Quantum computing company IQM, which manufactures quantum computing hardware, and also runs an online platform where users can run algorithms on quantum computers remotely via cloud computing, sponsored a challenge using their platform, specifically their 56-superconducting qubit quantum computer, to demonstrate genuine multipartite entanglement of as many qubits as possible. Genuine multipartite entanglement in the context of quantum computing means that multiple particles/qubits together form a single entangled state, and that entangled state cannot be broken down or separated into component states. This is difficult to show using real hardware, as error introduced into the system can make proving true multipartite entanglement problematic, and computational time and costs prohibits the use of certain techniques such as full quantum state tomography.
Teams were given a 24-hour period to read scientific literature, come up with a plan for tackling the challenge, familiarize themselves with the quantum computing platform, write code, and repeatedly test/debug/ and alter an approach. The challenge was graded on a variety of different criteria, including the theoretical correctness of results, the degree to which the solution was optimized for IQM’s quantum computers, the number of qubits demonstrating entanglement, the variety of different states demonstrating entanglement, and the quality of presentation. At the end of the 24 hours, results were presented to a panel of judges, including researchers at IQM.
Chowdhary’s Topological Ducks took first place, optimizing their prepared state and showing entanglement of 16 distinct superconducting qubits. Topological Ducks teammates included Brown undergraduates Simon Nirenberg ’28 and Hugo Mullen ’26, and software engineer Jeff Burka of Singularity Energy. “I think our team did well because of our varied skills and interests, which allowed us to target each of these criteria. The two things I'm most proud of my team for accomplishing were successfully demonstrating entanglement on 16 qubits, which required a lot of algorithmic thinking as we tried to optimize qubit routing for IQM’s machines. Our approaches ranged from using simple graph algorithms like Kruskal’s Algorithm and Dijkstra’s Algorithm, to using sophisticated open source optimization tools like Google’s OR-Tools.
“And for writing a program which automatically generated valid graph states, which allowed us to test our methodology on a number of states of different complexities. This essentially involved mapping a certain set of quantum states to graphical representations, which we were then able to generate with a non-deterministic algorithm. With more time, we were hoping to even extend this to be a fully-fledged benchmark suite for testing entanglement on quantum computers more generally. I was very happy with our presentation, which the team put a lot of time and effort into. In my personal opinion, it was one of the most satisfying parts of the competition, and I do feel as though our performance on the presentation contributed significantly to our win.”
Hochberg’s team, the Optismizers, proved genuine entanglement of 12 distinct superconducting qubits on a real quantum computer by finding and preparing graph states optimized for the hardware and leveraging a tool called an entanglement witness. The team consisted of Hochberg, Brown students Johanna Nguyen ’27 and Leo Li ’28, and Boston University student David Ou. The Optismizers took third place.
Pankaj’s teammates in the Quantum Rings’s challenge included physics concentrators Hayden Miller ’26, Woody Hulse ’26, Caden Schroeder ’26, and Patrick Jennings ’26. Quantum Rings is a startup that has developed a platform to simulate quantum circuits on classical computers. Through their platform, users can select the accuracy (threshold) for simulations, greatly impacting the runtime of simulations. “The runtime is also highly dependent on the complexity of the quantum circuit,” Pankaj said. “The challenge we were given was to create a model to predict the threshold and runtime required to simulate quantum circuits with a 75 percent fidelity.
“This was a difficult challenge because we were given very limited training data. For our winning submission, our team compared eight machine learning models and selected the best-performing ones to predict the threshold and runtime, respectively. For the latter, we used a graph neural network, which was a more advanced approach.”
Crowdhary added, “I really enjoy quantum computing hackathons like iQuHACK because they try to incorporate both mathematical rigor and low-level technical details into their challenges, and this was no different. Quantum computing is an inherently interdisciplinary field and is really enjoyable no matter which angle you approach it from, and I’d love to see more physicists, chemists, mathematicians, computer scientists, and engineers all further involve themselves with quantum computing at Brown.”
Brown Ph.D. candidate Miles Miller-Dickson and Innovation Management and Entrepreneurship (PRIME) graduate student Alan Bidart, former competitors in the hackathon, served as mentors and judges for the event.
