What we do/Team/In news

Quantum Computing    Quantum computers are expected to accelerate scientific discovery spanning many different areas such as medicine, AI, material science, and financial predictions. Quantum hardware manipulates with much more complex than binary information that is represented in classical computers. We are interested in quantum algorithms and methods of their hybridization with classical computing systems as well as how classical computing can facilitate and improve quantum computing.

Machine Learning    Many machine learning algorithms are prohibitive for large-scale number of variables, samples and high dimensionality. For example, this can happen because of the slow convergence or NP-hardness of underlying optimization problems (such as in support vector machines and cut-based clustering). We are interested in algorithms that cope with such problems.

AI, Literature Based Discovery and Text Mining    Hypothesis generation is becoming a crucial time-saving family of techniques which allow researchers to quickly discover implicit connections between important concepts. We are interested in such techniques and complex text mining problems, in general. Applications include biomedical discovery with scientific texts, healthcare and social media.

Network Science and Graph Algorithms    We are interested in computational, modeling, theory and data problems related to complex networks in social/natural/information sciences, and engineering. Their analysis often requires scalable algorithms for frequent pattern discovery, outliers detection, quantitative methods for importance ranking of network elements, time-dependent data analysis, evolution modeling, visualization, and community detection.

Combinatorial Scientific Computing    This is an area in which we study discrete optimization problems on large-scale graphs that are used to accelerate the performance of scientific computing algorithms. Examples include (hyper)graph partitioning, reordering, and coloring to improve load-balancing, task mapping, and data locality on HPC.

Multiscale Methods    A broad range of scientific problems involve multiple scales. Traditional monoscale approaches have proven to be inadequate, even with the largest supercomputers, because of the prohibitively large number of variables involved. We develop multiscale approaches in which a hierarchy of coarse scale approximations is used to solve large-scale problems efficiently.

The Duel of the Mixers in Quantum Approximate Optimization

Our new paper "Equivariant QAOA and the Duel of the Mixers" has been accepted in IEEE Transactions on Quantum Engineering!

In this work, we show that the choice of mixer Hamiltonian plays a critical role in QAOA performance. Many combinatorial optimization problems have natural symmetries, but standard QAOA mixers do not take advantage of them. We introduce new symmetry aware mixers that are tailored to the structure of the problem and can be efficiently implemented as quantum circuits. Through numerical experiments on graph coloring and graph partitioning, we demonstrate that these mixers consistently outperform the standard approach. We also explain why warm start QAOA often fails to improve results, identifying a fundamental limitation that prevents guaranteed convergence. This work shows that exploiting symmetry is a powerful and practical way to improve quantum optimization algorithms.

Congratulations and huge thanks to our co-authors Boris Tsvelikhovskiy and Yuri Alexeev! Check our paper at https://arxiv.org/abs/2405.07211

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 

Where Are Our PhD Students This Summer?

Congratulations to our PhD Students on their summer internships!

We’re proud to share that several of our PhD students are heading to exciting internship opportunities this summer. These positions will allow them to apply their research skills in real-world settings and gain valuable experience in industry and national labs.

Bao Bach - Los Alamos National Laboratory

Cameron Ibrahim - Pacific Northwest National Laboratory

Kien Nguyen - Fujitsu Research USA

Ilya Tyagin - BioCurie and Amazon

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!

QAOA-GPT: Efficient Generation of Adaptive and Regular Quantum Approximate Optimization Algorithm Circuits

Our new paper "QAOA-GPT: Efficient Generation of Adaptive and Regular Quantum Approximate Optimization Algorithm Circuits" is now out! We introduce QAOA-GPT, a generative AI framework that uses GPT-style model to synthesize quantum circuits for solving combinatorial optimization problems and demonstrate it on MaxCut. Unlike traditional quantum approximate optimization algorithms and adaptive approaches, which rely on slow, iterative optimization, QAOA-GPT generates full circuits in a single forward pass, greatly improving scalability and efficiency. Importantly, this approach does not rely on prompt engineering or fine-tuning—the model is trained entirely from scratch using a custom circuit representation and graph-conditioned architecture.

It was a great pleasure collaborating on this project with an amazing team Marwa Farag and Yuri Alexeev from NVIDIA, and Kyle Sherbert and Karunya Shirali from Virginia Tech - thank you all for making this project a great experience! And huge congratulations to PhD student Ilya Tyagin for leading this work!

Check our paper at https://arxiv.org/abs/2504.16350

Cross-problem parameter transferability in Quantum Approximate Optimization Algorithm

Our latest work on making Quantum Approximate Optimization Algorithm more efficient through parameter transferability. Now it is the transferability across combinatorial optimization problems. Imagine a scenario where we have pre-trained QAOA parameters on a large collection of known optimization problems during the preprocessing. But then, a new problem class emerges, perhaps less studied or previously unknown. Should we invest substantial computational resources to re-optimize from scratch? 

We propose a graph neural network-based retrieval framework that learns graph embeddings and predicts transferable QAOA parameter sets optimized for MaxCut to accelerate convergence on Maximum Independent Set. Congratulations to PhD students Kien Nguyen and Bao Bach!

Check our paper at https://arxiv.org/abs/2504.10733

Kien Nguyen is joining our group

 We're pleased to share that Kien Nguyen, PhD student in Computer Science, is joining our group. Kien will be working on quantum computing and machine learning.

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

Our collaboration with NASA/USRA, Rigetti and Purdue

We have another accepted paper which is the result of our collaboration with amazing colleagues at NASA/USRA, Rigetti and Purdue.

Filip B. Maciejewski, Bao Gia Bach, Maxime Dupont, P. Aaron Lott, Bhuvanesh Sundar, David E. Bernal Neira, Ilya Safro, Davide Venturelli "A Multilevel Approach For Solving Large-Scale QUBO Problems With Noisy Hybrid Quantum Approximate Optimization" is accepted in IEEE High-Performance Extreme Computing (HPEC) 2024

https://arxiv.org/abs/2408.07793

This work is a practical demonstration of our hybrid quantum-classical multilevel solver for the maxcut. Here we experimentally test how existing quantum processors perform as a sub-solver within the  multilevel strategy. We combine and extend (via additional classical processing) the recent Noise-Directed Adaptive Remapping (NDAR) and Quantum Relax & Round algorithms. We first demonstrate the effectiveness of our heuristic extensions on Rigetti's transmon device Ankaa-2 and then we find approximate solutions to 10 instances of fully connected Sherrington-Kirkpatrick graphs with random integer-valued coefficients obtaining normalized approximation ratios in the range ∼0.98−1.0. Then, we implement the extended NDAR and QRR algorithms as subsolvers in the multilevel algorithm for 6 large-scale graphs with at most ∼27,000 variables. The QPU (with classical post-processing steps) is used to find approximate solutions to dozens of problems, at most 82-qubit, which are iteratively used to construct the global solution. 

Recently accepted quantum computing papers

 July has been a prolific month for the quantum computing team in our lab. Four of our papers have been accepted for publication!

1) Jose Falla, Quinn Langfitt, Yuri Alexeev, Ilya Safro "Graph Representation Learning for Parameter Transferability in Quantum Approximate Optimization Algorithm" is accepted in Quantum Machine Intelligence. arxiv.org/abs/2401.06655

As the title suggests, we use graph embeddings to directly transfer precomputed parameters of QAOA to new graphs. Even relatively small-scale graph representation models imply good transferability, significantly accelerating computation.

2) Bao Bach, Jose Falla, Ilya Safro "MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical Multilevel QAOA" is accepted in IEEE Quantum Computing and Engineering. arxiv.org/abs/2404.14399

Here, using combinations of representation learning techniques, ideas of R-QAOA, and quantum-inspired recursive optimization, we significantly accelerate the hybrid quantum-classical multilevel method and achieves a great time/quality trade-off that successfully competes with the fastest classical heuristics, making this algorithmic scheme a strong candidate for demonstrating practical quantum advantage in the future.

3) Cameron Ibrahim, Teague Tomesh, Zain Saleem, Ilya Safro "Scaling Up the Quantum Divide and Conquer Algorithm for Combinatorial Optimization" is accepted in IEEE Quantum Computing and Engineering. arxiv.org/abs/2405.00861

In this paper, we introduce the Deferred Constraint Quantum Divide and Conquer Algorithm, a method for constructing quantum circuits that reduces inter-device communication costs for some quantum graph optimization algorithms, an important step for distributed quantum computing.

4) Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, Bao Bach, Ilya Safro "QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuit", accepted in IEEE Quantum Computing and Engineering, Quantum AI workshop, https://arxiv.org/abs/2408.13352, 2024

Here we present QAdaPrune - an adaptive parameter pruning algorithm that automatically determines the threshold and then intelligently prunes the redundant and non-performing parameters to train quantum circuits. We show that the resulting sparse parameter sets yield quantum circuits that perform comparably to the unpruned quantum circuits and in some cases may enhance trainability of the circuits even if the original quantum circuit gets stuck in a barren plateau.

Congratulations to the graduate students Bao Bach, Jose Falla, and Cameron Ibrahim, and our amazing collaborators Teague Tomesh, Zain Hamid Saleem, Quinn Langfitt, Yuri Alexeev, Ankit Kulshrestha, Xiaoyuan Liu, and Hayato Ushijima-Mwesigwa. We look forward to meeting many of you in person and having a productive IEEE Quantum Computing and Engineering 2024 in Montreal!

This is what DALL-E thinks about us celebrating accepted papers. The quantum hardware store doors are open but where can we find this store?

Check our article on challenges and opportunities of scaling up quantum computation in SIAM News. May 2024

Our article is in SIAM News, May 2024

Rapid advancements in various quantum computing architectures have ignited a sense of cautious optimism about the realization of quantum advantage, especially as the field transitions from predominantly theoretical explorations to more applied functionalities. In addition to addressing challenges that pertain to single quantum bit (qubit) noise, fidelity, and decoherence, vendors are now emphasizing the scalability of their quantum systems as a critical component of their success. Current architectures—including trapped ions, superconducting circuits, and Rydberg atoms—face scalability hurdles in terms of the number of qubits and their fidelity. These hurdles have inspired both academic and industry researchers to develop scaling strategies that are reminiscent of the early (but still relevant) stages of classical supercomputing. Here, we discuss some of quantum computing’s opportunities and challenges in the realms of circuit optimization and scalability.
Click here to read full article

Quantum computing for finance: Our work is in UDaily.

 Great recent article in UDaily about our joint project with Argonne National Lab, JPMorgan Chase, Fujitsu and Menten AI. We discuss the obstacles and opportunities of quantum computing in finance.

Click here for the full article

Benchmarking Biomedical Literature-based Discovery and Hypothesis Generation Systems

Update: The paper is accepted for publication in BMC Bioinformatics.

We introduce a benchmarking framework Dyport for evaluating biomedical hypothesis generation (HG) and literature based discovery (LBD) systems. The evaluation of HG and LBD is still one of the major problems of these systems, especially when it comes to fully automated large-scale general purpose systems. For these, a massive assessment (that is normal in the machine learning and general AI domains) is often infeasible. One traditional evaluation approach is to make a system “rediscover” some of the landmark findings. However, this approach does not scale. Another traditional approach is to automatically discover some information in biomedical texts, train the system on historical data and test it on that "future" information. While this approach does scale well, the reliability and biomedical importance of the extracted test set are far from being illuminating. 

Utilizing curated datasets, Dyport tests HG/LBD systems under realistic conditions, enhancing the relevance of our evaluations. We integrate knowledge from the curated databases into a dynamic graph, accompanied by a method to quantify discovery importance. This not only assesses hypothesis accuracy but also their potential impact in biomedical research which significantly extends traditional link prediction benchmarks. Applicability of our benchmarking process is demonstrated on several link prediction systems applied on biomedical semantic knowledge graphs. Being flexible, our benchmarking system is designed for broad application in hypothesis generation quality verification, aiming to expand the scope of scientific discovery within the biomedical research community. 

Dyport is available at https://github.com/IlyaTyagin/Dyport

Paper: https://arxiv.org/pdf/2312.03303.pdf

Ankit Kulshrestha's PhD Defense

Congratulations to Dr. Ankit Kulshrestha for successfully defending his Ph.D. thesis "A Machine Learning Approach To Improve Scalability and Robustness of Variational Quantum Circuits"! Ankit will join the quantum computing research group at Fujitsu Research USA.

Welcoming our new PhD students!

 We're pleased to share that our group has expanded with the addition of two PhD students.

Bao Bach, joining from the Quantum Science and Engineering program, will be working on quantum machine learning and variational quantum algorithms. 

Saeideh Valipour has started her journey with us through the Computer Science PhD program. Her research areas include text mining, machine learning, and biomedical data analysis.

Let's extend a warm welcome to Bao and Saeideh as they begin their research endeavors with our group.

 

Two papers are accepted at IEEE High-Performance and Extreme Computing

We are happy to announce that two of our papers have been accepted at the IEEE High-performance and Extreme Computing Conference (HPEC 2023). 

"Hybrid Quantum-Classical Multilevel Approach for Maximum Cuts on Graphs" by Anthony Angone, Xiaoyuan Liu, Ruslan Shaydulin, and Ilya Safro. 

This paper is focusing on the Max-Cut problem. The study introduces a scalable hybrid multilevel approach to solve large instances of Max-Cut using both classical solvers and the quantum approximate optimization algorithm (QAOA). The results showcase the excellent performance of both classical and hybrid quantum-classical methods. 

The solver is publicly available at https://github.com/angone/MLMax-cut. 

Congratulations to the lead student and first author, Anthony Angone! 

"Decomposition Based Refinement for the Network Interdiction Problem" by Krish Matta, Xiaoyuan Liu, and Ilya Safro. 

This paper introduces a novel algorithm for the shortest path network interdiction problem. By decomposing the problem into sub-problems, the method efficiently utilizes Ising Processing Units alongside classical solvers. The results demonstrate comparable quality to existing exact solvers and also highlight a significant reduction in computational time for large-scale instances. 

The source code and experimental results can be accessed at https://github.com/krishxmatta/network-interdiction. 

A special shout-out to the lead student and first author, Krish Matta. A significant portion of his paper was accomplished during his high school years! Once again, congratulations to Anthony and Krish for their outstanding work and contributions to the field. We look forward to presenting these papers at HPEC 2023!

Learning To Optimize Quantum Neural Network Without Gradients

Our paper is accepted in IEEE Quantum Computing and Engineering (QCE) 2023. The preprint is available at https://arxiv.org/abs/2304.07442

Congratulations to PhD student Ankit Kulshrestha and two former students from our lab, now quantum computing research scientists at Fujitsu Research, Xiaoyuan Liu and Hyato Ushijima-Mwesigwa!

Quantum Machine Learning is an emerging sub-field in machine learning where one of the goals is to perform pattern recognition tasks by encoding data into quantum states. This extension from classical to quantum domain has been made possible due to the development of hybrid quantum-classical algorithms that allow a parameterized quantum circuit to be optimized using gradient based algorithms that run on a classical computer. The similarities in training of these hybrid algorithms and classical neural networks has further led to the development of Quantum Neural Networks (QNNs). However, in the current training regime for QNNs, the gradients w.r.t objective function have to be computed on the quantum device. This computation is highly non-scalable and is affected by hardware and sampling noise present in the current generation of quantum hardware. In this paper, we propose a training algorithm that does not rely on gradient information. Specifically, we introduce a novel meta-optimization algorithm that trains a meta-optimizer network to output parameters for the quantum circuit such that the objective function is minimized. We empirically and theoretically show that we achieve a better quality minima in fewer circuit evaluations than existing gradient based algorithms on different datasets.

Accepted paper in Nature Physics Reviews

Our paper "Quantum Computing for Finance" is accepted in Nature Physics Reviews. 

https://www.nature.com/articles/s42254-023-00603-1

The paper and its long version available in arXiv https://arxiv.org/pdf/2201.02773.pdf discuss not only the quantum algorithms in one of the most fascinating application areas but also the realistic obstacles the quantum computing faces to achieve any practical advantage over classical computing.

Graduate student achievement

 Congratulations to our PhD student Ankit Kulshrestha for receiving Rama and Shashi Marda fellowship for graduate studies! Ankit's research is in the areas of quantum and classical machine learning.