Navigating the Future of Quantum Technology with McKinsey Digital

We caught up with Michael Bogobowickz, Partner, McKinsey & Company to explore what he is most excited about in quantum for 2024, Mckinsey's position within the Quantum landscape map and how far off he thinks quantum is from its Gen AI moment?

Please give us a little introduction on your current role and what you do

I lead the Quantum Services practice line in North America, along with my colleagues Niko Mohr and Henning Soller in Europe, where I explore the business of quantum, the impacts that it will drive across industries and how the roadmap to get to quantum adoption shifts with research advancements, government actions and industry value.

What do you consider your biggest professional achievement to date?

Being a part of the group that develops and releases the Quantum Monitor. With thousands of hours of research, we've put together a very different view on the quantum ecosystem, and continue to evolve it each year to match the maturing environment. With this year's Monitor, we've dived more heavily into the impacts of how the public funding structure has shifted the course of the quantum economy even in the face of a milder-than-expected quantum winter, how geographic innovation hubs are taking on the heavy lifting role of driving real world impact, and how GenAI has both created a short term drain while acting as a massive accelerator in the medium to long-term.

What are you most excited about for quantum in 2024? What predictions do you have for the year ahead?

We are excited to see continued acceleration of advancements in quantum computing, facilitating the transition from academic research to practical, real-world applications. In 2024 alone, we’ve already seen milestones in quantum algorithms, error correction and logical qubits. Simultaneously, there is growing demand from private companies and governments, and the rise of global quantum innovation hubs that will bring together the diverse groups needed to build out practical applications.

We gathered these insights in our fifth edition of McKinsey’s Quantum Technology Monitor. Download HERE.

Where does your organization sit within the quantum ecosystem?

We empower start-ups, investors, governments, and organizations to capture the competitive advantage of quantum technologies. We provide insights into the quantum computing industry, analysing how market, research, and policy forces shape the roadmap for this rapidly evolving technology. We help organizations understand and build the early use cases of quantum computing, ensuring they stay at the forefront of this groundbreaking field.

Where do you see breakthroughs in quantum in 2024 with enterprises?

We expect early adoption in logistics, financials, pharmaceuticals (particularly in trial and portfolio optimization vs. drug discovery) and materials science with real world use cases.

Do you see any breakthroughs in error correction this year?

We are optimistic about the impact of error correction this year, this could significantly influence the scalability of quantum computers. Error correction integrated with the hardware could address the challenge of noise and qubit stability, crucial factors for larger and robust quantum computers.

Do you think 2024 will see an uplift in quantum investment?

Given the potential value of quantum computing to unlock, we predict a continued uplift in quantum startups in 2024, though coming from materially different sources than the last few years, with a greater shift from private funding into public funding and revenue-funded investment.

How many qubits do you see us reaching in the next year to 18 months? Will they make a difference?

Not nearly enough. The number of qubits reached in the short term will vary a lot across the qubit technologies and architecture approaches (error mitigation techniques, topological dieffrences in qubit designs etc.,). There are better benchmarks than qubit counts, however the logical qubit race does have some significance and this along with 2-qubit gate fidelities are reflected in the roadmaps announced by key players.

How do you think quantum will impact on cyber-security? Do enterprises need to be approaching this challenge now, or is that hype?

This is by far the most significant area driven by the future timeline. The longevity of data (some of which, such as defence, which can have value of 50+ years) implies any data stolen today without post-quantum encryption will be fully readable in a short number of years.

Where do you stand on the current talent shortage in quantum? What more do you think can be done?

Talent shortage remains a critical issue in quantum advancement. Our latest analysis shows a significant increase (9% from the previous year) in quantum talent. Despite this growth, there is still a significant gap between industry needs and the availability of skilled talent. To bridge this gap, industry leaders, academia, and government need to collaborate to educate the next generation of quantum talent and build an inclusive environment for a diverse group of talents.

How far off do you think quantum is from its Gen AI moment?

Hype is fleeting. We are interested in long-term impact. Both (Gen)AI and quantum computing have tremendous potential to unlock value: (Gen)AI is set to unlock $2.6-4.4 trillion annually across 63 new use cases when applied across industries according to McKinsey research. In contrast quantum computing can generate $0.9-2 trillion in value by 2035 across chemicals, life sciences, finance, and automotive until 2035. But connecting the two could create impact that is larger than the sum of the two.

How do you see quantum working with AI and machine learning?

The intersection of quantum computing and AI is demonstrating early potential to significantly accelerate advancements in both fields. AI could play a crucial role in the evolution of fault tolerant quantum computers by enhancing algorithms and facilitating the discovery of new materials. On the other hand, quantum computing could potentially offer increase in speed and efficiency of AI models and their training systems leading to solving more complex problems at higher speed.

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