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.

Paper accepted in ACM Transactions in Quantum Computing

Bao Bach, Ilya Safro, Ed Younis "Efficient Compilation for Shuttling Trapped-Ion Machines via the Position Graph Architectural Abstraction", ACM Transactions in Quantum Computing, 2026

https://dl.acm.org/doi/10.1145/3831246

With the growth of quantum platforms for gate-based quantum computation, compilation holds a crucial role in deciding the success of the implementation. While there has been rich research in compilation techniques for the superconducting-qubit regime. The trapped-ion architectures, currently leading in robust quantum computations for their reliable operations, still lack competitive compilation strategies. This work introduces a unifying hardware abstraction, the “position graph”, representing various hardware architectures. With this abstraction, we model trapped-ion Quantum Charge-Coupled Device (QCCD) architectures, enabling high-quality, scalable compilation methods. Specifically, we propose scheduling algorithms called SHuttling-Aware PERmutative (SHAPER) and SHuttling-AWare (SHAW) heuristic searches to tackle the complex constraints and dynamics of trapped-ion machines, with the cooperation of state-of-the-art permutation-aware mapping. These approaches generate executable circuits and native instructions that respect the physical constraints of shuttling-based architectures. We evaluate proposed algorithms across theorized and real architectures using the position graph framework. For completeness, we also introduce a linear program of trapped-ion scheduling that yields the optimal schedules, enabling a direct comparison with our heuristics. Our algorithm can successfully compile programs for extreme architectures where prior algorithms fail. When the baseline does complete, our produced schedules are 1.45 times faster on average, with best-case speedups up to 4 times faster.

Seven of our papers have been accepted to IEEE Quantum Computing and Engineering 2026

Seven of our papers have been accepted to the IEEE Quantum Computing and Engineering 2026. We are looking forward to catch up with many colleagues and friends and meet new people in Toronto!

1. Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes

Kien Nguyen and Ilya Safro

arXiv: https://arxiv.org/abs/2604.25275

2. Scaling Qubit Mapping and Routing With Position Graph Abstraction and Memoization

Brent Russon, Bao Bach, Ed Younis, and Ilya Safro

arXiv: https://arxiv.org/abs/2605.09237

3. Constrained Counterdiabatic Quantum Approximate Optimization Algorithm for Portfolio Optimization

Jose Falla and Ilya Safro

arXiv: https://arxiv.org/abs/2605.06858

4. Warm-Starting Rank-2 MaxCut Relaxation Using Local Correlators From Depth-1 Quantum Approximate Optimization Algorithm

Bao Bach, Filip Maciejewski, and Ilya Safro

Soon on arXiv

5. Quantum Hypergraph Partitioning

Cameron Ibrahim, Bao Bach, Jad Salem, Reuben Tate, Kien Nguyen, Stephan Eidenbenz, and Ilya Safro

arXiv: https://arxiv.org/abs/2605.10623

6. Implications of Reduced DLAs for the Standard QAOA Ansatz

Jose Falla, Bao Bach, Boris Tsvelikhovskiy, and Ilya Safro

Soon on arXiv

7. QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling

Van-Quang-Huy Nguyen, Hoang-Quan Nguyen, Samee Khan, Ilya Safro, and Khoa Luu

arXiv: https://arxiv.org/abs/2605.14001

Many thanks and congratulations to all our students and collaborators from University of California, Riverside, Los Alamos National Laboratory, Berkeley Lab, NASA - National Aeronautics and Space Administration, United States Naval Academy, University of Arkansas and Kansas State University. It's always a great pleasure working with you.