This Fall’s IBM Quantum Challenge not only introduced participants to quantum computing and how to use Qiskit, but it was designed to inspire interest among industry professionals on how quantum technology can be applied in practical areas such as finance, chemistry, machine learning, and optimization. While many participants were new to quantum computing, more than 1,293 active participants were able to make at least one submission, and 677 participants completed all four challenge exercises. Participants earned badges from foundational to advanced — empowering them to continue their quantum journey driven by their desire to learn and share their skills forward.
The IBM Quantum Challenge Fall 2021 was the first quantum challenge to employ Qiskit Runtime into its challenge exercises. Qiskit Runtime allows users to run quantum programs that consist of a large amount of circuits in a containerized execution environment with low-latency access to quantum hardware. This makes quantum programs that uses iterative or repeated use of quantum hardware such as variational quantum eignesolver (VQE) algorithm dramatically faster than was previously possible.
For example, Qiskit Runtime was used in the second challenge problem where participants were asked to simulate and calculate energy bandgap of an organic molecule which is useful in OLED technology. Using Qiskit Runtime and the Qiskit Nature module, 881 participants ran over 7,000 VQE runtime jobs (on our simulator and real hardware) with over 328 million executions on the 7-qubit ibm_perth quantum system dedicated for the challenge.
The IBM Quantum Hub at Japan’s Keio University, including members Mitsubishi Chemical and JSR Corporation, are researching ways to model and analyze the deep molecular structures of potential new OLED materials — on IBM Quantum devices. Read more.
A Challenge for everyone
Qiskit’s application modules provide simplicity in implementing data structures and algorithms based on the problems you are trying to solve. This helped bring a wider audience to this challenge, including those from domains that may one day see impacts from quantum computing. While plenty of participants came from information technology, computer software, and higher education sectors, we also saw industry professionals from financial services, electrical and manufacturing, telecommunications, mechanical and industrial engineering, health care, automotive, pharmaceuticals, logistics and transportation, media and entertainment, and chemicals join our challenge.
Participants appreciated the perspective that the challenge brought. One participant said: “It was a great way to get hands-on experience across a variety of applications where quantum computing can offer an advantage over traditional computing.”
While another told us: “Using Qiskit’s application libraries to solve problems instead of building circuits from scratch was quite refreshing and fun.”
Read the challenge exercises and example solutions written by the authors, here.
Paving the road towards quantum advantage
The fourth and last optimization challenge had a final bonus stage that required reading a paper that introduced special techniques to reduce the circuit cost to solve a battery revenue problem. Despite the particularly high level of difficulty for this bonus exercise, 49 participants were able to make successful submissions and competed in the ranking race by continually improving their scores until the very end of the challenge.
This experience reiterated to us that the IBM Quantum Challenges serves as an important education platform, encouraging people from various backgrounds to engage in learning quantum programming using Qiskit and IBM's quantum systems. It also helped us highlight the fantastic talent of the participants, especially those who earned Advanced Level digital badges and high score rankings.
Top 10 scorers of the IBM Quantum Challenge Fall 2021
Scores were determined by measuring the circuit implementation cost to solve the final exercise. Score = (50 × depth) + (10 × # of Cx gate) + (# of Rz gate) + (# of single-qubit Sqrt(X) gate)
| Ranking | Name | Score |
|---|---|---|
| 1 | Naphan Benchasattabuse | 173,344 |
| 2 | Ibrahim Almosallam | 194,712 |
| 3 | Kamen Petroff | 219,080 |
| 4 | Kento Ueda | 232,824 |
| 5 | Kentaro Ohno | 240524 |
| 6 | Leonardo Zambrano | 241,936 |
| 7 | Alberto Maldonado | 263,220 |
| 8 | Takuya Furusawa | 269,940 |
| 9 | Yusheng Zhao | 282,639 |
| 10 | Yuki Koizumi | 297,772 |
Our winner Naphan Benchasattabuse is a PhD candidate at Keio University in Japan, who says that these challenges are a great way for people to learn something new very efficiently.
“I really enjoy the competitive aspect of the challenge. I think I learn new things the quickest this way. I really believe these challenges do play a big role in giving a fun and interactive learning opportunity for people who are new to quantum computing."
Naphan joined our first IBM Quantum Challenge in 2019 with no formal education in quantum computing at that time. This experience later inspired him to apply for a PhD program at Keio University to pursue research in the field of quantum computing. “I hope there will be a lot more quantum challenges to come for us to enjoy. I hope these challenges would have an impact on many people's lives as it had for me.”
Check out the top scorers’ solutions, here.
From beginner to expert in 10 days
With IBM’s recent announcement of the 127-qubit Eagle processor and the progress along its roadmap towards achieving quantum advantage, nurturing talent to help pave the road towards our ambitious goal is more important than ever. Perhaps the most rewarding experience organizing an event like the IBM Quantum Challenge is to see how participants can learn something new and grow in such a short amount of time. Many people started out as beginners coming into this challenge, yet emerged achieving great levels of experience and skill from tackling the problem sets and learning from their peers.
We saw the average score increase from 4.6 to 6.5 in terms of skills and knowledge level in quantum computing and 4.6 to 6.4 for that of Qiskit before and after the challenge.
We were extremely happy to see this achievement reflected in many comments and tweets from people who joined the challenge as they shared their learning experience and intentions to learn more about quantum computing. Thank you for participating.
