# Introducing the Qiskit Challenge India, A Taste of Quantum Machine Learning for Qiskitters in India

*Register here** for the Qiskit Challenge India, a two-week Quantum Machine Learning Challenge starting on September 1st at 17:00 IST*

*By Rana Prathap Simh Mukthavaram, Co-op at IBM Quantum and Qiskit*

Qiskit Challenge 2019 was my ticket into the quantum computing world. This year, the Qiskit team wants to bring the same opportunity exclusively to India’s aspiring quantum pioneers.

On Tuesday, September 1st, we’ll be kicking off a two-week-long event: Qiskit Challenge India. I’m excited to present this opportunity for quantum computing-interested folks in India to meet other Qiskitters and IBMers, learn the fundamentals of quantum computing, and tackle an open problem in the budding field of quantum machine learning.

I first encountered quantum computing in my second year studying Mathematics and Computing at IIT Kharagpur, when I read an article about quantum effects arising in transistors due to their decreasing size and how we could harness rather than avoid them to speed up computations. Whether or not it was technically correct, it seemed cool and immediately sparked my interest. The next summer, I interned at the University of Calgary, where I worked on a quantum hardware problem: building a quantum random number generator. Working with qubits as a unit of information storage got me interested in learning about what could be done with them in computational devices, re-charting my career trajectory toward quantum computing in the process.

At the same time, I started learning slowly by taking edX courses and playing around with Qiskit, where I found the resources to kick off my journey — and heard about the 2019 Challenge. Even though it was already three weeks into the four-week competition, I randomly reached out to another member of the Qiskit community who I’d never met before but who seemed to know a lot about quantum computing, Rahul Pratap Singh, and asked if he’d like to partner up. I’m so happy that I did.

We dove in, rereading all of the material that the IBM Quantum team had released in the past three weeks, learning about quantum computers and gates while we answered previous questions. But then we got to this amazing question: it was a graph coloring problem depicted through a story about a city with Japanese convenience stores called konbinis which we had to color such that no two adjacent stores had the same color. The story disguised the fact that we were actually working on an important mathematical problem on a quantum computer. We worked for three to four days trying to figure out the solution, and stayed up all night. It was invigorating. We noticed that we were behind in the leaderboard, so I took a small risk, one I thought was right on the verge of breaking the competition’s rules, and submitted it. We placed third.

A few days later, the competition’s organizer Yuri Kobayashi reached out to tell us that, while our solution was incredibly efficient, it didn’t conform to the rules, and so we were removed from the leaderboard. But the judges liked our work so much that we were invited to Qiskit Camp Asia. That experience was eye-opening. I realized that the quantum computing community was vast and much bigger than I expected. During the camp I had a conversation with a Ph.D student on a bus ride, and he explained a lot of what I wanted to know about his field of study, quantum error correction. I also witnessed just how many resources IBM had invested in the community to make a seemingly opaque area like quantum computing more accessible. Mainly, this entire experience gave me perspective on the different sub-fields of quantum computing research. I realized that you did not need a Ph.D in physics to work in quantum computing, and you could contribute significantly as a mathematician like me. This made me want to pursue a full-time career in quantum computing.

I’ve since joined the IBM Quantum and Qiskit team (where Rahul and I are coworkers, by the way) and now, I want to give back to the quantum community in India. Considering that I got into quantum computing through a competition, I thought that it would be a great opportunity for quantum novices to experience quantum computing the way I had: through a competition solving a real-world problem on a quantum computer.

This brings me to the Qiskit Challenge India. The challenge will begin with simple exercises to help you get your hands dirty, but the final question will require teamwork and possibly long hours of brainstorming. I picked Quantum Machine Learning (QML) as the challenge’s focus because I wanted to learn about this exciting new topic alongside you, the challenge’s participants. However, QML is still in its early stages, and it’s unclear just how good a speedup it will provide over classical machine learning in the near term. This challenge will be an opportunity for you to work on an open problem in QML today — one where we still don’t know what the best answer is. The best part is that you don’t need any prior knowledge in quantum computing, just a basic working knowledge of python.

My advice for you is to use this opportunity to interact with the community. It’s a team challenge, and working together will help you learn far faster than working on your own. Ask questions shamelessly, and use this as a way to get to know fellow Qiskitters, Qiskit Advocates, and IBMers. I hope that you’ll take part!

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*Interested in beginning your quantum journey? **Register here** for the Qiskit Challenge India, a 2-week Quantum Machine Learning Challenge starting on September 1st at 17:00 IST and running through September 15th at 17:00 IST. Teams of up to 5 can participate.*

*The top four teams to successfully complete the challenge at the end of the competition will receive certificates, Qiskit community swag, and exclusive access to an IBMer for 1 hour of mentoring. Every participant who successfully score 70% or above on the week 1 questions will also receive a certificate indicating their completion. Those who complete the final challenge with an accuracy above a given threshold will receive a certificate for their understanding of the VQC algorithm.*