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.
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.
Quantum technologies are moving from foundational science toward engineered systems in sensing and metrology, computing and simulation, communication and networking, and security. The transition is uneven in technological maturity: atomic clocks and several quantum sensors are operational; quantum key distribution has reached field deployment but remains constrained by integration, assurance and network architecture; post-quantum cryptography is entering standards-based migration; and general-purpose fault-tolerant quantum computing remains an engineering objective despite major progress in error correction. This treatise develops a mathematically grounded and application-oriented account of what quantum technologies are, what physical resources they exploit, what advantages have been proved or demonstrated, and where claims remain speculative. It distinguishes among scientific feasibility, engineering readiness, operational utility, economic value and societal legitimacy. Across fundamental physics, Earth and space sciences, environmental monitoring, health and life sciences, cybersecurity, critical infrastructure, hazards, defense, finance, decision support and AI-enabled systems, this work identifies bottlenecks and proposes a systems roadmap for robust, scalable, efficient, verifiable and responsible quantum-enabled capabilities.
Quantum Mechanics has revolutionized not only our understanding of Nature but also, being the foundation of electronics and lasers, virtually all the instruments we use every day. Then again, we have not yet been able to harness the consequences of the most profoundly quantum phenomena such as state superpositions and particle entanglement. The Second Quantum Revolution has the ambition of building novel "quantum machines" able to fully exploit the properties of both microscopic and macroscopic quantum states. Quantum sensors, making use of the phenomenon of entanglement in systems promise to reach the fundamental measurement limits determined by the laws of physics and correspondingly improve the current performance of the sensors by orders of magnitude in terms of precision and accuracy, with important application implications in the scientific, industrial, and commercial fields. They can measure with unprecedented precision a wide class of physical quantities, such as magnetic, electric, and inertial fields, times, frequencies, temperatures and pressures.
The merger between Quantum Computing (QC) and Machine Learning (ML) will enable health analytics to be experienced in a way never seen before with unmatched efficiency and power. Conventional methods of healthcare data processing have not been able to work with very big and complex data sets that are related to medical imaging, genomics, and real-time tracking of the patients. Quantum Machine Learning (QML) has an advantage, as it uses new algorithms to work with such multidimensional data with much better accuracy, speed, and predictive power. This paper presents the essential contribution of QML to healthcare analytics, such as supporting diagnosis, to personalized medicine and predictive modelling. This quantum enhanced deep learning models will achieve what is currently being done at the larger scale in the field of medicine at a faster rate a thousand times, through earlier detection of diseases like cancer, Alzheimer disease and heart diseases. In drug discovery, quantum algorithms decrease the simulation of molecular interactions down to the atomic scale, which saves a substantial amount of time and cost of research. Security is, nevertheless, a massive issue since future developments in quantum theory will generate quantum-wise systems that will bring misfortunes to conventional encryption solutions adopted within healthcare systems. The implications of post-quantum cryptography that ensures the strong data privacy and safe patient records in quantum-powered ecosystem are discussed. Scalability obstacles, systems creation, and integration with traditional ML models, however, exist. Real-world applications and future perspectives on the quantum-enhanced healthcare analytics upon which plans have already been laid are presented. Looking ahead, QML will build in the transition of healthcare systems toward analytics frameworks faster, much more precise, and enhanced secure, heralding in the day of precision medicine early diagnostics and a new age for healthcare innovation.
This survey report presents an overview of the field of quantum computing, focusing on the application of quantum technologies in machine learning, deep learning, image processing, and other related areas. A comprehensive analysis of research papers published between 2005 and 2022 is conducted, covering the latest advancements in quantum-based techniques. The report provides insights into various types of quantum technologies, including quantum machine learning, deep learning, computing, learning, and image processing. The survey encompasses a wide range of topics, such as quantum algorithms, quantum data encoding, quantum feature extraction, quantum neural networks, quantum image recognition, quantum optimization, and quantum simulations. It also identifies the emerging trends, challenges, and future research directions in this rapidly evolving field. The findings of this survey report are expected to provide valuable insights for researchers, practitioners, and decision-makers interested in the field of quantum and its applications in machine learning, deep learning, image processing, and related areas. Comprehensive analysis of the research papers and the identification of the current trends and challenges in this field can serve as a valuable reference for future research and development in the quantum computing domain.
Although Artificial Intelligence (AI) has had a huge influence on organizations, its capabilities are getting close to reaching a bottleneck for some complicated jobs. Utilizing quantum computing (QC), the emerging quantum AI (QAI) discipline provides a novel approach. This chapter reveals how QAI can revolutionize the way businesses operate. First, we dispel the myths surrounding qubits, superposition, and entanglement, which are the fundamental ideas of quantum mechanics. Those ideas permit QAI to resolve issues that conventional AI can’t clear up. We take a look at how QAI gives improved trouble-fixing abilities to forms and businesses, particularly in computationally demanding domains like drug research and chemical science. This chapter additionally explores how QAI complements information evaluation. Corporations can use QAI to analyze large datasets at formerly unheard-of speeds and ranges of efficiency, for you to improve decision-making in areas such as financial modeling, marketplace forecasting, and logistics optimization. Moreover, QAI transforms machine learning by speeding up the training of complex models, establishing the door for advanced AI programs in a variety of sectors. We present real-world examples from various industries, such as pharmaceutical research, financial risk management, cybersecurity, and logistics to demonstrate the practical uses of QAI. It is imperative to understand the present constraints of QAI technology, including challenges in error correction and hardware complexity. We also discussed ongoing research and development initiatives aimed at creating more dependable QAI systems and briefly addressed the ethical considerations surrounding their use. This chapter concludes by highlighting QAI’s ability to revolutionize business. QAI may transform several sectors by providing enhanced capabilities for data analysis, machine learning, and problem-solving. To obtain a competitive advantage in the future, we advise organizations to stay up-to-date and explore the potential of this emerging technology.
There are signs that we are approaching a technical revolution that might take humankind’s computational capabilities to a new level. Quantum Computing leverages the principles of quantum mechanics, enabling the computational power to solve specific problems currently intractable for classical computers (e.g., cracking cryptographic keys). Since we are getting to a point where we can no longer build smaller, more powerful, more efficient devices with conventional methodologies, we need to think of new ways for technological progress. The manufacturing of microchips is reaching its limits in the use of traditional architectures. Hence, new and more advanced technologies need to address the challenges of increasing energy consumption and data processing. Connectivity, scalable architectures and concepts like Cloud Computing and Software-as-a-Service have already helped to scale computational-intense applications in the last years and have attracted the interest of IS researchers (e.g., Benlian et al. 2009 ; Benlian et al. 2018 ; Messerschmidt and Hinz 2013 ). Looking ahead to the next generation, computing will comprise technologies that enable high-performance applications far beyond today’s possibilities. One prominent example with a disruptive potential is Quantum Computing. While there are abundant open questions and directions for further developments, researchers in this area have made substantial progress in the last decade, indicating that, after medical imaging, lasers, and superconductors, another quantum-technological innovation might be approaching. We, therefore, intend to provide an overview of the current state of Quantum Computing research and outline how this new technology might stimulate research in Information Systems.