Quantum sensing’s untapped potential: Insights for leaders

Quantum sensing, which allows sensors to collect data at the atomic level, is a tested technology. It has broad applications in commercial products (such as medical devices), in services (such as navigation), and as an enabler of internal processes (such as quality control)—and the technology is already usable. It enables many types of measurements that are currently too difficult to achieve, not precise enough, or simply unfeasible otherwise. Depending on the underlying technology, the size, weight, and power of the sensors enable a variety of use cases in different industries, making further deployment of quantum-computing sensors outside the lab possible.

“Quantum sensors are reaching maturity levels that will allow us to run pilots in a wide range of environments and for a wide range of applications,” said Mathieu Munsch, CEO of quantum-sensing start-up Qnami.

Most of the remaining hurdles are commercial and related to adoption—such as engineering and creating products, taking those products to market, building a supportive ecosystem, and managing the transition to using new tools. Because of these qualities, decision makers in many industries should have at least some familiarity with quantum sensing.

This article provides an overview of the technology’s possible impact on industry as well as a look into the current state of the quantum-sensing ecosystem, highlighting ways companies could think about using the technology. Specifically, corporate stakeholders could investigate the costs and benefits of quantum-sensing use cases for themselves and their industries, set up their organizations to engage with the technology, and seek out partners with whom they could contribute to the quantum-sensing ecosystem. The road to commercialization may be relatively short, so the time to learn about and assess the technology is now.

A quietly growing ecosystem that can create change across industries

Quantum sensors offer more precise readings than traditional sensors. In addition, the technology provides more consistency, because quantum sensors depend on physical constants that also keep the sensors calibrated. And because quantum sensors could take more frequent or even continuous measurements without requiring significant trade-offs such as the need to make sensors larger, the technology also offers the possibility of more readings.

These advantages are significant because sensors are ubiquitous, particularly in industry—consider the 14.8 billion (and rising) Internet of Things–connected devices around the globe. Below, we explore how this ubiquity makes the technology’s impact potentially far-reaching and discuss the developing ecosystem.

How broad applications could yield substantial value

Our analysis suggests that quantum sensing’s capabilities in monitoring, imaging, navigation, and identification will make a significant impact, both on their own and as enablers of processes. The use cases for these capabilities span a broad range of industries, including life sciences, energy and materials, communications and logistics, and microelectronics. We expect the market impact of quantum sensing to ramp up moderately as we approach the 2030 timeframe, but believe the technology holds the potential for significant acceleration and disruption in the years that follow. Quantum sensing has clear use cases in the above sectors, could enable completely new use cases, and addresses currently unsolvable or impossible challenges.

Consider life sciences, in which the technology can support imaging for both baseline readings and concerns such as cardiovascular irregularities and brain abnormalities. These imaging capabilities can also be applied in media and entertainment, such as advanced gaming interfaces that harness subtle signals from users. Experts predict that noninvasive brain–computer interfaces could help users control and interact with media.

In navigation, quantum sensing could help the automotive and assembly industry monitor and optimize production lines and identify faulty parts in production. Precise atomic clocks, magnetic sensors, inertial measurement units (which can measure objects’ specific gravities and angular rates), and magnetometers (which measure changes in the Earth’s magnetic field) could all make navigation more accurate.

A modest but developing current ecosystem

“There has been tremendous work in the science and engineering of quantum-sensing hardware over the past decades, and now this technology is also poised to benefit from advances in related fields,” Nadia Carlsten, vice president of product at SandboxAQ, told us. “More sensor types and better control technologies mean that the footprint of quantum-sensing systems is getting smaller and more suited to commercial applications, and more-advanced machine learning techniques are making it possible to improve signal to noise in real-world situations.”

For now, the number of start-ups and corporations working in quantum sensing is small relative to that of the industry’s quantum-computing counterpart. The quantum-sensing ecosystem gained a handful of start-ups in the past two years, for a total of 48 quantum-sensing start-ups today. By comparison, 261 quantum-computing start-ups launched in the same period. And while half of the freestanding quantum-sensing start-ups are in components, McKinsey research suggests that relative to the number of players, start-up investment is proportionately much higher in the development of hardware and the development of applications and services.

Core parts of the quantum-sensing ecosystem—hardware and software—are already in the prototype stage, which suggests a high level of technological maturity. However, most of the revenue in the ecosystem currently comes from components (the parts that go into building hardware systems) and joint research projects, not from commercialized offerings. Consider that equipment and components are currently the most developed area of the ecosystem, as measured by the number of companies (of any maturity level) working in that area (exhibit).

Meanwhile, quantum-sensing hardware products are in development, and the relatively few start-ups in that part of the ecosystem attract considerable investment. For now, most of the research on hardware is focused on finding the right combination of sensitivity, size, weight, and other specifications (such as shielding quantum sensors from noise, discussed below) so the technology can be broadly marketed and used for a wide range of use cases and needs for which the quantum sensor would outperform its classical counterpart. For instance, quantum sensors used for imaging in semiconductor production prioritize the need for nanometer-level precision rather than weight, while a quantum navigation sensor on most aircraft may prioritize weight.

One way to address technical challenges is to shield quantum sensors from noise in the environment. Reducing environmental noise for individual sensors through shielding tends to require trade-offs in cost and usability. An alternative approach to reducing environmental noise is to use systems of sensors to amplify the signal being measured and to exclude noise rather than rely on a single sensor. Yet another approach is to use AI to learn the characteristics of the noise that interferes with readings for a particular signal to amplify the signal relative to the noise. Several joint ventures between corporations and quantum-sensing start-ups are also aimed at standardizing and refining how quantum-sensing products are engineered and manufactured.

As the hardware matures, the value of application software and services expands (for more on the quantum-sensing technology stack, see sidebar, “The quantum-sensing technology stack”).

The value of software and services will be further enabled by the development of the quantum-sensing ecosystem. Current sensors are typically expensive, and their usage is mostly confined to that of singular sensors. Going forward, the advent of the right software will allow the exchange and combination of data between sensors to enable further use cases. Additionally, the data exchange should make it possible for sensors to be used by more than one user and for more than one purpose. This combination of sensors in the ecosystem will lower the cost for individual participants and improve the overall precision of the sensors. The coordination of the ecosystem and its corresponding participants is far from clear today; nonetheless, the future value of quantum-sensing companies will depend on their coverage of the value chain and the corresponding development of the ecosystem.

Meanwhile, opportunities in the quantum-sensing ecosystem are plentiful, and our research suggests that corporations are already exploring them. According to McKinsey analysis, more than 80 percent of investment in quantum technology from 2001 to 2023 came from venture capital and corporate investors. Investment in quantum sensing from established companies is likely higher than the roughly $380 million that quantum-sensing start-ups raised from 2003 to 2021. Significantly, the five most richly funded start-ups drew more than 80 percent of funding for the quantum-sensing industry. But this concentration in funding doesn’t mean the industry itself is concentrated. The full value chain of the quantum-sensing ecosystem has yet to be built, so there is still room for many more participants.

Pushing quantum sensing’s potential closer to reality

According to Mathieu Munsch of Qnami, companies that explore and validate their use cases can secure a competitive advantage, because production ramp-up takes time. As prospective participants in the quantum-sensing ecosystem, corporate decision makers could evaluate the technology’s possible impact on their industries and organizations before getting their organizations ready to work with the technology and partnering to join the ecosystem.

Assessing quantum sensing’s industry and corporate impact

Because quantum sensing has not yet gained much attention, the first step for most corporate stakeholders would be to identify use cases for quantum sensing. From there, decision makers could evaluate the value and costs of each use case in the short, medium, and long terms. Corporate stakeholders could uncover the highest-value problems that quantum sensing could address and guide the rest of the quantum-sensing ecosystem toward collaborating to resolve them.

Identifying the most valuable use cases helps determine which underlying quantum-sensing technology to use, because the potential of certain use cases is unlocked with the appropriate technology. Among the commercial applications under development, the types of quantum sensing most commonly used are solid-state spins, neutral atoms, superconducting circuits, and trapped ions. These quantum-sensing technologies have shown capabilities across various physical properties—including magnetic fields, electric fields, rotation, acceleration, temperature, gravity, time, and pressure—that make them suitable for a range of commercial applications.

Quantum sensors’ most obvious allure is their ability to take measurements that are impossible or impractical to achieve with traditional sensors. For many use cases, quantum sensors may be smaller or more sensitive than traditional sensors. The conditions under which measurements must be taken may also render measuring with traditional sensors impossible. For instance, magnetic-field detection within a fusion reactor is impossible with traditional sensors because a fusion reactor’s internal temperatures can approach 150,000,000°C. But diamond-based quantum sensors could withstand and take readings in such an environment.

Of course, many use cases for quantum sensing are improvements on classical sensors that create value not by taking measurements that were previously impossible but by simplifying and reducing ongoing costs. Corporations considering the technology for their organizations or industries would then need to determine whether classical sensors meet their needs and when using quantum sensors would be worthwhile.

This assessment will change over time. At the moment, quantum sensors are more expensive than traditional sensors in most use cases. However, the business case for using quantum sensors could shift more in quantum sensors’ favor as manufacturing for quantum sensors becomes established, standardized, and refined.

Companies have already begun to explore the impact of quantum-sensing use cases. One multinational has been working on miniaturizing quantum sensors, applying the technology to use cases in medicine (particularly in diagnostics) and in ultraprecise navigation, working with public and private sector partners to deploy its navigation systems in aerospace.

There is no single best path forward. Companies embarking on similar efforts could partner with researchers, test multiple quantum-sensing technologies, develop their own sensors, and experiment with integrating off-the-shelf sensors with quantum sensors in systems. The right combination of approaches will be specific to the organization spearheading the effort.

Setting up an organization for quantum sensing

With the prioritized use cases in hand, corporate decision makers could assess their organizations’ capabilities against the kind of support those use cases would need and then seek out and assemble the right teams. This series of decisions is about both talent and organizational design: selecting the right talent, training people, and cultivating environments that would allow a company’s specific mix of talent to innovate effectively. Decision makers would also need to determine what kind of talent to include at each developmental stage and how to ensure that staff with different expertise—for instance, physicists and end users—can communicate effectively.

In use cases that involve gathering types of data that have never been gathered before, new teams would need to be deployed to manage the sensors. Notably, often the end users of quantum-sensing technologies will not have expertise in quantum sensing or quantum physics. For example, medical technicians, nurses, and doctors would be expected to use quantum sensing–enabled imaging tools. Serving the needs of these users—who are not experts in quantum technology—makes usability design, training, and relevant upskilling critical.

At the same time, quantum sensors generate large, complex data sets—generating up to ten terabytes per second, according to Kevin Berghoff, CEO of QuantumDiamonds. AI tools could help both users and quantum-sensing experts interpret data and generate insights. Any quantum-sensing solutions would also need to correspond with existing processes or risk being unadoptable.

In use cases in which organizations are monitoring the development of quantum sensors to determine the right time to deploy them, corporate stakeholders could preemptively determine which teams might be affected when the time comes and be ready to upskill them or staff them with additional team members.

Partnering to join the ecosystem quickly

Partnerships can help large organizations realize their highest-priority use cases. They can contribute to closing the gap between laboratory prototypes and industry applications, which is crucial to unlocking the full potential of quantum sensors and adding real value.

“Partnering with organizations that bring application-specific subject-matter expertise is key to ensuring the whole system not only performs better but also is integrated seamlessly into the environment it is designed to operate in,” SandboxAQ’s Nadia Carlsten said.

When it comes to partnerships, corporate leaders should define an aspiration for the role they want to play in the quantum-sensing ecosystem. Based on these goals, organizations could evaluate their own value propositions and close any gaps in strategic capabilities, including strong analytics capabilities and domain expertise. Indeed, an end-to-end product mindset is vital to advancing existing components and creating the (often required) new markets with fully developed novel products. However, because the quantum-sensing ecosystem is still small, the pool of potential partners is modest, making finding a good partner more challenging.

This reality increases the importance of assessing the match. The two sides should invest time and resources in determining their internal capabilities, gauging where they can partner, and identifying capabilities that neither partner possesses.

Any company interested in partnering should identify partners that want to work at a compatible—preferably fast—pace, because developments happen quickly in the field. The partners should also be prepared to pivot quickly and to reflect on as-yet-unknown considerations affecting the partnership, such as opportunities, challenges, hurdles, and risks. In fact, the partnership should be structured to make being responsive—an agile, iterative approach—as easy as possible.

Finally, before entering a partnership, the parties involved should also discuss, in broad terms, how they will share intellectual property, customers, and amassed data­­.

If a corporation is considering partnering with a start-up, the established company’s decision makers should be prepared for their organization to take on many responsibilities related to developing equipment and managing vendors—in other words, tasks that larger organizations with more resources are better suited for than a start-up.

Partnerships will be especially relevant when considering overall coverage of the value chain within the ecosystem. While many companies today do not have the capabilities on their own, partnerships offer the possibility of ensuring better coverage of the ecosystem without one company taking on the development of all capabilities by itself.

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Widespread adoption of quantum sensing is close to reality, so it’s time for decision makers across industries to consider how it could fit into their strategies—and whether they may want to participate in commercialization efforts.