A quantum computer exhibits quantum advantage when it can perform a calculation that a classical computer is unable to complete. It follows that a company with a quantum computer would be a monopolist in the market for such a calculation if its only competitor was a company with a classical computer. Conversely, economic outcomes are unclear if quantum computers do not exhibit a quantum advantage, but classical and quantum computers have different cost structures. We model a Cournot duopoly where a quantum computing company competes against a classical computing company. The model features an asymmetric variable cost structure between the two companies and the potential for an asymmetric fixed cost structure, where each firm can invest in scaling its hardware to expand its respective market. We find that even if (1) the companies can complete identical calculations, and thus there is no quantum advantage, and (2) it is more expensive to scale the quantum computer, the quantum computing company may be more profitable and also invest more in market creation due to efficiency gains from using quantum algorithms. Finally, we provide examples of settings where the classical computer can also perform a calculation, but not in a cost-effective enough manner to be commercially viable. In such a setting, the quantum computing company becomes a monopolist despite exhibiting no quantum advantage. Taken together, quantum computers may not need to display a quantum advantage to be able to generate a quantum economic advantage for the companies that deploy them. This paper was accepted by D. J. Wu, information systems. Funding: R. G. Melko is supported by the Natural Sciences and Engineering Research Council of Canada, Canada Research Chair program, and the Perimeter Institute for Theoretical Physics. Research at the Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development Canada and by the Province of Ontario through the Ministry of Colleges and Universities. A. Goldfarb is supported by the Sloan Foundation and the Social Sciences and Humanities Council of Canada. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4578 .
A quantum computer exhibits quantum advantage when it can perform a calculation that a classical computer is unable to complete. It follows that a company with a quantum computer would be a monopolist in the market for such a calculation if its only competitor was a company with a classical computer. Conversely, economic outcomes are unclear if quantum computers do not exhibit a quantum advantage, but classical and quantum computers have different cost structures. We model a Cournot duopoly where a quantum computing company competes against a classical computing company. The model features an asymmetric variable cost structure between the two companies and the potential for an asymmetric fixed cost structure, where each firm can invest in scaling its hardware to expand its respective market. We find that even if (1) the companies can complete identical calculations, and thus there is no quantum advantage, and (2) it is more expensive to scale the quantum computer, the quantum computing company may be more profitable and also invest more in market creation due to efficiency gains from using quantum algorithms. Finally, we provide examples of settings where the classical computer can also perform a calculation, but not in a cost-effective enough manner to be commercially viable. In such a setting, the quantum computing company becomes a monopolist despite exhibiting no quantum advantage. Taken together, quantum computers may not need to display a quantum advantage to be able to generate a quantum economic advantage for the companies that deploy them. This paper was accepted by D. J. Wu, information systems. Funding: R. G. Melko is supported by the Natural Sciences and Engineering Research Council of Canada, Canada Research Chair program, and the Perimeter Institute for Theoretical Physics. Research at the Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development Canada and by the Province of Ontario through the Ministry of Colleges and Universities. A. Goldfarb is supported by the Sloan Foundation and the Social Sciences and Humanities Council of Canada. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4578 .
Quantum computing represents one of the most disruptive technological paradigms of the 21st century, with profound implications for economic modelling, financial optimization, and computational decision-making. Unlike classical computing, quantum systems leverage superposition, entanglement, and interference to solve problems of exponentially higher complexity, a capability with transformative potential for economics and finance. This study examines the intersection of quantum computing and economics, systematically evaluating both the promises and the barriers defining this emerging field. Regarding opportunities, quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA) and Grover's algorithm, demonstrate significant theoretical advantages in portfolio optimization, risk assessment, option pricing, and the resolution of large-scale combinatorial problems that are intractable for classical machines. Quantum machine learning extends these prospects, offering accelerated capabilities for pattern recognition and predictive modelling relevant to financial markets. However, substantial barriers temper these expectations. Current quantum hardware operates in the NISQ (Noisy Intermediate-Scale Quantum) era, where decoherence, gate errors, and a limited number of qubits constrain practical applicability. The gap between theoretical quantum advantage and real-world economic implementation remains significant, compounded by a shortage of specialists, high infrastructure costs, and the absence of standardized programming frameworks tailored to economic applications. Through a bibliometric analysis of existing literature and a critical review of recent experimental results, the study maps the current state of quantum computing adoption in economics, identifies the most active research frontiers—quantum game theory and quantum portfolio optimization—and outlines the conditions under which quantum advantage could become economically relevant. The findings suggest that, although quantum computing will not transform economic practice in the short term, its long-term disruptive potential warrants proactive engagement from economists, financial institutions, and policymakers.
Open-source projects that aim to make their offerings public have competed against for-profit, proprietary companies in a number of domains. These open-source projects often arise in response to the offerings of proprietary companies in markets where products have already been commercialized. We assess what impact an open-source project might have when it enters the market for quantum computing - a market where the core technology is still being developed. We argue that an open-source quantum computer might alleviate market frictions that have impeded the development of a fault-tolerant quantum computer by providing the market with a possible mechanism for: 1.) benchmarking, 2.) the development of hardware agnostic technology, and 3.) improved liquidity on the supply side of the market for labor. Should these outcomes be realized, they may not only benefit the open-source project, but also the for-profit, proprietary companies operating in the ecosystem. In this respect, an open-source quantum project may play a more complementary role to the proprietary firms in the ecosystem, rather than a more competitive role. Our collective insights may have implications for other settings where an open-source project enters into a market where the core technology has yet to be commercialized.
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.
Developing a systematic view of where quantum computers will outperform classical ones is important for researchers, policy makers and business leaders. But developing such a view is challenging because quantum advantage analyses depend not only on algorithm properties, but also on a host of technical characteristics (error correction, gate speeds, etc.). Because various analyses make different assumptions about these technical characteristics, it can be challenging to make comparisons across them. In this paper, we introduce an openaccess web-tool designed to make such comparisons easy. Built on the framework introduced by Choi, Moses, and Thompson (2023), it calculates when quantum systems will outperform classical computers for a given algorithmic problem. These estimates can be easily updated based on various assumptions for error correction, overhead, and connectivity. Different hardware roadmaps can also be used and algorithm running times can be customized to particular applications. It can currently be accessed at https://futuretech.mit.edu/quantum-economic-advantage-calculator. This integrated prediction tool also allows us to explore which technical factors are most important for quantum “economic” advantage (outperforming on a cost-equivalent basis). Overall, we find that for some algorithms (e.g. Shor's) the timing of advantage is quite robust, whereas for others (e.g. Grover's) it is contingent, with numerous technical characteristics substantially impacting these dates. In the paper, we discuss both why this occurs and what we can learn from it.
Abstract Quantum computing leverages fundamental quantum-mechanical phenomena—most notably superposition, entanglement, and quantum tunneling—to perform computations in ways that differ fundamentally from classical digital processing. Compared to conventional computing architectures, quantum systems promise significant reductions in computational time and potentially lower energy consumption for specific classes of problems. These advantages, combined with advances in nanoscale engineering and the slowing of traditional hardware scaling trends such as Moore’s Law, have led to substantial commercial and national-security-driven investments in quantum technologies over the past decade. A major milestone was reached when Google announced the experimental demonstration of “quantum supremacy,” showing that a quantum processor could execute a specific computational task infeasible for a classical supercomputer within practical time constraints. Although the demonstrated problem had no immediate practical application, this achievement marked a crucial step toward the development of quantum systems capable of delivering real-world computational advantages and provided a more precise technical framing of the concept of quantum advantage. Despite the fact that the mathematical foundations, programming models, and principal quantum algorithms were established in the 1990s, quantum computing remained largely confined to a specialized research community for decades. Today, the increasing accessibility of cloud-based quantum hardware calls for a broader dissemination of implementation-oriented knowledge among researchers and practitioners in computer science, software engineering, and related fields. The quantum programming paradigm differs fundamentally from classical programming models and often relies on physical and algebraic notations that may pose barriers to traditionally trained computer scientists. This series of articles provides a self-contained and concise introduction to quantum computing and its core algorithms, with a strong emphasis on practical implementation. Given that real quantum processors—such as those accessible via the IBM quantum ecosystem—are now available as cloud services, experimental validation can be conducted both on simulators and on actual hardware for small-scale inputs. While other platforms such as Rigetti and IonQ also offer cloud-based quantum computing services, this review focuses exclusively on the IBM quantum computing ecosystem.
Acknowledgements: AB acknowledges financial support from the European Research Council (ERC Grant Agreement No. 81876) and VILLUM FONDEN via the QMATH Centre of Excellence (Grant No.10059). Moreover, AB is supported by the French National Research Agency in the framework of the “France 2030” program (ANR-11-LABX-0025-01) for the LabEx PERSYVAL. AC acknowledges the support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 470903074 – TRR 352. APH is partially supported by the Escuela Técnica Superior de Ingenieros Industriales (UNED) of Spain, project 2023-ETSII-UNED-01, as well as by the Spanish Ministerio de Ciencia e Innovación project PID2020-113523GB-I00 and by Comunidad de Madrid project QUITEMAD-CM P2018/TCS4342. AC and AB are grateful for the hospitality of Perimeter Institute where part of this work was carried out. Research at Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development and by the Province of Ontario through the Ministry of Colleges and Universities. This research was also supported in part by the Simons Foundation through the Simons Foundation Emmy Noether Fellows Program at Perimeter Institute.