{"id":22448,"date":"2026-05-07T11:01:43","date_gmt":"2026-05-07T11:01:43","guid":{"rendered":"https:\/\/ideainthebox.com\/index.php\/2026\/05\/07\/why-businesses-should-experiment-with-quantum-computing-now\/"},"modified":"2026-05-07T11:01:43","modified_gmt":"2026-05-07T11:01:43","slug":"why-businesses-should-experiment-with-quantum-computing-now","status":"publish","type":"post","link":"https:\/\/ideainthebox.com\/index.php\/2026\/05\/07\/why-businesses-should-experiment-with-quantum-computing-now\/","title":{"rendered":"Why Businesses Should Experiment With Quantum Computing Now"},"content":{"rendered":"
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Matt Chinworth\/theispot.com<\/p>\n<\/figcaption><\/figure>\n<\/p>\n

Executives tracking the latest news<\/span> about quantum computing might conclude that with technical milestones still to be reached, the prudent approach is to watch and wait before investing. But that overlooks what other, bolder companies recognize: Quantum computing is an enabling technology, and user organizations have a critical role to play in shaping how it will create value.<\/p>\n

Headlines about new chips, qubit (quantum bit) counts, and error correction suggest that the key question is whether quantum computing has reached the point where it can outperform classical computers on practical problems. This framing leads to a familiar strategic dilemma: \ufeffWait until the technology is clearly \u201cready\ufeff,\u201d or risk getting involved too early in something that may not pay off. Qubit counts and error rates are appropriate engineering milestones, but they are a poor guide to how and when most companies should engage. For technologies like quantum computing, economic value does not arrive all at once, when a technical threshold is crossed. It emerges gradually, through experimentation, complementary innovation, and organizational learning, often well before the technology is fully mature.<\/p>\n

Like other enabling technologies, quantum computing will not generate much economic value on its own. Instead, its economic value will emerge through repeated cycles of co-invention between the technology-producing sector and complementary innovations developed by users in application settings. Technologies with the highest enabling potential are referred to as general-purpose technologies.<\/p>\n

Electricity is one such general-purpose technology. Its development and diffusion depended on continuous co-invention between producers and users: Early innovations in power generation prompted downstream experimentation in lighting, motors, appliances, and factory layouts, and those downstream innovations in turn reshaped what kinds of power generation and distribution systems were needed upstream. Classical computers followed a similar pattern: Progress in hardware depended on complementary innovations in software, data storage, and organizational processes, while advances in those complements fed back into hardware design by advancing performance requirements.<\/p>\n<\/p>\n

Quantum computing fits this same pattern. Its economic impact will not come suddenly, after passing a particular technical threshold, nor will it diffuse as a plug-and-play tool. Instead, value will be created through feedback loops in which user experimentation reveals near-term economic opportunities, what levels of performance matter, and what processes and skills are required to generate value. The practical challenge for company leadership is therefore not predicting which quantum computing vendors will reach certain technical milestones; rather, it is developing the organizational capability to decide when and how to participate in these co-invention cycles, by translating business problems into quantum computing use cases and strategically redesigning processes as the technology evolves.<\/p>\n

The Managerial Dilemma<\/h3>\n

When viewed as an enabling technology, quantum computing presents managers with a distinct set of challenges. Value creation depends on active engagement from downstream users, because experimentation in application settings clarifies which technical progress should be prioritized. At the same time, companies\u2019 incentives to engage are weak because the path to capturing value from such experimentation is uncertain.<\/p>\n

For enabling technologies, the most significant economic benefits rarely appear in the short run or in the first applications explored. They accumulate over time, as learning from early experiments informs more meaningful applications down the line. For example, significant returns from electricity accrued only after several cycles of co-invention, with the emergence of electric motors in manufacturing plants and household appliances. A similar pattern played out with the internet. Early uses focused on basic connectivity and information sharing, but the largest economic gains emerged later, after complementary innovations in software, data, and organizational processes enabled new business models, such as e-commerce, digital platforms, and cloud-based services.<\/p>\n

This makes early value capture hard to assess. Companies that develop early use cases for quantum computing may not be able to secure long-run advantages, considering that the most valuable use cases likely have not yet been discovered.1<\/a> This could raise concerns that the value generated through early experimentation may be captured by others. For example, a great deal of the value created by internet technologies was captured by companies such as Google and Meta.<\/p>\n

When companies hesitate to engage in early experimentation, the result is persistent uncertainty about what customers value. Developers already face uncertainty about how to achieve a technically viable system, but what counts as viable depends on which applications potential customers prioritize and what performance thresholds matter in practice, especially in the near term. When downstream companies do not engage, upstream producers lack clear signals about what to optimize for, potentially slowing technical progress. When the technology remains immature, downstream companies struggle to specify concrete use cases that would justify experimentation and near-term investment. The result is a catch-22: Near-term value-capture uncertainty sustains demand uncertainty and discourages co-invention, which in turn makes it harder to resolve technological uncertainty. Companies want proof before experimenting, but proof often arrives only because companies experiment.<\/p>\n<\/p>\n

But near-term uncertainty about capturing value is not a reason to delay. Because quantum computing is an enabling technology, co-invention processes shape how the technology develops. The feedback loops between producers and users imply that who engages and what problems they choose influence which applications become technically feasible early on. Engaging early gives companies opportunities to influence which performance dimensions are prioritized and to identify future complementarities. For example, financial services companies that engage early will shape the direction of innovation in quantum computers and related software toward their needs. In this sense, managerial decisions about engagement are not just responses to technological progress but also inputs into the direction that progress takes. They are a mechanism for discovering how to adapt a company\u2019s assets and strategy to applications that will later generate higher value.<\/p>\n

Moreover, organizations that benefit most from enabling technologies are those that redesign their processes to take advantage of what technology makes possible, even though such actions are the most costly, slow, and difficult to evaluate in advance.2<\/a> Early uses often generate only incremental value. The largest gains, which typically come later, are generally enabled by companies that envision new processes.<\/p>\n<\/p>\n

Experimentation in Quantum Computing<\/h3>\n

What will arise that will allow companies to capture significant value from quantum computing is still an open question. Today, companies need to experiment and learn. Such experimentation is already happening.3<\/a> One early example is Lockheed Martin\u2019s decision to move from watching quantum progress to engaging in hands-on experimentation. In 2011, after having spent about a year evaluating the technology, Lockheed entered a multiyear agreement for a D-Wave One system, a 128-qubit quantum annealing machine. Reporting at the time valued the deal at roughly $10 million, including support and maintenance. Rather than treating the D-Wave One as a turnkey product, Lockheed helped establish the USC-Lockheed Martin Quantum Computing Center at the University of Southern California\u2019s Information Sciences Institute, giving researchers and engineers sustained access to the system and a setting designed for iterative learning. The intent was not that a single installation would transform operations overnight. It was to build familiarity with what the machine could and could not do, to test problem formulations, and to identify where quantum approaches might eventually matter for practical problems.<\/p>\n

IBM pursued a different kind of experiment \u2014 one designed to scale learning beyond a single company. On May 4, 2016, it launched IBM Quantum Experience, a cloud service that offered users access to a 5-qubit quantum processor and a matching simulator to run their own experiments. Uptake was immediate. Roughly 7,000 users registered within the first week, and over 17,000 more registered the following week.4<\/a> Over time, the user base grew into the hundreds of thousands. This 5-qubit device was not commercially useful; it mattered because cloud access enabled broad engagement with a prototype, which accelerated downstream experimentation. The IBM cloud service launch was followed by several other\ufeff similar services, such as Alibaba Cloud\u2019s quantum computing platform, Rigetti\u2019s Forest, and D-Wave\u2019s Leap in 2018; Xanadu Cloud, the Honeywell System Model H1, and Amazon Braket became generally available in 2020.5<\/a><\/p>\n

Companies across industries have been using access to quantum computers to explore concrete problems before the technology has matured. Examples include a partnership between Airbus and 4colors Research on quantum optimization; the Port of Los Angeles and Fenix Marine Services\u2019 collaboration with D-Wave on cargo terminal operations; Volkswagen\u2019s work with D-Wave on traffic optimization; and Telef\u00f3nica Germany\u2019s partnership with Amazon to explore network optimization. Across these cases, the common pattern is not immediate operational transformation but experimentation that helps companies learn which problem formulations, workflows, and benchmarks matter.<\/p>\n<\/p>\n

These exploratory efforts can also lead to innovation that has immediate practical value for established systems. For example, investments in quantum computing have produced quantum-inspired innovations, such as algorithms that can be implemented on classical computing hardware.6<\/a> Other innovations include methods that improve the efficiency of recommendation systems, optimization routines, and materials discovery.7<\/a> Fujitsu\u2019s Digital Annealer is an example of quantum-inspired optimization methods being implemented on classical hardware. It has been used in areas such as distribution and warehouse operations to improve routing and part placement, in financial services to support portfolio optimization under complex constraints, and in manufacturing and logistics to enhance production planning and operational efficiency. Several other companies, including IBM and Google, have reported similar classical computing advances that emerged directly from their quantum computing efforts.<\/p>\n

Elements of a Quantum Strategy<\/h3>\n

Executives planning a quantum strategy should treat quantum computing as an enabling technology. Their objective should not be to time a technological breakthrough but to enable co-invention feedback loops and to reduce both technological and demand uncertainty through deliberate engagement. This involves building the organizational capacity to turn learning into action when a technological breakthrough occurs that makes the company\u2019s use cases possible. The focus should not be on when to adopt. Instead, it should be on learning, experimentation, and preparation for process changes over time. To that end, leaders should take the following steps.<\/p>\n

1. Develop boundary spanners linking quantum technology to company-specific problems. <\/strong>For companies outside the tech industry, keeping track of emerging technologies is always challenging. Such companies rarely employ experts in the underlying science, and so they must rely on outside signals to assess progress. This challenge is particularly acute with quantum computing. The technology is complex and unintuitive, and public narratives often oscillate between hype and skepticism. As a result, companies risk overestimating their readiness to work with quantum, underestimating the technology\u2019s eventual impact, or missing specific applications relevant for their industry that might not be highlighted in general-interest news coverage.<\/p>\n

Companies need to ensure that they have access to people who understand both the business and what the technology can do. Such boundary-spanning roles, sometimes filled by generalists who can connect insights across fields, are critical for translating technological progress into company-specific questions.8<\/a> For example: Which problems might become tractable? Which constraints matter most? What co-invention is needed? What kinds of performance improvements would justify investment? The employees assigned to this role can stay abreast of developments by attending relevant events, connecting with quantum technology organizations, and plugging into ecosystem initiatives.<\/p>\n

The goal of these initiatives is to connect quantum expertise with industry to shape the direction of quantum computing co-invention efforts in ways that will benefit user companies. For example, in 2025, the state of Maryland and the University of Maryland announced the $1 billion Capital of Quantum initiative, with IonQ positioned as an anchor partner. In the same ecosystem, Microsoft subsequently announced plans for a quantum research center in the University of Maryland\u2019s Discovery District. In Chicago, the Chicago Quantum Exchange connects major universities and national labs with industry partners, while the Illinois Quantum and Microelectronics Park is being built as a large-scale public-private site intended to host quantum companies and shared facilities. Elevate Quantum has been designated a regional tech hub and is developing the Quantum Commons as a 70-acre campus in Colorado intended to connect startups, industry, academia, and shared infrastructure. In Calgary, the University of Calgary\u2019s Quantum City partnership (with the Government of Alberta and an industry partner) was created to build infrastructure, talent programs, and adoption pathways, including a dedicated collaborative hub to connect R&D with implementation.<\/p>\n

2. Find near-term opportunities to anchor learning. <\/strong>A second strategic priority is to identify opportunities that make experimentation feasible. Some of these opportunities involve narrowly defined problems where even modest performance improvements would be valuable and integration into existing processes would be manageable. Other opportunities arise from quantum-inspired methods that run on classical hardware but emerge directly from engagement with quantum computing. These near-term opportunities rarely represent the largest long-run payoff, but they support important organizational learning.<\/p>\n

For example, recent research identified ways in which companies may derive economic benefits from quantum approaches even before the technology delivers clear technical superiority over classical computing.9<\/a> The key is to observe that the relevant comparison is not whether quantum computers can solve problems that classical computers cannot but whether they change the cost, speed, or resource intensity of solving near-term economically meaningful problems. When quantum-based or quantum-inspired methods alter these trade-offs, companies can justify experimentation and investment based on near-term economic value.<\/p>\n

Focusing on problems that matter today makes experimentation easier to justify and easier to organize. It creates incentives for teams to engage, provides concrete benchmarks for evaluating progress, and helps companies learn how quantum approaches differ from classical ones in practice. At the same time, working on near-term applications exposes organizations to new ways of formulating problems, paving the way for identifying complementarities and ideas for redesigned processes that will facilitate higher returns in the long run. In this sense, near-term opportunities are entry points that allow companies to build intuition about where quantum methods might offer advantages, how those advantages could translate into economic value, and where further experimentation might open up more consequential possibilities over time.<\/p>\n

3. Create space for longer-term experimentation and process innovation. <\/strong>Finally, companies need to recognize that the largest returns from enabling technologies typically come from changes in processes, not from incremental improvements to existing tasks. Classic research on technological change has found that established companies often struggle to benefit from emerging technologies not because they fail to recognize them but because they try to evaluate and implement them using the performance metrics, processes, and incentives of the existing business.10<\/a> As a result, promising new technologies are either underfunded, misapplied, or forced into use cases that fit current operations rather than future opportunities.<\/p>\n<\/p>\n

Companies must create spaces where experimentation with emerging technologies can proceed unencumbered by day-to-day operational metrics and short-term performance expectations. For example, in retail, the internet served as an enabling technology that generated most value when traditional brick-and-mortar processes were redesigned. Walmart invested early and persistently in e-commerce capabilities, data infrastructure, and digitally integrated supply chains, gradually redesigning its processes. Sears, in contrast, largely treated the internet as a peripheral sales channel layered onto existing brick-and-mortar processes and struggled to adapt its operating model as retail shifted online.<\/p>\n

The implication for quantum computing is that organizational space should be deliberately created to enable exploration that is not tightly tied to immediate returns. Such experimentation may need to be separated from core operations, supported by different incentive structures, and evaluated based on learning rather than near-term financial impact. The goal is not simply to test quantum tools but to explore how quantum-enabled capabilities might eventually support new ways of delivering value.<\/p>\n<\/p>\n

Taken together, these strategies turn quantum computing from a waiting game into an active learning process. Rather than asking when the technology will be ready, managers can focus on whether their organization is developing the interpretive capability, experiential knowledge, and organizational flexibility needed to benefit when more powerful applications become feasible. In the context of enabling technologies, preparedness is about being ready to recognize, shape, and act on opportunities as they emerge. If and when quantum becomes useful for specific enterprise problems, those companies will be positioned to move quickly.<\/p>\n<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Matt Chinworth\/theispot.com Executives tracking the latest news about quantum computing […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[194],"tags":[],"class_list":["post-22448","post","type-post","status-publish","format-standard","hentry","category-graphic-design"],"acf":[],"_links":{"self":[{"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/posts\/22448","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/comments?post=22448"}],"version-history":[{"count":0,"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/posts\/22448\/revisions"}],"wp:attachment":[{"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/media?parent=22448"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/categories?post=22448"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ideainthebox.com\/index.php\/wp-json\/wp\/v2\/tags?post=22448"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}