Quantum computing (QC) is a technology with enormous potential, but for the moment, it is one only of potential. Despite the considerable amount of QC research and development (R&D) underway, to date, no economically meaningful use cases have been demonstrated. Over time, quantum computing is likely to follow Amara’s law regarding technology forecasting—its uses and benefits will be overestimated for the near-term and underestimated for the long-run.1 This report focuses on the near-term. It evaluates potential near-term QC applications as well as the prospect of using public-private partnerships (PPPs) to accelerate the time horizon for meaningful applications of quantum computing.
Numerous proof-of-concept applications for quantum computing have been explored and reported. There is consensus that many applications, especially the more ambitious, will require true fault-tolerant quantum computers with large (on the order of 100-1,000) qubit counts2 to run effectively. It is unknown when such fault-tolerant computing will be available. Before then, it is possible that algorithms running on noisy intermediate-scale quantum (NISQ) computers, including quantum annealers, may compete favorably against classical approaches, especially for optimization and selected machine learning (ML) applications. It is also unclear when this may happen. As a proof of concept, it may happen within the next three years. A review of the QC application literature indicates that on an economic basis, it is unlikely to happen within the next three years.
This is due both to the technological uncertainty surrounding QC development paths and to the fact that the classical computing approaches against which quantum computing must compete are continuously improving. They are a moving target. Quantum computing will only be useful when it can surpass the performance of the very best classical methods for a given computational problem. And it cannot merely be marginally better. It must be dramatically better if organizations are to incur the significant switching cost and potentially dramatic changes to commercial, industrial, and government workflow required to migrate from classical to quantum computing processes.
The combined technology and market uncertainty means that predicting when or even if economically meaningful QC applications will appear is impossible. Any forecast regarding quantum computing is inherently speculative. Nevertheless, several areas are believed to have the greatest potential for near-term use. These areas include:
- Chemicals and materials
- Manufacturing and warehousing
- Logistics, supply chain management, traffic, and route management
- Financial fraud detection
Many of these use cases rely on hybrid quantum-classical approaches to computation. Hybrid approaches perform much of their computation with classical computing hardware and reserve valuable qubits for the parts of each problem for which they are most necessary. Hybrid approaches represent a way to potentially accelerate the near-term use of quantum computing as they alleviate the need for fully scaled all-quantum systems.
Against the backdrop of the state of QC R&D and its uncertain future, SRI International (SRI) examined the history and performance of nine PPPs established to help accelerate technology development. These PPPs cover a range of science and technology (S&T) areas. Each partnership includes a significant role for government participants. Their success comes from setting clear goals that are understood by all partnership participants, aligning these goals with important government missions, and translating top-level goals into more detailed objectives and workplans. Based on the state of QC R&D and the experience of these partnerships, SRI proposes three PPP models that the federal government should consider adopting to accelerate QC uses. The models are meant to complement each other by addressing different aspects of the QC development challenge.
The most effective way to identify a set of potential near-term QC applications of value to government is through a discovery process that involves cooperation among all stakeholders, from quantum scientists to domain subject matter experts to end users to regulators. Accordingly, the federal government should consider establishing a PPP or leveraging an existing PPP (e.g., QED-C) whose mission is to find possible near-term QC applications by facilitating planned interaction and cooperation among QC hardware and software experts, application domain experts, user communities, and policy and market experts. Such a partnership should be organized thematically around a significant area of public interest, such as climate and sustainability or public health, where there is an emerging critical mass of quantum R&D already underway. The partnership’s goals would not be defined in terms of achieving real-world applications in a given number of years. Rather its objective would be to evaluate possible application areas in much more depth than can be done via a survey of the literature, thus identifying strategies to advance the date at which real-world progress can be made.
Looking more narrowly, government-sponsored challenges have demonstrated their effectiveness in accelerating the development of technology intended for use in government mission areas. The iterative approach to competition allows the government to revise timelines and objectives in response to participant progress and improved understanding of what is technologically possible, an attribute suited to QC use case development. The U.S. federal government should consider organizing a QC challenge. The targeted challenge should focus on an area with (a) clear government mission relevance, (b) active interest by the private sector, and (c) a critical mass of current QC research. Several areas described in the near-term applications section meet these criteria. Financial fraud detection stands out given the level of interest on the part of private-sector financial services firms in quantum computing for fraud detection and the enormous amount of real-world data available with which to experiment and develop quantum machine learning (QML) anomaly and fraud detection tools.
In addition to application-focused partnerships, the federal government should consider supporting a PPP focused on addressing the underlying technology development challenges of quantum computing in a manner similar to the Department of Energy’s (DOE) Innovation Network for Fusion Energy (INFUSE) program. INFUSE awards are intended to help solve specific challenges related to fusion-enabling technology development and are awarded based on the expected impact of proposed projects on the overall progress of fusion energy R&D. Most of the technical work is performed by DOE laboratories. An INFUSE model for quantum computing would include participation of DOE’s Quantum Information Science (QIS) Research Centers and other participants from the private sector and academia and, in effect, would create focused PPPs for each approved project. Such a partnership would not address specific QC applications but the development of enabling technologies in technical areas such as qubit control, error correction, cryogenics, and system scaling, areas with broad, pre-competitive application.
1 Roy Charles Amara was an American scientist and President of the Institute for the Future who also worked at the Stanford Research Institute (later SRI International).
2 McKinsey indicates early- and late-stage fault tolerance at 100 and 1,000 logical qubits respective. See: McKinsey & Company, “Quantum Computing: An Emerging Ecosystem and Industry Use Cases,” (December 2021).