What is quantum computing?

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Quantum computing is a new approach to calculation that uses principles of fundamental physics to solve extremely complex problems very quickly.

Flip a coin. Heads or tails, right? Sure, once we see how the coin lands. But while the coin is still spinning in the air, it’s neither heads nor tails; it’s some probability of both.

This gray area is the simplified foundation of quantum computing.

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Miklós Gábor Dietz is a senior partner in McKinsey’s Vancouver office, Dieter Kiewell and Jared Moon are senior partners in the London office, Alexandre Ménard is a senior partner in the Paris office, Mark Patel is a senior partner in the Bay Area office, and Rodney Zemmel is an alumnus of the New York office.

For decades, digital computers have been making it easier and easier for us to process information. But quantum computers, a fundamentally different approach, are poised to take computing to a whole new level. Quantum computers have the potential to solve very complex statistical problems that are well beyond the limits of today’s computers, across a range of industries and applications, including finance, transportation, pharmaceuticals, and green technology. While quantum computing is just one of three main areas of emerging quantum technology, it alone could account for nearly $1.3 trillion in value by 2035 and make possible unprecedented business capabilities. Investors of all kinds are perking up their ears—and opening up their wallets. McKinsey’s 2024 survey of quantum-industry leaders indicates that many quantum firms are growing quickly in size: 39 percent of respondents said their companies have more than 100 employees, up from 9 percent in 2023. What’s more, government investors alone have pledged $34 billion in investments.

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How does a quantum computer work?

Classical computing, the technology that powers your laptop and smartphone, is built on bits. A bit is a unit of information that can store either a zero or a one. By contrast, quantum computing is built on quantum bits, or qubits, which can store both zeros and ones. Qubits can represent any combination of both zero and one simultaneously; this is called superposition, and it is a basic feature of any quantum state. When a qubit’s subatomic particles are in a superposition state, each subatomic particle can interact with and influence others, a phenomenon called quantum interference. Quantum chips make up the physical hardware that stores qubits, similar to microchips in classical computers.

When a classical computer uses multiple variables to solve a problem, it must conduct a new calculation every time one of those variables changes. Each calculation is a single path to a single result. Quantum computers, on the other hand, can explore many paths in parallel through superposition.

Additionally, qubits can interact with one another through a phenomenon known as entanglement. Entanglement allows qubits to scale exponentially; two qubits, for example, can store and process four bits of information, three can process eight, and so on. This exponential scaling gives quantum computers much more power than classical computers.

Currently, organizations are mainly using five qubit technologies in their attempts to build a scalable, universal quantum computer. These technologies are photonic networks, superconducting circuits, spin qubits, neutral atoms, and trapped ions (exhibit).

What are the most recent advancements in quantum computing?

Some heavyweight tech organizations are already placing their bets on quantum technology. In 2024, Google unveiled an experimental quantum computer that, in five minutes’ time, could perform a calculation that would take most supercomputers ten septillion years to finish—longer than the age of the known universe. Google’s quantum chip, called Willow, is primarily suited for research and specialized fields. Other organizations are also developing their own quantum computers, and as of 2022, the Chinese government had pledged $15.3 billion in public funds to quantum computing.

In February 2025, Microsoft announced the discovery of a new state of matter that it says will support a quantum computing breakthrough. After 17 years of physics research, the tech giant unveiled the Majorana 1 quantum chip. This microprocessor harnesses the properties of a material called a “topological qubit,” which yields particles that are neither liquid, solid, nor gas—though the reality of the new state of matter as well as the practical use of the new quantum machines has yet to be proven. Microsoft says the chip could be used to accelerate drug discovery, battery development, and the race toward AI dominance. Many scientists agree that Microsoft’s topological qubits could lead to much more efficient and less complex error correction. They could also help avoid quantum decoherence, which happens when a quantum system loses its quantum properties and starts behaving more like a classical-computing system.

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While the problems that quantum computers can solve are impressive compared with what current workaday computers can manage, they have limited real-world applications. But we are rapidly approaching a time when quantum computers will have a real impact on people’s lives.

What are quantum computers used for?

Today’s classical computers are relatively straightforward. They work with a limited set of inputs and use an algorithm to spit out an answer—and the bits that encode the inputs do not share information about one another. Quantum computers are different. For one, when data are input into the qubits, the qubits interact with other qubits, allowing for many different calculations to be run simultaneously; this is why quantum computers can work so much faster than classical computers. But that’s not the end of the story. Quantum computers don’t deliver just one clear answer like classical computers do, but a range of possible answers.

For calculations that are limited in scope, classical computers are still the preferred tools. But for very complex problems, quantum computers can save time by narrowing down the range of possible answers.

When will quantum computers be available?

Over the next few years, the major players in quantum computing—as well as a small cohort of start-ups—will work to steadily increase the number of qubits that their computers can handle and improve how the technology functions. Progress in quantum computing, however, is expected to remain a long-term race to achieve the quantum volume that’s required to quickly solve real-world problems. According to McKinsey’s conversations with tech executives, investors, and academics in quantum computing, 72 percent believe we’ll see a fully fault-tolerant quantum computer by 2035. The remaining 28 percent think this milestone won’t be reached until 2040 or later.

Still, some businesses will begin to derive value from quantum well before then. At first, businesses will receive quantum services via the cloud. As we’ve seen, several major computing companies have already announced their quantum cloud offerings.

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What’s the relationship between quantum computing and AI?

Quantum computing and AI have a potentially symbiotic relationship, and they stand to advance each other’s capabilities and respective fields. Together, they might contribute to achieving artificial general intelligence (AGI). Here’s how each technology uses the other to increase efficiency:

  • Quantum computing can help AI rapidly process extensive data sets, thereby accelerating AI processes and training models. Quantum computing can enable AI applications to achieve the following milestones, which may eventually support AGI:
    • enhanced computational power, enabling the rapid data processing and computations needed for AGI
    • more efficient problem-solving, which boosts AI’s advanced reasoning and decision-making
    • improved learning capabilities, which are critical for AGI’s adaptability across diverse tasks and environments
    • parallel processing, which improves information analysis and synthesis
    • easier handling of complex data structures, which provides AI with tools to process and understand intricate information
    • potential breakthroughs in AI research

      •  

  • AI can enhance the development, optimization, and practical applications of quantum computing. Here are a few areas of quantum computing that could benefit from AI:

    •  

    • Error correction. Machine learning predicts and corrects quantum computation errors, improving quantum computers’ reliability.
    • Noise reduction. AI can analyze noise patterns to develop reduction strategies.
    • Quantum-algorithm design and optimization. Both of these processes can happen via AI-like reinforcement learning.
    • Quantum-hardware control. AI can automate quantum-device calibration and dynamically adjust its control parameters.
    • Resource management. AI can help allocate qubits efficiently and optimize the scheduling of quantum tasks.
    • Simulation and emulation. AI can enhance quantum-system simulations for algorithm and hardware testing, as well as aid research by emulating quantum processes on classical hardware.
    • Benchmarking and performance analysis. AI can develop sophisticated benchmarking tools to evaluate and compare quantum devices and algorithms.
    • Hybrid quantum–classical systems. AI can optimize the distribution of tasks between classical and quantum processors, maximizing overall efficiency.
    • Quantum machine learning (QML). AI can improve the training of QML models by enhancing performance and applicability.

What are some obstacles that may impede the development of quantum computing?

One major challenge in the advancement of quantum computing is the volatility of qubits. Whereas a bit in today’s classical computers is in a state of either one or zero, a qubit can be any possible combination of the two. When a qubit changes its status, inputs can be lost or altered, which throws off the accuracy of the results. Another obstacle to development: For a quantum computer to operate at the scale that’s needed to deliver significant breakthroughs, it will require potentially millions of qubits to be connected. The few quantum computers that exist today contain nowhere near that number of qubits.

Here are some other challenges to scaling quantum computing technology:

  • High-fidelity two-qubit gates at scale. Maintaining “high fidelity” (meaning a level of accuracy and reliability that’s greater than 99.99 percent) is required for fault-tolerant quantum computers. Doing so at scale will be difficult.
  • Speed. Qubits need to retain their quantum state to interact with one another. Even under specific environmental conditions, they will eventually degrade.
  • Multiqubit networking. Connecting, or networking, qubits to one another could theoretically make quantum computers much more powerful. The key challenge here is connecting qubits across chips, or from one physical quantum computer to another.
  • Individual qubit control at scale. The control of individual qubits becomes increasingly complex as the number of qubits in a given quantum computer increases.
  • Cooling power and environmental control. As quantum computers become larger, the size and power requirements of the equipment to cool them become more and more costly, from both a financial and an environmental standpoint. Currently, powering a quantum computer large enough to connect millions of qubits is cost prohibitive for most companies.
  • Manufacturability. Producing large numbers of quantum computers will require automation of both the manufacturing and testing processes. The production of certain quantum computers may require developing entirely new manufacturing techniques.

How can classical computers and quantum computers work together?

Slowly, at first. Initially, quantum computing will be used alongside classical computing to solve multivariable problems. One example? Quantum computers can narrow the range of possible solutions to a finance or logistics problem, helping a company reach the best solution faster. This kind of slower progress will be the norm until quantum computing advances enough to deliver more significant breakthroughs.

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How can organizations find the quantum computing talent they need?

There’s a wide gap between the business need for quantum computing and the number of quantum professionals who are available to meet it. This talent and skills gap could jeopardize potential value creation, which McKinsey estimates to be as much as $1.3 trillion.

The talent challenge is felt differently by companies of different sizes. Small start-ups working in the quantum space typically grow out of university research labs and often have direct access to skilled candidates. Larger companies, though, might have less of a connection to these talent pools.

McKinsey research has found that there is only one qualified quantum candidate for every three quantum job openings. According to McKinsey research, less than 50 percent of quantum jobs in 2025 will be filled unless there are significant changes to either the talent pool or the predicted rate of quantum job creation.

Here are five lessons from the AI talent journey that can help organizations build the quantum talent they need:

  • Define your talent needs clearly. In the early days of AI, some organizations hired data scientists without a clear understanding of what skills were needed. To avoid making the same mistake with quantum, organizations should first identify possible fields of applications that a quantum computing team would work on, then ensure that new hires come from diverse areas of expertise.
  • Invest early in translators. As the early buzz built up around AI, the role of analytics translators became crucial to helping leaders identify and prioritize the business challenges that AI was best suited to help solve. With quantum, there’s a similar need: for translators with engineering, application, and scientific backgrounds who can help organizations understand the opportunities and identify the right players in this rapidly expanding ecosystem.
  • Create pathways for a diverse talent pipeline. Many of the first AI models reflected the same biases from the information that was used to train them. Also common was a lack of diverse perspectives and experiences among the people who built and tested the models, which contributed to the bias issue. While it’s too early to know all the risks that could emerge from quantum, we can expect similar challenges if a diverse and empowered quantum workforce is not involved with this new technology.
  • Build technology literacy for all. For employees at all levels to make the best use of a new technology, they need a basic understanding of how it works and what it can do. With quantum, business leaders as well as workers up and down the supply chain—in marketing, IT infrastructure, finance, and more—will require basic fluency in quantum topics.
  • Don’t forget talent development strategies. Companies tend to focus heavily on talent attraction during times of technological foment, but that’s just one piece of the talent puzzle. To retain specialists, companies need to carve out clear paths for talent development. One pharmaceutical company leans into both the purpose of its work—developing use cases that help save lives—and the freedom it offers its team to choose which quantum-related questions they work on and to collaborate with external experts and practitioners.

What are potential business use cases for quantum computers?

Quantum computers have four fundamental capabilities that differentiate them from today’s classical computers, all of which can be applied to a business context:

  • Quantum simulation. Quantum computers can model complex molecules, which may eventually help reduce development time for chemical and pharmaceutical companies. When scientists are developing new drugs, they need to examine the structure of a molecule to understand how it will interact with other molecules. It’s almost impossible for today’s computers to provide accurate simulations, because each atom interacts with other atoms in complex ways. But experts believe that quantum computers are powerful enough to eventually be able to model even the most complex molecules in the human body. This opens up the possibility for faster development of new drugs and new, transformative cures.
  • Optimization and search. Every industry relies on optimization in one way or another. Where are robots best placed on a factory floor? What’s the shortest route for a company’s delivery trucks to travel? There are myriad questions that need to be answered to optimize a company’s efficiency and value creation. With classical computing, companies must make one complicated calculation after another—a potentially time-consuming and costly process, considering the many variables in a given situation. Since a quantum computer is able to work with multiple variables simultaneously, it can be used to quickly narrow the range of possible answers. From there, classical computing can be used to zero in on one precise answer.
  • Quantum AI. Quantum computers have the potential to work with more advanced algorithms that could transform machine learning across a diverse range of industries, from automotive to pharmaceuticals. In particular, quantum computers could accelerate the arrival of self-driving vehicles. Companies such as Ford, GM, Volkswagen, and other mobility start-ups are running video and image data through complex neural networks. Their goal? To use AI to teach cars to make crucial driving decisions. Quantum computers’ ability to perform multiple complex calculations with many variables simultaneously allows for faster training of such AI systems.
  • Prime factorization. Today’s businesses use large, complex prime numbers—numbers that are too big for classical computers to process—as the basis for their encryption efforts. Using a process called prime factorization, quantum computers will be able to use algorithms to solve these complex prime numbers more easily than classical computers can. (In fact, a quantum algorithm known as Shor’s algorithm can theoretically do this; there’s just not a computer powerful enough to run it.) Once quantum computers have advanced enough, new quantum-encryption technologies will be needed to protect online services—and scientists are already working on quantum cryptography to prepare for this eventuality. McKinsey estimates quantum computers will be powerful enough for prime factorization by the late 2020s at the very earliest.

As these capabilities develop at pace with quantum computing power, additional use cases will likely proliferate.

What are other quantum technologies aside from computing?

According to McKinsey’s analysis, quantum computing is still years away from widespread commercial application. But other quantum technologies such as quantum communication (QComm) and quantum sensing (QS) could become available much earlier.

Quantum communication will help enable strong encryption protocols, which could greatly improve the security of sensitive information and help in the following situations:

  • Full security. QComm applications such as quantum-encryption protocols and quantum teleportation help ensure complete protection when information is transferred between locations. These protocols are more secure than classical protocols, most of which are likely to be broken once quantum computers attain more computing power or can work with more efficient algorithms.
  • Enhanced quantum computing power. QComm enables two important types of quantum processing: parallel quantum processing (where multiple processors are connected and simultaneously execute different calculations from the same problem) and blind quantum computing (where quantum communications provide access to remote, large-scale quantum computers in the cloud). Both types of processing are made possible by the entanglement of quantum particles, when particles such as qubits have connected properties (that is, one particle’s properties can be manipulated by the actions done to another).

Quantum sensing allows for more accurate measurements than ever before, including measurements of physical properties such as temperature, magnetic fields, and rotation. Once quantum sensors are better optimized and have decreased in size, they will be able to measure data that current sensors are unable to capture.

The markets for QComm and QS are currently smaller than the market for quantum computing, which has so far attracted most of the headlines and funding. But McKinsey expects both QComm and QS to attract serious interest and funding in the future. Although investing in these quantum technologies is not without risk, the potential payoff is high: By 2030, QComm and QS could generate $13 billion in revenues.

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This article was updated in March 2025; it was originally published in May 2023.

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