But as the government works with industry and academia to expand R&D and maintain U.S. leadership in this arena, are there quantum computing (QC) service offerings and use cases available either today or in the near-term future to advance federal missions and business operations?
While some debate exists about what qualifies as general-purpose QC and when it will become readily available, more targeted cloud-based offerings are commercially available today. By focusing on more limited use cases, providers can industrialize the operating environment to achieve the stability needed to deliver reliable problem solving. These include offerings from both QC pure-plays like IBM, Rigetti, and D-Wave as well as cloud leaders AWS, Microsoft Azure and Google Cloud Platform.
“Real quantum computers exist and can be used to solve meaningful problems,” notes IDC in the analyst firm’s Worldwide Quantum Computing Forecast, 2021–2025 report. “However, the underlying technology is still not ready for large-scale production and requires exceptionally stringent operating conditions to deliver stable outcomes and only the top IT vendors and service providers can afford to build and maintain them.” IDC further points out that within the next decade, QC technology “will be closer to large-scale consumption and be suited for solving problems so complex that no amount of classical compute, even in the shape of accelerated supercomputers, could solve them.”
Many of the QC use cases today across all industry sectors involve what is known as quantum annealing, which concern the solving of discreet combinatorial optimization problems. Optimization problems search for the best of many possible combinations. This might involve, for example, finding greater efficiencies in scheduling or supply chains. Sampling problems involve building a probabilistic model of reality, typically for machine learning applications. Samples of data inform an algorithm about the model state for a given set of parameters, which can then be used to improve the model. Probabilistic models explicitly handle uncertainty by accounting for gaps in knowledge and errors in data sources. While quantum annealers are among the preferred types of QC technologies employed today, so too are quantum algorithms, cloud-based quantum computing, and quantum simulators.
Optimization challenges are particularly prevalent in the financial service and manufacturing industries, where much of today’s early QC activity is occurring. In the financial sector, companies are employing QC for credit and asset scoring, derivative pricing, portfolio management, fraud detection, investment risk analysis, and portfolio management, among others. Similar use cases would apply as well to federal agencies that are heavily financial — for example, the departments of Agriculture and Treasury, the Federal Reserve Board, the Federal Deposit Insurance Corp., and the Securities and Exchange Commission.
In manufacturing, current QC use cases include fabrication optimization and process planning, manufacturing supply chain, materials and chemistry discovery, structural design, fluid dynamics, aircraft design optimization, autonomous vehicle navigation, battery simulation, and robotics. Similar use cases would apply to fleet optimization, Defense Department agencies such as the Armed Services’ material and systems commands, the Defense Logistics Agency, and the Missile Defense Agency, among others.
But many ripe use cases exist as well in the fields of healthcare and life sciences, energy, distribution and logistics, transportation, and IT services, most of which would have relevance for federal agencies.