Two new papers in quantum computing and graph algorithms

We are happy to announce two new quantum computing papers from our group! I am particularly excited because both papers are direct applications of combinatorial scientific computing methods in the quantum computing domain.

The first, led by Hanjing Xu from Purdue University, presents a novel approach to transferring QAOA parameters for Maximum Independent Set problem using graph attention networks. This is a step toward building distributed hybrid quantum-classical algorithms, where large graph optimization problems are divided into smaller, quantum-solvable pieces and solved across quantum and classical systems. The approach achieves competitive performance and shows a promising direction for scalable quantum optimization.

Check our paper at https://arxiv.org/abs/2504.21135

The second paper, led by Mitchell Chiew from University of Cambridge and Cameron Ibrahim from University of Delaware, introduces optimization strategies for fermion-qubit mappings, a foundational step in simulating fermionic systems on quantum computers. By framing the problem as a quadratic assignment and strategically adding ancilla qubits, we achieve very good reductions to existing mappings, lowering the resource demands for quantum simulations. 

Check our paper at https://arxiv.org/abs/2504.21636

These papers couldn’t have happened without the great collaboration with Joey Xiaoyuan Liu from Fujitsu Research USA, Alex Pothen from Purdue University, and Sergii Strelchuk from the University of Oxford - thank you!