IEEE Quantum Computing and Engineering 2025

We had a fantastic time at IEEE Quantum Computing and Engineering (a.k.a. Quantum Week) https://lnkd.in/esWPtjHH. This is a highlight of the year that never disappoints. The conference initiated many exciting collaborations, and I’m especially proud that our lab presented or participated in five papers this time.

Special thanks to all of our amazing collaborators who made these papers possible Yuri Alexeev Marwa Farag Kyle Sherbert Karunya Shirali Sergii Strelchuk Mitchell Chiew Filip Maciejewski Khoa Luu Samee Khan. 

1. Ilya Tyagin, Marwa Farag, Kyle Sherbert, Karunya Shirali, Yuri Alexeev, Ilya Safro  "QAOA-GPT: Efficient Generation of Adaptive and Regular Quantum Approximate Optimization Algorithm Circuits"

2. Mitchell Chiew, Cameron Ibrahim, Ilya Safro, Sergii Strelchuk "Optimal fermion-qubit mappings via quadratic assignment"

3. Bao Bach, Filip Maciejewski, Ilya Safro "Solving Large-Scale QUBO with Transferred Parameters from Multilevel QAOA of low depth"

4. Kien Nguyen, Bao Bach, Ilya Safro "Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach"

5. Hoang-Quan Nguyen, Xuan-Bac Nguyen, Sankalp Pandey, Samee U. Khan, Ilya Safro, Khoa Luu

 "QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks"

What made it even more special was reconnecting with our former students Ruslan Shaydulin and Joey Xiaoyuan Liu, now leading experts in quantum computing. Seeing their impact on the field is both exciting and inspiring.

Looking forward to next year’s Quantum Week in Toronto!

#QuantumComputing #QAOA #QuantumOptimization #IEEEQCE #QuantumEngineering #HybridAlgorithms #Research #MachineLearning #QML 

Best Paper Award in Quantum Algorithms and IEEE Quantum Week

Our team had an amazing time at IEEE Quantum Computing and Engineering 2024 (aka IEEE Quantum Week) in Montreal, where we were honored to receive the Best Paper in Quantum Applications Award for the paper "MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical Multilevel QAOA"! The conference was packed with excellent talks, workshops, tutorials and panels. 

It was also wonderful to catch up with our lab’s alumni, the Algorithms and Computational Science Lab legends Ankit Kulshrestha Joey Xiaoyuan Liu Ruslan Shaydulin, and see their success in the field. Graduate students Bao Bach and Cameron Ibrahim gave excellent talks on their papers. Big thanks to everyone who made this event such a success. This year there were over 1,550 participants from universities, national labs, and industry. Looking forward to even better event next year in Albuquerque, NM! 

#IEEEQuantum #QCE24 #QuantumComputing #QuantumWeek #IEEE #QuantumWeek2024

Recently accepted papers in optimization and quantum and quantum inspired computing.

• Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, Indradeep Ghosh, Ilya Safro "Partitioning Dense Graphs with Hardware Accelerators", International Conference on Computational Science (ICCS), 2022, preprint at https://arxiv.org/pdf/2202.09420.pdf
• Ankit Kulshrestha, Ilya Safro "BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithms", IEEE Quantum Computing and Engineering (QCE), 2022, preprint at https://arxiv.org/abs/2204.13751
• Xiaoyuan Liu, Ruslan Shaydulin, Ilya Safro "Quantum Approximate Optimization Algorithm with Sparsified Phase Operator", IEEE Quantum Computing and Engineering (QCE), 2022, preprint at https://arxiv.org/abs/2205.00118
• Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, Avradip Mandal, Sarvagya Upadhyay, Ilya Safro, Arnab Roy "Leveraging Special-Purpose Hardware for Local Search Heuristics", Computational Optimization and Applications, 2022, preprint at https://arxiv.org/pdf/1911.09810.pdf

Tutorial on the quantum approximate optimization algorithm, its applications and simulation

We recorded this tutorial for IEEE International Conference on Quantum Computing and Engineering (QCE) 2020. This tutorial consists of four parts:

1. QAOA theory and quantum computing basics
2. Hands on example of QAOA and MaxCut
3. Introduction to problem decomposition and solving large-scale problems with QAOA
4. Tensor networks and simulation of QAOA with classical computers

AI@Clemson

It was great discussing AI and text mining at Clemson University research  symposium on AI.