Organizers

Dr. Yuri Alexeev is a senior quantum algorithm engineer at NVIDIA Corporation and a senior member of the IEEE Society. He has expertise in the development of quantum algorithms using the NVIDIA CUDA-Q framework and GPU-optimized quantum circuit simulators. Before joining NVIDIA, he worked in Argonne Leadership Computing Facility to parallelize and optimize scientific codes for exascale supercomputers and integration of of high-performance computing with quantum computing. He completed PhD studies at Iowa State University and contributed to over 100 publications.

Dr. Tom Lubowe is the product manager for quantum computing libraries at NVIDIA. Prior to joining, he worked on quantum computing, machine learning, and tensor networks for materials design at GenMat. Tom also worked on Xanadu Cloud, and Rigetti Quantum Cloud Services in business development, product, and product operations roles. Before that, he started a quantum machine learning company, Everettian Technologies, after working on products at SEI Investments.

Dr. Salvatore Mandra obtained his Ph.D. in theoretical physics at the University of Milan (Italy) in 2013, focusing on hard-optimization problems and the relationship between computational complexity and phase transitions in spin-glass models. After the Ph.D, he worked as a postdoctoral researcher at Harvard University in the Aspuru-Guzik Lab and focused on quantum annealing and quantum computation. In 2016, he joined the Quantum Artificial Intelligence Lab (QuAIL) at NASA Ames to work on the development of novel classical and quantum methods. In 2025, he joined the Google Quantum AI team, working on the development of HPC algorithms and simulations. His expertise ranges from the theoretical development of new classical/quantum algorithms, as well as the numerical optimization of large scale classical/quantum simulations of quantum circuits (including high level programming in C/C++ and distributed programming in MPI/OpenMP). In the last few years, Dr. Mandrà focused on the development of large scale benchmarks for both analog and digital quantum computers, including the development of high-performance algorithms to raise the bar for quantum advantage.

Dr. Matthew Otten is an assistant professor of physics at the University of Wisconsin -- Madison, broadly studying the theoretical and computational aspects of quantum computing. After obtaining a B.S. in Physics from Illinois Tech, he attended Cornell to obtain his Ph.D., also in physics. He spent three years as the Maria Goeppert Mayer fellow at Argonne National Laboratory and three years as a scientist at HRL Laboratories. Dr. Otten’s work includes new quantum algorithms, large-scale simulations of various quantum systems, and methods for understanding and mitigating inevitable noise on quantum computers and sensors.

Dr. Gonzalo Alvarez is a senior R&D staff in the computational sciences directorate at Oak Ridge National Laboratory, and a member of the Quantum Science Center. He received his PhD in Physics from Florida State University in 2004. His overarching topic of research has been the theoretical and computational study of strongly correlated electron systems, and the understanding of the complexity that emerges from these systems. Quantum computing has emerged as a technology that can help in this understanding. For example, Alvarez and collaborators have recently applied gene expression programming to create quantum circuits applicable to condensed matter and graph theory. On classical computing, Alvarez's research has focused on tensor networks, on the density matrix renormalization group, and on other algorithms that do not rely on uncontrolled approximations but that can systematically converge to the exact answer, and where the error made can be estimated. Alvarez has over fifteen years of experience in programming C++. He has advocated for native compiled programming, for release of scientific computer programs with a free and open-source license, for maintenance of programs, for openness to contributions, and for making every effort to aid researchers in reproducing published numerical results.

Dr. Peter Groszkowski is a scientist at the Oak Ridge National Laboratory. He is interested in various areas of physics, especially those that have a close connection to the field of quantum computing and information. In the past, he has worked on theoretical studies of superconducting circuits in the context of device-modeling, as well as gates and control. His other core research interests include understanding and finding simple, but accurate descriptions of the effects of non-Markovian noise on quantum systems. More recently, he has also been exploring the physics of quantum metrology and sensing, concentrating on finding methods for generating highly entangled, spin-squeezed states, which promise to allow a measurement sensitivity beyond the standard quantum limit. Besides physics, Peter is interested in all aspects of software development ranging from numerical modeling, to instrumentation control.