The IBM Quantum Challenge Fall 2021 will run from October 27 to November 5 in collaboration with the University of Tokyo and Keio University this year. Additionally, participants will be able to run their quantum programs using Qiskit Runtime in this challenge. Qiskit Runtime is a new architecture offered by IBM Quantum that streamlines computations that require many iterations.
What fields can we expect to see impacts from quantum computation in the near future? The IBM Quantum Challenge Fall 2021 aims to answer this question by showcasing some of quantum computing’s different industry applications, featuring Qiskit’s Kernels. Algorithms. Models. Read more about application-specific modules in the IBM Quantum Development Roadmap.application modules: Finance, Nature, Machine Learning, and Optimization.
Collaborating with research and industry across the world
This year marks the first installations of the IBM Quantum System One outside of the United States, with systems in Germany and in Japan. Representatives from the University of Tokyo and Keio University, who are collaborating on this year's challenge, have been hard at work not only conducting quantum research, but creating new business use cases for clients.
The University of Tokyo unveiled a landmark collaboration in 2020 with the launch of the Quantum Innovation Initiative Consortium (QIIC) to forge a path for Japan’s discovery of practical quantum applications for the benefit of society. The cooperation between industry, academia, and government aims to create a new community for quantum computation research and use cases with members of the QIIC. Consortium members include Keio University, Toshiba, Hitachi, Mizuho, MUFG, JSR, DIC, Toyota, Mitsubishi Chemicals, Sony, Sumitomo Mitsui Trust, Yokogawa, and IBM Japan.
Keio University became the first IBM Quantum Network Hub in Asia back in 2018 along with four industrial partners JSR Corporation, MUFG Bank, Mizuho Financial Group, and The research triumvirate of Mitsubishi Chemical, Keio University, and IBM Quantum is working to better understand lithium-oxygen’s potential as an energy source by using new algorithms that take advantage of quantum computing. Read the case study.Mitsubishi Chemical Corporation. Since then, Keio University together with its hub members have demonstrated significant accomplishments in finance and chemistry applications research.
In fact, some of the exercises developed for this challenge are based on the accomplishments of the Keio Quantum Network Hub and will be implemented using Qiskit’s application module for the first time. We hope these challenges will appeal not only to students and quantum developers, but also to various industry professionals who are new to quantum computing and curious to learn about its future applications.
What to expect from the IBM Quantum Challenge Fall 2021
You have 10 days to learn and work on real-life problems in each of the following four promising areas of application for quantum computers. To learn more about how to run quantum programs using Qiskit Runtime, a new architecture offered by IBM Quantum that streamlines computations that require many iterations, check out our documentation.
In the financial services industry, there are many computationally intensive problems involving simulation, optimization and machine learning in applications such as asset management, investment banking, and retail & corporate banking. Quantum computing in finance has the potential to show advantage over classical approaches in solving these computationally challenging problems and has become an active topic of research. In our first challenge exercise, you will learn how to use classes and methods provided by the Qiskit Finance module to solve an actual financial problem such as portfolio optimization using a quantum computer.
For learning about the different applications of quantum computing in Finance and the different classes and methods in Qiskit you can use to solve them, read these tutorials in the Qiskit Finance documentation.
The ability to simulate molecules would greatly accelerate the development of new materials and drug discovery, but it is computationally expensive to accurately calculate chemical reactions. Quantum algorithms provide an efficient method for determining the energy of molecules, and studies in which the molecules of hydrogen, lithium hydride, and beryllium hydride were simulated using an actual quantum computer are well known.
A recent research paper, by IBM and Keio Hub researchers, published1 results of calculating the excited states of organic light-emitting diode Industrial chemists at Mitsubishi Chemical and JSR Corporation, and academics from Keio University modeled and analyzed the deep molecular structures of potential new OLED materials using IBM Quantum systems.(OLED) materials with high accuracy. In this exercise, we will look into how we can calculate such excited states using variational quantum algorithms.
You can learn more about simulating molecules in chapter 4.1.2 of the Qiskit textbook, and the video, below, of lectures 22 to 27 from the Quantum Global Summer School 2020.
Recent IBM research demonstrated that there exists machine learning problems2 for which quantum algorithms could provide an exponential speedup over classical methods known as the quantum kernel method. This method uses the effect of quantum entanglement to map data into a high-dimensional feature space, which may reveal features that have not been fully analyzed so far. In this exercise you will learn how a quantum-enhanced version of a classical machine learning algorithm can solve a certain class of classification problem which is one of the most fundamental problems in machine learning.
For a quick introduction to machine learning, watch Episode 6, below, from Coding with Qiskit, Season 2, or study the tutorials in Qiskit Machine Learning documentation.
Optimization is a data-driven technology that is used in a variety of situations in all industries. In manufacturing, it is used to improve product performance, reduce costs, and improve efficiency in manufacturing; in supply chain management, it is used for order planning and distribution routes; and in finance, it is used to optimize asset portfolios. Among these optimization problems, the traveling salesman problem, the max-cut problem, and the knapsack problem are some of the so-called NP-hard problems that cannot be solved in polynomial time by classical computers. In the final exercise, we will tackle one of these NP-hard problems using a quantum computer.
You can learn about solving combinatorial optimization problems using QAOA in the Qiskit textbook. And there are more tutorials on solving optimization problems in Qiskit—including a tutorial that features the Max-Cut is an NP-complete problem, with applications in clustering, network science, and statistical physics. The traveling salesman problem is an NP-complete problem that has drawn the attention of computer scientists and mathematicians for over two centuries that has important bearings on finance and marketing. Try the Qiskit tutorial.Max-Cut and Traveling Salesman Problem.
In addition to the existing learning materials mentioned above, we are also planning to launch a two-part livestream series on Qiskit YouTube channel for you to learn and explore all four Qiskit application modules:
- Part 1: Qiskit Optimization & Machine Learning Demo Session with Atsushi Matsuo & Anton Dekusar on October 8 at 10:00 AM (EDT)
- Part 2: Qiskit Nature & Finance Demo Session with Max Rossmannek & Julien Gacon on October 15 at 10:00 AM (EDT)
You can watch the replays if you missed the livestream.
Start your quantum journey
IBM Quantum Challenge is in its third year, and with each challenge we see many beginners join and start their quantum journey. Even more exciting is that some participants have hosted their own regional challenges in countries like India, last year, and Africa, last month—empowering local Qiskit communities and further spreading the joy and excitement of learning quantum computing. Similarly, we look forward to seeing new and unique stories created through this challenge.
- Gao, Q., Jones, G.O., Motta, M. et al. Applications of quantum computing for investigations of electronic transitions in phenylsulfonyl-carbazole TADF emitters. npj Comput Mater 7, 70 (2021).↩
- Havlíček, V., Córcoles, A.D., Temme, K. et al. Supervised learning with quantum-enhanced feature spaces. Nature 567, 209–212 (2019).↩