Share this post:
To encourage more teachers and students to take advantage of the IBM Q Experience and the IBM Qiskit development platform, we announced in January a number of challenges and prizes to inspire people to take the quantum leap.
We’re happy to announce the winners of the third IBM Q Award: the IBM Q Best Paper Award, which offered one first place prize of $1,500, one second place prize of $1,000, and a travel stipend of up to $1,500 for the authors of the best five papers to attend a quantum event held at an IBM Research lab.
The prizes were awarded for the highest-impact scientific papers by a master’s student, PhD student or postdoctoral researcher using the IBM Q Experience and Qiskit as tools to achieve the presented results.
Congratulations to our first-place winner, Christophe Vuillot of QuTech and TU Delft; our second-place winner, Clement Javerzac-Galy, and his students, of EPFL; and our three runners-up, Maria Schuld of the University of KwaZulu-Natal, Shantanu Debnath of the University of Maryland, College Park; and Alejandro Pozas-Kerstjens of ICFO – The Institute of Photonic Sciences.
“My research showed an average improvement of the task of sampling from states that can be fault-tolerantly prepared in the [[4,2,2]] code, when using a fault-tolerant technique well suited to the layout of the IBM five-qubit chip, showing that fault-tolerant quantum computation is already within our reach,” says Christophe.
Clement’s paper started as a student project when his master’s class on quantum information at EPFL started using the IBM Q Experience. “Very quickly we decided to shift to Qiskit to program some more complex experiments,” he says. “We realized how powerful the full system is for learning and for training the next generation of quantum engineers.”
Clement Javerzac-Galy’s students
“In my paper, I propose that quantum machine learning should be thought of from the direction of quantum circuits which lead to classifiers, instead of the other way around,” says Maria. “We implemented a proof-of-principle experiment with the IBM Q Experience, and together with numerical simulations, showed that this classifier works surprisingly well in simple benchmarks, providing a minimal example of a quantum machine learning algorithm that can be implemented and understood by beginners to quantum computing.”
Shantanu’s experimental comparison of two quantum computing architectures supported the idea that quantum computer applications and hardware should be co-designed, and Alejandro developed a quantum algorithm for performing Bayesian learning on deep neural networks.
Our sincere thanks goes to all the participants. It was difficult to pick a winner, as all of the entries were of high quality.
Visit the Qiskit blog over the coming weeks to read blog posts by the winners!
The IBM Q Awards
The first award, Teach Me Qiskit, was awarded in June to Alba Cervera Lierta, a Ph.D. student at the University of Barcelona and the Barcelona Supercomputing Center in Spain. Read her blog post on her submission here.
The second award, the IBM Qiskit Developer Challenge, was awarded in August to Alwin Zulehner (first place), Sven Jandura and Eddie Schoute (tied for second place). Read Alwin’s blog post here, Sven’s here and Eddie’s here.
In the meantime, there is still time for the Teach Me Quantum Award, open to professors or lecturers who use Qiskit or the IBM Q Experience in their lectures. Entries must be received by Thursday, November 15, 2018.
Is error detection helpful on IBM 5Q chips? – Christophe Vuillot
Efficient quantum algorithms for GHZ and W states, and implementation on the IBM quantum computer – Diogo Cruz, Romain Fournier, Fabien Gremion, Alix Jeannerot, Kenichi Komagata, Tara Tosic, Jarla Thiesbrummel, Chun Lam Chan, Nicolas Macris, Marc-André Dupertuis, Clément Javerzac-Galy
Implementing a distance-based classifier with a quantum interference circuit – Maria Schuld, Mark Fingerhuth, Francesco Petruccione
Experimental comparison of two quantum computing architectures – Norbert M. Linke, Dmitri Maslov, Martin Roetteler, Shantanu Debnath, Caroline Figgatt, Kevin A. Landsman, Kenneth Wright, Christopher Monroe
Bayesian Deep Learning on a Quantum Computer – Zhikuan Zhao, Alejandro Pozas-Kerstjens, Patrick Rebentrost, Peter Wittek