Yuri Alexeev

VLDB 2026 Research / reviewers in the wild / expert

dblp:65/2523 · DBLP profile ↗

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17ranked-venue papers

5as first author

8since 2021 · last 2025

0000-0001-5066-2254ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 12 · 3 first-author · 5 since 2021Theory of computation · 3 · 2 first-author · 3 since 2021Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging computing · 1

Year	Publication	Venue	Position
2025	What to Support When You're Compressing: The State of Practice Gaps and Opportunities for Scientific Data Compression abstract Over the last nearly 20 years, lossy compression has become an essential aspect of HPC applications’ data pipelines, allowing them to overcome limitations in storage capacity and bandwidth and, in some cases, increase computational throughput and capacity. However, with the adoption of lossy compression comes the requirement to assess and control the impact lossy compression has on scientific outcomes. In this work, we take a major step forward in describing the state of practice and by characterizing workloads. We examine applications’ needs and compressors’ capabilities across 9 different supercomputing application domains. We present 24 takeaways that provide best practices for applications, operational impacts for facilities achieving compressed data, and gaps in application needs not addressed by production compressors that point towards opportunities for future compression research. Franck Cappello, Robert Underwood, Yuri Alexeev, Allison H. Baker, Ebru Bozdag, Martin Burtscher, Kyle Chard, Sheng Di, Kyle Gerard Felker, Paul Christopher O'Grady, Hanqi Guo 0001, Yafan Huang, Peng Jiang 0004, Sian Jin, Petter Johansson, Shaomeng Li, Xin Liang 0001, Erik Lindahl, Peter Lindstrom 0001, Zarija Lukic, Magnus Lundborg, Danylo Lykov, Masaru Nagaso, Kento Sato, Amarjit Singh, Seung Woo Son 0001, Shihui Song, William Tang 0002, Dingwen Tao, Jiannan Tian, Kazutomo Yoshii, Kai Zhao 0008	SC	3
2024	Quantum-centric supercomputing for materials science: A perspective on challenges and future directions Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini, Torey Battelle, Daan Camps, David Casanova, Young Jay Choi, Fred Chong, Charles Chung, Christopher Codella, Antonio D. Córcoles, James Cruise, Alberto Di Meglio, Ivan Duran, Thomas Eckl, Sophia E. Economou, Stephan J. Eidenbenz, Bruce Elmegreen, Clyde Fare, Ismael Faro, Cristina Sanz Fernández, Rodrigo Neumann Barros Ferreira, Keisuke Fuji, Bryce Fuller, Laura Gagliardi, Giulia Galli, Jennifer R. Glick, Isacco Gobbi, Pranav Gokhale, Salvador de la Puente Gonzalez, Johannes Greiner, William Gropp, Michele Grossi, Emanuel Gull, Burns Healy, Matthew R. Hermes, Benchen Huang, Travis S. Humble, Nobuyasu Ito, Artur F. Izmaylov, Ali Javadi-Abhari, Douglas M. Jennewein, Shantenu Jha, Bert de Jong, Petar Jurcevic, William M. Kirby, Stefan Kister, Masahiro Kitagawa, Joel Klassen, Katherine Klymko, Kwangwon Koh, Masaaki Kondo, Doga Murat Kürkçüoglu, Krzysztof Kurowski, Teodoro Laino, Ryan Landfield, Matthew L. Leininger, Vicente Leyton-Ortega, Ang Li 0006, Meifeng Lin, Junyu Liu, Nicolás Lorente, André Luckow, Simon Martiel, Francisco Martín-Fernández, Margaret Martonosi, Claire Marvinney, Arcesio Castañeda Medina, Dirk Merten, Antonio Mezzacapo, Kristel Michielsen, Abhishek Mitra, Tushar Mittal, Kyungsun Moon, Joel Moore, Sarah Mostame, Mario Motta, Young-Hye Na, Yunseong Nam, Prineha Narang, Yu-ya Ohnishi, Daniele Ottaviani, Matthew Otten, Scott Pakin, Vincent R. Pascuzzi, Edwin Pednault, Tomasz Piontek, Jed W. Pitera, Patrick Rall, Gokul Subramanian Ravi, Niall Robertson, Matteo A. C. Rossi, Piotr Rydlichowski, Hoon Ryu, Georgy Samsonidze, Mitsuhisa Sato, Nishant Saurabh, Kunal Sharma, Soyoung Shin, George Slessman, Mathias Steiner, Iskandar Sitdikov, In-Saeng Suh, Eric D. Switzer, Joel Thompson, Synge Todo, Minh C. Tran, Dimitar Trenev, Christian Trott, Huan-Hsin Tseng, Norm M. Tubman, Esin Tureci, David García Valiñas, Sofia Vallecorsa, Christopher Wever, Konrad W. Wojciechowski, Xiaodi Wu 0001, Shinjae Yoo, Nobuyuki Yoshioka, Victor Wen-zhe Yu, Seiji Yunoki, Sergiy Zhuk, Dmitry Zubarev	Future Gener. Comput. Syst.	1
2023	GPU-Accelerated Error-Bounded Compression Framework for Quantum Circuit Simulations abstract Quantum circuit simulations enable researchers to develop quantum algorithms without the need for a physical quantum computer. Quantum computing simulators, however, all suffer from significant memory footprint requirements, which prevents large circuits from being simulated on classical super-computers. In this paper, we explore different lossy compression strategies to substantially shrink quantum circuit tensors in the QTensor package (a state-of-the-art tensor network quantum circuit simulator) while ensuring the reconstructed data satisfy the user-needed fidelity.Our contribution is fourfold. (1) We propose a series of optimized pre- and post-processing steps to boost the compression ratio of tensors with a very limited performance overhead. (2) We characterize the impact of lossy decompressed data on quantum circuit simulation results, and leverage the analysis to ensure the fidelity of reconstructed data. (3) We propose a configurable compression framework for GPU based on cuSZ and cuSZx, two state-of-the-art GPU-accelerated lossy compressors, to address different use-cases: either prioritizing compression ratios or prioritizing compression speed. (4) We perform a comprehensive evaluation by running 9 state-of-the-art compressors on an NVIDIA A100 GPU based on QTensor-generated tensors of varying sizes. When prioritizing compression ratio, our results show that our strategies can increase the compression ratio nearly 10 times compared to using only cuSZ. When prioritizing throughput, we can perform compression at the comparable speed as cuSZx while achieving 3-4× higher compression ratios. Decompressed tensors can be used in QTensor circuit simulation to yield a final energy result within 1-5% of the true energy value. Milan Shah, Xiaodong Yu 0001, Sheng Di, Danylo Lykov, Yuri Alexeev, Michela Becchi, Franck Cappello	IPDPS	5
2023	Introduction to the Special Issue on Software Tools for Quantum Computing: Part 2 abstract Quantum computing is emerging as a remarkable technology that offers the possibility of achieving major scientific breakthroughs in many areas. By leveraging the unique features of quantum mechanics, quantum computers may be instrumental in advancing many areas, including science, energy, defense, medicine, and finance. This includes solving complex problems whose solution lies well beyond the capacity of contemporary and even future supercomputers that are based on conventional computing technologies. As a foundation for future generations of computing and information processing, quantum computing represents an exciting area for developing new ideas in computer science and computational engineering. Yuri Alexeev, Alex McCaskey, Wibe de Jong	ACM Trans. Quantum Comput.	1
2022	QuYBE - An Algebraic Compiler for Quantum Circuit Compression abstract Qu YBE is an open-source algebraic compiler for the compression of quantum circuits. It has been applied for the efficient simulation of the Heisenberg Hamiltonian on quantum computers. Currently, it can simulate the time dynamics of one-dimensional chains. It includes modules to generate the quantum circuits for the above as well as produce the compressed circuits, which are independent of the time step. It utilizes the Yang-Baxter equation (YBE) to perform the compression. QuYBE enables users to seamlessly design, execute, and analyze the time dynamics of the Heisenberg Hamiltonian on quantum computers. QuYBE is the first step toward making the YBE technique available to a broader community of scientists from multiple domains. The QuYBE compiler is available at https://github.com/ZichangHe/QuYBE. Sahil Gulania, Zichang He, Bo Peng 0024, Niranjan Govind, Yuri Alexeev	SEC	5
2022	Introduction to the Special Issue on Software Tools for Quantum Computing: Part 1 abstract Quantum computing is emerging as a remarkable technology that offers the possibility of achieving major scientific breakthroughs in many areas.By leveraging the unique features of quantum mechanics, quantum computers may be instrumental in advancing many areas, including science, energy, defense, medicine, and finance.This includes solving complex problems whose solution lies well beyond the capacity of contemporary and even future supercomputers that are based on conventional computing technologies.As a foundation for future generations of computing and information processing, quantum computing represents an exciting area for developing new ideas in computer science and computational engineering. Yuri Alexeev, Alex McCaskey, Bert de Jong	ACM Trans. Quantum Comput.	1
2021	TILT: Achieving Higher Fidelity on a Trapped-Ion Linear-Tape Quantum Computing Architecture abstract Trapped-ion qubits are a leading technology for practical quantum computing. In this work, we present an architectural analysis of a linear-tape architecture for trapped ions. In order to realize our study, we develop and evaluate mapping and scheduling algorithms for this architecture. In particular, we introduce TILT, a linear “Turing-machinelike” architecture with a multilaser control “head,” where a linear chain of ions moves back and forth under the laser head. We find that TILT can substantially reduce communication as compared with comparable-sized Quantum Charge Coupled Device (QCCD) architectures. We also develop two important scheduling heuristics for TILT. The first heuristic reduces the number of swap operations by matching data traveling in opposite directions into an “opposing swap.”, and also avoids the maximum swap distance across the width of the head, as maximum swap distances make scheduling multiple swaps in one head position difficult. The second heuristic minimizes ion chain motion by scheduling the tape to the position with the maximal executable operations for every movement. We provide application performance results from our simulation, which suggest that TILT can outperform QCCD in a range of NISQ applications in terms of success rate (up to 4.35x and 1.95x on average). We also discuss using TILT as a building block to extend existing scalable trapped-ion quantum computing proposals. Xin-Chuan Wu, Dripto M. Debroy, Yongshan Ding 0001, Jonathan M. Baker, Yuri Alexeev, Kenneth R. Brown, Fred Chong	HPCA	5
2021	Multilevel Combinatorial Optimization across Quantum Architectures abstract Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the post Moore’s law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this article, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. To demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave’s quantum annealer and IBM’s gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitude larger than the current quantum hardware size, and we observe results comparable to state-of-the-art solvers in terms of quality of the solution. Reproducibility : Our code and data are available at Reference [1]. Hayato Ushijima-Mwesigwa, Ruslan Shaydulin, Christian F. A. Negre, Susan M. Mniszewski, Yuri Alexeev, Ilya Safro	ACM Trans. Quantum Comput.	5
2020	Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems abstract Quantum computing is a computational paradigm with the potential to outperform classical methods for a variety of problems. Proposed recently, the Quantum Approximate Optimization Algorithm (QAOA) is considered as one of the leading candidates for demonstrating quantum advantage in the near term. QAOA is a variational hybrid quantum-classical algorithm for approximately solving combinatorial optimization problems. The quality of the solution obtained by QAOA for a given problem instance depends on the performance of the classical optimizer used to optimize the variational parameters. In this paper, we formulate the problem of finding optimal QAOA parameters as a learning task in which the knowledge gained from solving training instances can be leveraged to find high-quality solutions for unseen test instances. To this end, we develop two machine-learning-based approaches. Our first approach adopts a reinforcement learning (RL) framework to learn a policy network to optimize QAOA circuits. Our second approach adopts a kernel density estimation (KDE) technique to learn a generative model of optimal QAOA parameters. In both approaches, the training procedure is performed on small-sized problem instances that can be simulated on a classical computer; yet the learned RL policy and the generative model can be used to efficiently solve larger problems. Extensive simulations using the IBM Qiskit Aer quantum circuit simulator demonstrate that our proposed RL- and KDE-based approaches reduce the optimality gap by factors up to 30.15 when compared with other commonly used off-the-shelf optimizers. Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash	AAAI	4
2019	Full-state quantum circuit simulation by using data compression abstract Quantum circuit simulations are critical for evaluating quantum algorithms and machines. However, the number of state amplitudes required for full simulation increases exponentially with the number of qubits. In this study, we leverage data compression to reduce memory requirements, trading computation time and fidelity for memory space. Specifically, we develop a hybrid solution by combining the lossless compression and our tailored lossy compression method with adaptive error bounds at each timestep of the simulation. Our approach optimizes for compression speed and makes sure that errors due to lossy compression are uncorrelated, an important property for comparing simulation output with physical machines. Experiments show that our approach reduces the memory requirement of simulating the 61-qubit Grover's search algorithm from 32 exabytes to 768 terabytes of memory on Argonne's Theta supercomputer using 4,096 nodes. The results suggest that our techniques can increase the simulation size by 2~16 qubits for general quantum circuits. Xin-Chuan Wu, Sheng Di, Emma Maitreyee Dasgupta, Franck Cappello, Hal Finkel, Yuri Alexeev, Fred Chong	SC	6
2018	PaSTRI: Error-Bounded Lossy Compression for Two-Electron Integrals in Quantum Chemistry abstract Computation of two-electron repulsion integrals is the critical and the most time-consuming step in a typical parallel quantum chemistry simulation. Such calculations have massive computing and storage requirements, which scale as O(N^4) with the size of a chemical system. Compressing the integral's data and storing it on disk can avoid costly recalculation, significantly speeding the overall quantum chemistry calculations; but it requires a fast compression algorithm. To this end, we developed PaSTRI (Pattern Scaling for Two-electron Repulsion Integrals) and implemented the algorithm in the data compression package SZ. PaSTRI leverages the latent pattern features in the integral dataset and optimizes the calculation of the appropriate number of bits required for the storage of the integral. We have evaluated PaSTRI using integral datasets generated by the quantum chemistry program GAMESS. The results show an excellent 16.8 compression ratio with low overhead, while maintaining 10-10absolute precision based on user's requirement. Ali Murat Gok, Sheng Di, Yuri Alexeev, Dingwen Tao, Vladimir A. Mironov, Xin Liang 0001, Franck Cappello	CLUSTER	3
2018	Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks abstract BACKGROUND: Real-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success. With sensors becoming increasingly fast, frameworks such as Deep Neural Networks are required to perform calculations within the strict timing constraints for real-time operation. However, traditional computing platforms responsible for running these algorithms incur a large overhead due to communication protocols, memory accesses, and static (often generic) architectures. In this work, we implement a low-latency Multi-Layer Perceptron (MLP) processor using Field Programmable Gate Arrays (FPGAs). Unlike CPUs and Graphics Processing Units (GPUs), our FPGA-based design can directly interface sensors, storage devices, display devices and even actuators, thus reducing the delays of data movement between ports and compute pipelines. Moreover, the compute pipelines themselves are tailored specifically to the application, improving resource utilization and reducing idle cycles. We demonstrate the effectiveness of our approach using mass-spectrometry data sets for real-time cancer detection. RESULTS: We demonstrate that correct parameter sizing, based on the application, can reduce latency by 20% on average. Furthermore, we show that in an application with tightly coupled data-path and latency constraints, having a large amount of computing resources can actually reduce performance. Using mass-spectrometry benchmarks, we show that our proposed FPGA design outperforms both CPU and GPU implementations, with an average speedup of 144x and 21x, respectively. CONCLUSION: In our work, we demonstrate the importance of application-specific optimizations in order to minimize latency and maximize resource utilization for MLP inference. By directly interfacing and processing sensor data with ultra-low latency, FPGAs can perform real-time analysis during procedures and provide diagnostic feedback that can be critical to achieving higher percentages of successful patient outcomes. Ahmed Sanaullah, Chen Yang 0010, Yuri Alexeev, Kazutomo Yoshii, Martin C. Herbordt	BMC Bioinform.	3
2017	An efficient MPI/openMP parallelization of the Hartree-Fock method for the second generation of Intel® Xeon Phi™ processor abstract Modern OpenMP threading techniques are used to convert the MPI-only Hartree-Fock code in the GAMESS program to a hybrid MPI/OpenMP algorithm. Two separate implementations that differ by the sharing or replication of key data structures among threads are considered, density and Fock matrices. All implementations are benchmarked on a super-computer of 3,000 Intel® Xeon Phi™ processors. With 64 cores per processor, scaling numbers are reported on up to 192,000 cores. The hybrid MPI/OpenMP implementation reduces the memory footprint by approximately 200 times compared to the legacy code. The MPI/OpenMP code was shown to run up to six times faster than the original for a range of molecular system sizes. Vladimir A. Mironov, Yuri Alexeev, Kristopher Keipert, Michael D'Mello, Alexander A. Moskovsky, Mark S. Gordon	SC	2
2012	Heuristic static load-balancing algorithm applied to the fragment molecular orbital method abstract In the era of petascale supercomputing, the importance of load balancing is crucial. Although dynamic load balancing is widespread, it is increasingly difficult to implement effectively with thousands of processors or more, prompting a second look at static load-balancing techniques even though the optimal allocation of tasks to processors is an NP-hard problem. We propose a heuristic static load-balancing algorithm, employing fitted benchmarking data, as an alternative to dynamic load balancing. The problem of allocating CPU cores to tasks is formulated as a mixed-integer nonlinear optimization problem, which is solved by using an optimization solver. On 163,840 cores of Blue Gene/P, we achieved a parallel efficiency of 80% for an execution of the fragment molecular orbital method applied to model protein-ligand complexes quantum-mechanically. The obtained allocation is shown to outperform dynamic load balancing by at least a factor of 2, thus motivating the use of this approach on other coarse-grained applications. Yuri Alexeev, Ashutosh Mahajan, Sven Leyffer, Graham Fletcher, Dmitri G. Fedorov	SC	1
2005	Multilevel Parallelism in Computational Chemistry using Common Component Architecture and Global Arrays abstract The development of complex scientific applications for high-end systems is a challenging task. Addressing complexity of the involved software and algorithms is becoming increasingly difficult and requires appropriate software engineering approaches to address interoperability, maintenance, and software composition challenges. At the same time, the requirements for performance and scalability to thousand processor configurations magnifies the level of difficulties facing the scientific programmer due to the variable levels of parallelism available in different algorithms or functional modules of the application. This paper demonstrates how the Common Component Architecture (CCA) and Global Arrays (GA) can be used in context of computational chemistry to express and manage multi-level parallelism through the use of processor groups. For example, the numerical Hessian calculation using three levels of parallelism in NWChem computational chemistry package outperformed the original version of the NWChem code based on single level parallelism by a factor of 90% when running on 256 processors. Manojkumar Krishnan, Yuri Alexeev, Theresa L. Windus, Jarek Nieplocha	SC	2
2002	Performance and Implementation of Distributed Data CPHF and SCF Algorithms abstract This paper describes a novel distributed data parallel self consistent field (SCF) algorithm and the distributed data coupled perturbed Hartree-Fock (CPHF) step of an analytic Hessian algorithm. The distinguishing features of these algorithms are: (a) columns of density and Fock matrices are distributed among processors, (b) pairwise dynamic load balancing and an efficient static load balancer were developed to achieve a good workload, and (c) network communication time is minimized via careful analysis of data flow in the SCF and CPHF algorithms. By using a shared memory model, novel work load balancers, and improved analytic Hessian steps, we have developed codes that achieve superb performance. The performance of the CPHF code is demonstrated on a large biological system. Yuri Alexeev, Michael W. Schmidt, Theresa L. Windus, Mark S. Gordon, Ricky A. Kendall	CLUSTER	1
2002	A Distributed Data Implementation of Parallel Full CI Program abstract A distributed data parallel full CI program is described The implementation of the FCI algorithm is organized in a combined Cl driven approach With extra computation we were able to avoid redundant communication, and convert the collective communication into more efficient point-to-point communication. The network performance is further optimized by improved DDI library. Examples show very good speedup performance on 16 node PC clusters. The application of the code is also demonstrated. Zhengting Gan, Yuri Alexeev, Ricky A. Kendall, Mark S. Gordon	CLUSTER	2