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