This article highlights synergies between quantum information science (QIS) and operations research for QIS-curious operations researchers (and vice versa), and the author challenges the community to discover more. History: This “Challenge” paper was invited by the Editor in Chief and based on the topics raised by the author at his plenary address at the 2022 INFORMS Computing Society Conference in Tampa, Florida. Funding: This material is based on work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Accelerated Research in Quantum Computing, Fundamental Algorithmic Research for Quantum Computing. This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC, under Contract DE-NA0003525 with the U.S. Department of Energy (DOE).
This article highlights synergies between quantum information science (QIS) and operations research for QIS-curious operations researchers (and vice versa), and the author challenges the community to discover more. History: This “Challenge” paper was invited by the Editor in Chief and based on the topics raised by the author at his plenary address at the 2022 INFORMS Computing Society Conference in Tampa, Florida. Funding: This material is based on work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Accelerated Research in Quantum Computing, Fundamental Algorithmic Research for Quantum Computing. This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC, under Contract DE-NA0003525 with the U.S. Department of Energy (DOE).
Quantum Information Science (QIS) is an interdisciplinary field involving mathematics, computer science, and physics. Appealing aspects include an abundance of accessible open problems, active interest and support from government and industry, and an energetic, open, and collaborative international research culture. We describe our student–faculty joint work in QIS that has led to many student co-authored papers in research journals. We address program structure and funding, and describe the work performed by our students.
Quantum information science (QIS) is a new field of science and
technology, combining and drawing on the disciplines of
physical science, mathematics, computer science, and
engineering. QIS aims to understand how certain fundamental
principles of physics discovered early in the 20th century can
be harnessed to dramatically improve the acquisition,
transmission, and processing of information. The exciting
scientific opportunities offered by QIS are attracting the
interest of the growing community of scientists and engineers,
and are promoting unprecedented interactions across traditional
disciplinary boundaries.
Scientific and technological progress has repeatedly revolutionized society, from the invention of steam-power-driven machines that brought forth the Industrial Age to the development of the transistor that sparked the Information Age. As societies and economies become more connected through global communication networks and reliant on computational power, the demand for faster, more efficient, and secure information capabilities escalates commensurately. Enter Quantum Information Science (QIS) (QIS), a field founded on the physical laws of quantum mechanics that introduce extraordinary different and powerful paradigms for generating, manipulating, measuring, securing, and processing information. While many quantum effects are generally well understood and form the basis of widely exploited current technology, QIS is still a richly underexplored research area. This overview provides a basic research perspective on the foundational resources that empower QIS, the main challenges in exploiting QIS for practical applications, and the need for input from researchers outside quantum physics, such as those from the aerospace community, to stimulate new ideas and advance progress in QIS. This paper introduces key QIS concepts and continues with discussions on each of the following subareas: sensing, computation and algorithms, networks, and information security. The paper concludes with an outlook of the field as it relates to aerospace.
Quantum Information Systems (QIS) have emerged as a transformative paradigm, integrating the principles of quantum mechanics with advanced computational and communication technologies to address complex problems beyond the capabilities of classical systems. This review provides a comprehensive synthesis of current developments, beginning with the foundational principles that define QIS, including its theoretical underpinnings, quantum state representations, and computational frameworks. It then examines advances in quantum computing for QIS, with a focus on hardware architectures, the design and optimization of core algorithms, and the computational advantages over classical approaches. The discussion extends to quantum machine learning for QIS, exploring hybrid quantum–classical learning models, quantum data encoding techniques, and optimization strategies. Finally, it surveys diverse application domains, such as finance, industrial information integration, energy systems, healthcare, and intelligent transportation. In addition, the paper highlights pressing technological, data-centric, algorithmic, and ecosystem-level challenges, and discusses future trajectories shaped by innovations such as quantum blockchain, quantum artificial intelligence, and decentralized QIS. Through a structured and integrative analysis, this work aims to provide a roadmap for researchers and practitioners navigating the rapidly evolving QIS landscape.
The HHL algorithm is a prominent quantum algorithm that offers exponential speedup over its classical counterparts for solving a system of linear equations. However, synthesizing and executing HHL circuits demand significant computational resources from both classical and quantum systems. In this paper, we benchmark the HHL algorithm using the optimized Quantum Phase Estimation (QPE) generation algorithm, LuGo [1], to enhance its scalability and efficiency. We leverage various highperformance computing (HPC) systems–the National Energy Research Scientific Computing Center's (NERSC's) Perlmutter supercomputer and Oak Ridge Leadership Computing Facility's (OLCF's) Andes system and Frontier supercomputer–to evaluate the scalability of generating HHL circuits and to measure the time to simulate the generated circuits. Additionally, enabled by the Quantum Computing User Program (QCUP) of OLCF, we provide a comprehensive analysis of the algorithm's performance on various state-of-the-art superconducting and trappedion quantum devices, including studies on qubit connectivity, fidelity comparisons, and hardware compatibility and robustness. Our results offer preliminary insights into potential practical applications of the HHL algorithm enabled by LuGo and the performance of various types of quantum hardware.11Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231.
The Second Quantum Revolution—comprising Quantum Computing, Quantum Communications, and Quantum Sensing—holds the promise of further abilities to improve the human condition, security, and sustainability, and hence, mass prosperity, across the world, in a variety of ways, including improving health (through better diagnosis as well as new drug development, via exciting applications of Quantum Machine Learning and Quantum Sensing) as well as by enhancing communication and cybersecurity. For certain computational workloads and sensing tasks, quantum systems have demonstrated or projected energy advantages over purely digital methods at equivalent precision, although system-level comparisons remain an active and unsettled research area. Seen through a data-science lens, quantum technologies are not only physical systems but also data-generating systems whose calibration, validation, and control depend on statistical inference, hypothesis testing, and learning under uncertainty. Integer programming, queuing, Markov decision processes, and semidefinite programs are some of the fundamental methodologies in operations research (OR) and management science (MS) that are used to tackle practical applications from business (supply chain, finance), engineering (communication networks), and medicine (cancer genomics, image recognition). At the Tepper Quantum Technologies Group, we are exploring the twin questions: (a) What can quantum do for OR/MS; and (b) what can OR/MS do for quantum. This article—a companion to my 2025 INFORMS Keynote and not intended as a comprehensive survey of the field but rather as a selective perspective organized around illustrative examples from our group—provides a brief summary of our research spanning algorithms, hardware, and applications (AHA). I hope that it helps illustrate how we can contribute to the Second Quantum Revolution. History: Yu Ding served as the senior editor for this article.