Quantum computing potentially enables the acceleration of key problems in science, engineering, and industry. However, between domain experts capable of supplying relevant use cases and quantum computing researchers possessing the expertise to implement novel solutions, there exists a 'know how-knowledge gap'. Therefore, methodologies need to be developed to identify quantum computing relevant use cases by bridging the divide and enabling co-development for near-term quantum hardware. We present a strategy using short-term, results oriented projects to quickly gauge the potential of proposed use cases to find opportunities for robust future quantum advantage.
Quantum computing potentially enables the acceleration of key problems in science, engineering, and industry. However, between domain experts capable of supplying relevant use cases and quantum computing researchers possessing the expertise to implement novel solutions, there exists a 'know how-knowledge gap'. Therefore, methodologies need to be developed to identify quantum computing relevant use cases by bridging the divide and enabling co-development for near-term quantum hardware. We present a strategy using short-term, results oriented projects to quickly gauge the potential of proposed use cases to find opportunities for robust future quantum advantage.
Identify, Transform, Benchmark, Show Quantum Advantage (ITBQ): Evaluating use cases for quantum computers. We introduce a four-step framework for assessing quantum computing applications -- from identifying relevant industry problems to demonstrating quantum advantage -- addressing steps often overlooked in the literature, such as rigorous benchmarking against classical solutions and the challenge of translating real-world tasks onto quantum hardware. Applying this framework to cases like NMR, multireference chemistry, and radicals reveals both significant opportunities and key barriers on the path to practical advantage. Our results highlight the need for transparent, structured criteria to focus research, guide investment, and accelerate meaningful quantum progress.
Quantum computing has been rapidly evolving as a field, with innovations driven by industry, academia, and government institutions. The technology has the potential to accelerate computation for solving complex problems across multiple industrial sectors. Finance and economics, with many problems exhibiting computationally heavy requirements, comprise a high-profile sector where quantum computing could have a significant impact. Therefore, it is important to identify and understand to what extent the technology could find utility in the sector. This technical review is written for quantum applications researchers, quantitative analysts in finance and economics, and researchers in related mathematical sciences. It is divided into two parts: (i) a survey of quantum algorithms pertinent to problems in finance and economics, and (ii) mapping of several use cases in the sector to the potential quantum algorithms presented in part (i). We discuss some challenges on the pathway to achieving quantum advantage. Ultimately, this review aims to be a catalyst for interdisciplinary research that will accelerate the advent of the practical advantages of quantum technologies to solve complex problems in this sector.
Quantum Computing (QC) is transitioning from theoretical frameworks to an indispensable powerhouse of computational capability, resulting in extensive adoption across both industrial and academic domains. QC presents exceptional advantages, including unparalleled processing speed and the potential to solve complex problems beyond the capabilities of classical computers. Nevertheless, academic researchers and industry practitioners encounter various challenges in harnessing the benefits of this technology. The limited accessibility of QC resources for classical developers, and a general lack of domain knowledge and expertise, represent insurmountable barrier, hence to address these challenges, we introduce a framework- Quantum Computing as a Service for Hybrid Classical-Quantum Software Development (QCSHQD), which leverages service-oriented strategies. Our framework comprises three principal components: an Integrated Development Environment (IDE) for user interaction, an abstraction layer dedicated to orchestrating quantum services, and a service provider responsible for executing services on quantum computer. This study presents a blueprint for QCSHQD, designed to democratize access to QC resources for classical developers who want to seamless harness QC power. The vision of QCSHQD paves the way for groundbreaking innovations by addressing key challenges of hybridization between classical and quantum computers.
Recently, there has been increasing awareness of the tremendous opportunities inherent in quantum computing (QC). Specifically, the speed and efficiency of QC will significantly impact the Internet of Things, cryptography, finance, and marketing. Accordingly, there has been increased QC research funding from national and regional governments and private firms. However, critical concerns regarding legal, political, and business-related policies germane to QC adoption exist. A few relevant studies are currently available, and those few focus heavily on the technical side of QC. Thus, this study offers a multidisciplinary review of QC, drawing on the expertise of scholars from a wide range of disciplines whose insights coalesce into a framework that simplifies the understanding of QC. This study offers a timely contribution to both practitioners and academia, as it explores use cases in business, addresses fundamental legal and political issues undermining QC’s adoption, and highlights several research gaps critical to advancing knowledge within the field.
The increasing maturity of Quantum Computing (QC) and quantum hardware hasbrought Quantum Software Design (QSD) into focus as a distinct research concernwithin Quantum Software Engineering (QSE), yet no prior study has systematicallymapped its approaches, challenges, and opportunities. This thesis addresses thatgap through two complementary research phases: a Systematic Literature Review(SLR) of 32 studies published between 2006 and 2025, and semi-structured inter-views with four domain experts. Both phases confirm that quantum-classical hybrid design is the dominant approach in current QSD research, and that the adaptationof classical tools, particularly Unified Modeling Language (UML) and Model-DrivenEngineering (MDE), is the primary methodological strategy. Hardware-software co-design is identified as a necessary design concern under Noisy Intermediate-Scale Quantum (NISQ) constraints; two further directions absent from the published liter-ature were identified by practitioners: AI-assisted QSD and self-adaptive quantumsystems. The challenge analysis reveals a potential causal sequence of challenges in the field. Human resources challenges, specifically, the bidirectional knowledge gap between software engineers and quantum computing researchers, emerge exclusively from the interviews and are absent from all 32 reviewed studies. MDE is the most convergent near-term opportunity, and the formalization of human resources and educational pathways represents a new research direction not addressed in the existing literature.
The emergence of quantum computing represents a paradigm shift in the world of technology, promising unprecedented computational power to tackle complex problems that were once considered insurmountable. As quantum computing technologies continue to advance, organizations across various industries are eager to harness this potential. However, the transition to quantum computing is not a mere plug-and-play affair. It requires a systematic and thoughtful approach to seamlessly integrate quantum capabilities into existing systems, processes, and workflows.