The IBM Quantum Challenge Fall 2022 kicks off Friday, November 11 at 9:00 a.m. U.S. Eastern for eight days of learning and exploring the most promising applications in quantum computing through special missions distributed across four levels of ascending difficulty.
Join us for an expedition into the far reaches of outer space, where you’ll chart your course through vast interstellar clouds, decrypt mysterious radio signals, and escape the unyielding gravitational pull of a massive black hole — all by solving a series of four mind-bending quantum computing challenges.
The technology of quantum computing is advancing fast, but we still have a long way to go before we achieve scalable, truly fault-tolerant quantum systems. In the meantime, researchers and developers at IBM Quantum have been hard at work creating a constellation of tools and services that allow us to get useful results from the comparatively small, noisy quantum computers we have today. Now, with the IBM Quantum Challenge Fall 2022, we’re giving users a closer look at some of the brightest stars in that constellation with — what else? — a journey into space.
The IBM Quantum Challenge Fall 2022 runs from 9:00 a.m. US Eastern on Friday, November 11 until 9:00 a.m. US Eastern on Friday, November 18.
The IBM Quantum Challenge Fall 2022 runs from 9:00 a.m. US Eastern on Friday, November 11 until 9:00 a.m. US Eastern on Friday, November 18, and will focus on two trailblazing offerings unveiled by IBM Quantum in recent years: Qiskit Runtime and After launching the Qiskit Runtime as a containerized execution environment for quantum-classical programs, we enabled a simpler programming experience via Qiskit Runtime primitives. Read more.primitives. Participants in this year’s Fall Challenge will enjoy eight days of learning, exploring the most promising applications in quantum computing through special missions distributed across four levels of ascending difficulty. In the final mission, you’ll compete with your fellow Challenge participants to earn a place on our leader board of high scores.
In this Fall Challenge, each user will take on the role of a lifetime, serving as captain of the Earth’s first faster-than-light starship. It’s your job to lead humanity into a new era of space exploration, and you’ll surely encounter some magnificent discoveries along the way — from beautiful distant worlds to mind-boggling cosmic phenomena. But to survive this long and arduous journey, you’ll need the help of IBM Quantum’s Qiskit Runtime service and the new primitives-based programming model that it enables.
In May of 2021, IBM Quantum delivered 120x speedup of quantum workloads with Qiskit Runtime. Read more.Qiskit Runtime was first announced in the spring of 2021 as a tool designed to make quantum programs run faster. Cloud-enabled quantum computing had emerged just five years prior to that, in 2016, making quantum computers more widely accessible than ever before. However, early users had to deal with slow performance and long wait times, issues caused by the physical separations between quantum and classical hardware, and by the long queues of other users all waiting to run the next iteration of their quantum circuits. Qiskit Runtime addresses both of those problems. When users send a job to Qiskit Runtime, it arrives at the optimal environment — situated in close physical proximity to quantum hardware — and runs as a block, delivering a substantial speed-ups for quantum algorithms that require multiple iterations.
A year after its initial introduction, IBM Quantum developers made Qiskit Runtime even more powerful with the addition of primitives. Qiskit Runtime primitives are simple, pre-defined programs that abstract away basic quantum computing tasks — such as performing error mitigation, or calculating the expectation values of observables at the output of a quantum circuit. Primitives provide a useful starting point for efficiently extracting more relevant information from quantum hardware, delivering a managed experience that makes using quantum computers much easier for those who aren’t already experienced with these fundamental processes.
To complete the challenge, you’ll need to build on those primitives and take advantage of the Qiskit Runtime environment to calculate the reaction energies of interstellar molecules, find optimal routes for your spaceship, and more. In the process, you’ll become intimately familiar with the inner workings of Qiskit Runtime and Qiskit Runtime primitives, and you’ll get the chance to put that knowledge to work in challenges centered around three key quantum computing applications: machine learning, optimization, and chemistry.
Lab 1: Qiskit Runtime and Primitives
In Lab 1, our first challenge exercise, you’ll learn how to use primitives in Qiskit Runtime while solving simple exercises using basic quantum circuits. Even if you're completely unfamiliar with Qiskit Runtime, these exercises will give you the background you need to move on to the next challenge. If you want to learn more about Qiskit Runtime before the challenge, check out these tutorials in our Qiskit Runtime documentation.
Lab 2: Quantum Machine Learning
For Lab 2, you’ll need to learn the basics of quantum machine learning (QML) to decode your captain’s log and unearth a hidden clue about your mission. You’re likely already familiar with classical machine learning, where we use data mining and algorithmic techniques to generate meaningful insights and predictions from data sets. QML leverages quantum mechanical properties to deliver potential learning improvements over purely classical methods, and with the Qiskit Machine Learning Module, it’s easy to rapidly prototype quantum kernels, quantum neural networks, and other ML models. In this part of the challenge, you’ll learn about data encoding and classical ML methods before exploring quantum kernels and building your own quantum classifier.
Lab 3: Quantum Optimization
Lab 3 will put your navigation skills to the test as you explore the fascinating world of quantum optimization. Mathematical optimization techniques play a role in nearly every sector of the global economy — from manufacturing and shipping logistics to engineering and beyond. However, many famous and valuable Read our tutorial on the Max-Cut and Traveling Salesman Problemoptimization problems are intractable to classical computation. In Lab 3, you’ll use optimization algorithms from the Qiskit Optimization Module to do some cosmic clean-up, working out the most fuel-efficient route to eliminate dangerous pieces of space debris before slingshotting your starship out of a sticky situation.
Lab 4: Quantum Chemistry
The challenge concludes with an exercise in quantum chemistry simulation, where you’ll discover strange interstellar molecules never before seen on Earth. Chemists have been using classical computation to model and simulate the structure of molecules for decades, but classical simulations of these inherently quantum systems are incredibly computationally expensive. Classical computers can only simulate large molecular systems if they rely on rough approximations, but quantum computers have the potential to simulate these systems much more precisely and efficiently. For this final lab, you’ll use the Qiskit Nature Module to calculate key properties of your mystery molecules, and obtain important information to discover cosmic clouds that may aide in your journey home.
Error Suppression and Error Mitigation
In addition to mastering the applications detailed above, you’ll also learn about two techniques that are essential in quantum computing — error mitigation and error suppression. Modern quantum hardware is extremely susceptible to noise and error, which limit system performance by generating incorrect or unexpected outputs. One way to work around these limitations is to apply various error mitigation and suppression techniques, which serve to minimize performance degradation from hardware error. The Fall Challenge will introduce you to some exciting up-and-coming error mitigation and error suppression techniques.
So, are you ready to sign on as the captain of… Wait a minute. What is the name of your starship, anyway? We can’t just call it “Starship.” That would be pretty boring!
If you’re excited to join the fun as a participant in this year’s IBM Quantum Fall Challenge, let us know by giving your starship a fabulous name and sharing it on Twitter with the hashtag #IBMQuantumChallenge. You can also share it with us in the #challenge-fall-2022 Slack channel of the Qiskit Slack workspace, which you can join here: https://ibm.co/joinqiskitslack.
Will the upcoming Fall Challenge be your very first expedition into the world of quantum computing? Fear not — the resources listed below will help you get started on your journey:
- The Qiskit Textbook. This exhaustive online resource is a great way to study both general introductory topics and much more specialized and advanced topics, such as QML.
- So What is Qiskit Runtime, Anyway? This blog post from the Qiskit Medium offers a simple overview of what Qiskit Runtime is and why it’s so important.
- Quantum Machine Learning Course. Since QML is the focus of the second lab, you might find this self-paced course helpful. It’s similar to the original Qiskit Textbook but includes some useful bonus features, like interactive quizzes.
- Solving combinatorial optimization problems using QAOA. Qiskit Textbook chapter centered on mathematical optimization (Lab 3).
- Simulating Molecules using VQE. Qiskit Textbook chapter centered on quantum chemistry (Lab 4).
If you’re excited about the IBM Quantum Fall Challenge 2022, you might also enjoy taking part in the IBM Open Science Prize, which will be announced later this year. Keep an eye out for more news about that coming soon. And see you in space!
Date18 Oct 2022
- Note 1: After launching the Qiskit Runtime as a containerized execution environment for quantum-classical programs, we enabled a simpler programming experience via Qiskit Runtime primitives. Read more. ↩︎
- Note 2: In May of 2021, IBM Quantum delivered 120x speedup of quantum workloads with Qiskit Runtime. Read more. ↩︎
- Note 3: Read our tutorial on the Max-Cut and Traveling Salesman Problem ↩︎