE, Bahrain, Kuwait, and Sri Lanka.

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A new guide on programming quantum algorithms leads programmers through every step, from theory to implementing the algorithms on IBM’s publicly accessible 5-qubit ibmqx4 quantum computer and others.

The guide covers the fundamentals, along with a summary of the main quantum algorithms and instructions on how to implement them on publicly available quantum computers

As quantum computers proliferate and become more widely available, would-be quantum programmers are left scratching their brains over how to get started in the field. A new beginner’s guide offers a complete introduction to quantum algorithms and their implementation on existing hardware.

“Writing quantum algorithms is radically different from writing classical computing programs and requires some understanding of quantum principles and the mathematics behind them,” said Andrey Y. Lokhov, a scientist at Los Alamos National Laboratory and lead author of the recently published guide in ACM Transactions on Quantum Computing. “Our guide helps quantum programmers get started in the field, which is bound to grow as more and more quantum computers with more and more qubits become commonplace.”

The book reviews 20 quantum algorithms in brief, stand-alone parts and includes well-known, fundamental quantum algorithms like Grover’s Algorithm for database searching and much more, and Shor’s Algorithm for factoring integers. The tutorial then teaches programmers how to implement the algorithms on several quantum computers, including IBM’s publicly available 5-qubit IBMQX4 quantum computer, to make the connection to the real world. In each instance, the authors go through the implementation’s outcomes and clarify the differences between the simulator and actual hardware runs.

“This article was the result of a rapid-response effort by the Information Science and Technology Institute at Los Alamos, where about 20 Lab staff members self-selected to learn about and implement a standard quantum algorithm on the IBM Q quantum system,” said Stephan Eidenbenz, a senior[{” attribute=””quantum computing scientist at Los Alamos, a coauthor of the article and director of ISTI when work on it began.

It was intended to train employees who had little or no training with quantum computing to implement a quantum algorithm on a real-world quantum computer in order to prepare the Los Alamos workforce for the quantum era, according to Eidenbenz.

These staff members, in addition to a few students and well-established quantum experts, make up the long author list of this “crowd-sourced” overview article that has already been heavily cited, Eidenbenz said.

Before moving on to the more complex topics of unitary transformations and gates, quantum circuits, and quantum algorithms, the first section of the guide explains the fundamentals of programming a quantum computer, including qubits and qubit systems, superposition, entanglement, and quantum measurements.

The section on the IBM quantum computer covers the set of gates available for algorithms, the actual physical gates implemented, how the qubits are connected, and the sources of noise, or errors.

Another section looks at the various types of quantum algorithms. From there, the guide dives into the 20 selected algorithms, with a problem definition, description, and steps for implementing each one on the