IBM Quantum is excited to announce the third annual Open Science Prize. Submissions are open now. Learn more and register here. And go to the Qiskit Community Github for the starter notebook.
This year’s challenge asks participants to tackle a problem based on the concept of quantum state preparation. Quantum state preparation entails taking a quantum system from one state to another, like taking a system with qubits all set to |0>, and putting it into a known but arbitrary state. This can be far more challenging than merely setting the values of qubits, however. Quantum state preparation is a fundamental element of quantum computation, but it can be very difficult in practice.
Participants will attempt to prepare the highly frustrated ground state of a Heisenberg spin-1/2 model on a Kagome lattice using the Variational Quantum Eigensolver (VQE) algorithm. This may sound like a mouthful, but it is rich with interesting physics. The Kagome lattice is a tessallation of hexagons and triangles. While there are many crystaline lattices found in nature, the Kagome lattice is instead one that scientists work to simulate in labs to study their quantum properties. The lattice is rich with interesting physics: the magnetic spins of its component particles are subject to competing forces due to its structure, causing the whole system to take on strange macroscopic behaviors.
You will implement the VQE algorithm on an IBM Quantum Falcon heavy-hex device with 16 qubits. This is the largest device used in an Open Science Prize to date and of course, working with more qubits presents its own challenges. But don’t be intimidated. We have included a list of resources to help you get started in the template notebook.
The best open source solution will receive a 30,000-dollar prize, and runner up will receive 20,000. Participants can form teams of up to five.
This year marks the third annual IBM Quantum Open Science Prize, founded in 2020 by IBM Quantum researchers to develop open source, hardware-aware approaches to pressing quantum computing problems. The 2020 challenge posed two problems to competitors: to reduce SWAP gate errors, and to improve the fidelity of graph state preparation on IBM Quantum hardware. The 2021 challenge asked participants to simulate the time dynamics a Heisenberg model Hamiltonian for a three-particle system.
We selected the previous years’ problems because of their applicability to the quantum computing field as a whole, and this year’s challenge is no different. Many applications of quantum computing rely upon the ability to perform high-fidelity quantum state preparation, like quantum algorithms, quantum machine learning, quantum error correction, and quantum physics research. The cool physics of this specific quantum state is the icing on the cake.
We hope that the solutions to this challenge, which will be published and announced in the Spring of 2023, will push the state-of-the-art techniques for quantum computing, and bring the whole field forward. Visit the Qiskit Community Github for the starter notebook.
