It is hard to imagine any innovation having a greater influence on automotive design than the wheel. Invented in Mesopotamia around 3500 BC, historians believe the first wheels were cut from tree trunks and used in pottery long before revolutionizing transportation. But while other significant inventions predated the wheel, anthropologists and engineers alike insist it is difficult to exaggerate the social and economic importance of the wheel, which, as Smithsonian magazine noted in a 2009 tribute, is regularly “cited as the hallmark of man’s innovation.”

And yet, quantum computing may one day force a rethinking of how we rank influential inventions and innovations. After all, innovation relies on calculations, and quantum computers offer a radical new form of computation, one that Bank of America megatrends analyst Haim Israel recently told Barron’s “could be a revolution for humanity bigger than fire, bigger than the wheel.”

Forward-looking automakers are already preparing for the potential dramatic increase in computational power that quantum computing appears on track to eventually make widely accessible. BMW, for example, recently established an endowed chair of Quantum Information Systems at a German university. The company also recently partnered with a leading manufacturer of neutral atoms quantum processors, aiming to eventually deploy highly accurate computational simulations that would allow the automaker to replace costly physical build-test-improve cycles. BMW and Volkswagen are also founding members of the Quantum Technology and Application Consortium (QUTAC), which aims to ensure that the existing fundamentals of quantum computing develop into usable automotive applications.

The auto sector, of course, is not alone when it comes to betting significant resources on the eventual arrival of commercially useful quantum computers. BASF, Boehringer Ingelheim, Bosch, Infineon, Merck, Munich Re, SAP, and Siemens have also signed on as founding members of QUTAC, which, in addition to the auto sector, will focus on bringing industry-relevant quantum computing applications to market for the technology, chemical and pharmaceutical, and insurance sectors.

“If quantum computing reaches its full potential at scale—which seems increasingly likely given recent escalations in investment and an accelerated pace of scientific breakthroughs—we are looking at a whole new era of computing, one with the potential to deliver profound and disruptive changes across industries.”

The idea of a quantum computer was originally proposed in the 1980s. But progress has been slow, as the technical challenges are immense. It will still take years, possibly decades, for quantum computing to achieve its full potential. Nevertheless, early engagement with an emerging technology, even before the final form factor or application process is obvious, can reap significant results. And it is important to note that governments around the world have made investing in this technology a serious priority because if quantum computing reaches its full potential at scale—which seems increasingly possible given recent escalations in investment and an accelerated pace of scientific breakthroughs—we are looking at a whole new era of computing, one with the potential to deliver profound and disruptive changes across industries.

Some changes will be more welcome than others. Critical elements of our digital life like privacy and security almost exclusively rely on techniques that are essentially impossible for anyone using classical computers to defeat, but getting around them would be trivially easy for capable quantum machines. This fact alone represents a potential seismic disruption to business, not to mention national security, which is why U.S. President Joe Biden recently signed two directives that aim to ensure American leadership in quantum computing while mitigating the risks to national and economic security.

Simply put, quantum computing poses a real threat to any organization that fails to plan ahead. But as many frontier firms already understand, it is a mistake to focus only on quantum computing’s downsides because its benefits will also be tremendous.

By harnessing the naturally occurring principles of quantum mechanics, quantum computers will be substantially better at executing several families of computing algorithms—ones used for everything from machine learning to designing new chemical processes. And that’s why no organization that cares about being competitive can afford to sit on the sidelines, watching and waiting as other firms build talent while deploying intermediate-scale quantum computing solutions to run experiments and test use-cases (one of the most sought-after applications at Accenture’s quantum computing advisory service today is using quantum machine learning and optimization for trading in financial markets. Other use-cases the consulting firm has collaborated on include quantum-enabled fraud detection, material science, currency arbitrage, drug discovery, and life sciences projects).

The bottom line is that building quantum capabilities is vital to generating momentum in the near term and being ready to expand rapidly as the technology matures. In the rest of this article, we offer a brief introduction to the core principles behind quantum computing technology, then outline how to make your existing workforce the catalyst for the crucial experimentation phase that we believe organizations must embrace now in order not to get left behind as the new era of computing changes the game.

Quantum Computing 101

Quantum computers are not the next incarnation of today’s supercomputers. By harnessing the naturally occurring principles of quantum mechanics, they use “qubits” rather than “bits” to store and process information. A traditional bit can be anything that has two distinct configurations (one represented by “0,” and the other represented by “1”). A quantum bit can also have these two distinct states, but it can also exist in superposition states, be subjected to incompatible measurements, or be entangled with other quantum bits. This doesn’t make quantum computing intrinsically faster than classical computing, but qubits do allow us to analyze information in new ways, making them more powerful than classical bits, especially when used to run dedicated quantum algorithms in areas such as optimization, information encryption, simulating natural systems, and searching unstructured data.

As pointed out in “The Prospects of Quantum Computing in Computational Molecular Biology,” using a modern supercomputer to compute “the full electronic wavefunction of an average drug molecule numerically is expected to take longer than the age of the universe,” but with enough high-quality qubits connected together, a quantum computer utilizing a quantum algorithm may be able to solve the equivalent molecule challenge “in a timescale of days.”

Nature utilizes the underlying principles of quantum computing as a building block for the universe. However, enormous engineering challenges must be overcome before quantum computers become a commercial reality. As things stand, the hardware required to run quantum algorithms at scale is not sufficiently developed. There isn’t even an agreed upon approach. Instead, competing technologies are being pursued in parallel (the two most promising are superconducting qubits and trapped ions). It is possible that more than one modality will prevail—each with strengths and weaknesses depending on the use case. Whatever happens on the hardware front, we expect more novel quantum algorithms to be discovered as the research progresses and developers test and experiment on new equipment.

Preparing for the Quantum Leap  

While companies may need to decide which hardware approach best meets their needs, they probably won’t need to build, buy, or maintain their own physical systems. The prevailing approach to quantum computing is cloud-based access via global providers, and that approach is likely to continue. Therefore, instead of worrying about hardware development, we recommend starting your organization’s quantum journey by focusing on building quantum computing capabilities.

Building capabilities early is critical because quantum computing will be subject to some of the same constraints that held up previous technological advances. These will include issues related to organizational talent and culture, which have repeatedly proven to be major bottlenecks to new technology adoption. To get the ball rolling, you need not begin by recruiting externally, where quantum specialists are in extremely high demand. Instead, focus on upskilling existing employees and developing external relationships that can become trusted partnerships.

Building quantum-capable teams will be a similar journey to that undertaken by many firms when machine learning techniques exploded in recent years. Utilizing existing talent, mapping skill gaps, envisioning the future, and experimenting are vital components. We suggest starting the quantum capabilities journey by filling the following key roles: use-case manager; quantum data scientist; quantum software developer; and executive sponsor.

Use-case manager: As organizations start experimenting with quantum computing, they will need to develop an interface between the technologists and the business units. This is where a use-case manager comes into play. They are needed to match the menu of available quantum computing capabilities with the highest potential target applications within the organization. As this role expands, it may incorporate working with key external partners such as customers or suppliers.

Quantum data scientist: Data scientists analyze large amounts of data to arrive at valuable and actionable business insights. The role often requires the use of advanced statistics, machine learning techniques, and a variety of programming languages. A quantum data scientist is a data scientist that utilizes quantum algorithms and quantum computing infrastructure to achieve similar outcomes. Just as statisticians, research analysts, and programmers evolved into data scientists, the role currently played by machine learning data scientists and engineers can evolve to take on a hybrid function responsible for assigning challenges to quantum and classical machines based upon an understanding of which approach will work best. You can begin expanding your data science team’s capabilities by creating an expanded role and give your team members rotating responsibility for quantum solutions for a pre-determined period. There are high-quality online training resources that can help a self-motived employee get started with the upskilling processes.

Quantum software developer: A quantum software developer needs to be capable of utilizing the broader quantum computing environment, such as libraries, for building algorithms, tools for composing circuits, and application programming interfaces (APIs). They must also have capabilities around integrating quantum with non-quantum applications and datasets within the organization. Making applications usable, maintainable, and modular is just as vital for the quantum world as the classical world.

Executive sponsor: Although IT has become increasingly decentralized across organizations, budgets for digital innovation and experimentation still typically require an executive sponsor to fulfill their promise. This is particularly true for emerging technologies, where calling executive sponsorship a critical success factor is an understatement. Executive sponsors must be able to make a compelling case to the rest of the leadership team for the investment that quantum computing requires. They must be able to ensure traditional silos and norms are challenged to accelerate progress. They must also be able to build continuous forward momentum without the benefit of established return-on-investment metrics, which will not be achievable in quantum computing for some time.

Building dynamic capabilities with nascent, uncertain technologies can provide a significant return on investment over the medium to long haul, especially in innovation-focused markets. And when it comes to quantum computing, you don’t need to understand the difference between bits and qubits to understand what’s at stake. As Bank of America’s megatrend analyst put it, “I can’t even think about an industry that won’t be revolutionized.”

Many firms that embraced machine learning early in its development generated an advantage by investing ahead of the curve in critical complements to the core technology such as data processing, human talent, and third-party relationships. If your organization hopes to lead the way in quantum computing, it should follow a similar path.

All it takes is forward-looking executive support for initiatives that could make or break your organization’s competitiveness when the new era of computing arrives at scale.

The research supporting this article is a collaboration between Accenture and MIT’s Initiative on the Digital Economy (IDE) and was performed under the MIT and Accenture Convergence Initiative for Industry and Technology.

The authors would like to acknowledge research support from Laura Converso, senior principal, and Kate Greene, research manager, and editorial help from Gargi Chakrabarty, senior editor, of Accenture Research.