by Henning Soller, with Hussein Hijazi, Martina Gschwendtner, and Reem Taibah
The dream of quantum computing (QC) might become reality quicker than expected. Quantum industry leaders agree that QC could revolutionize industries, scientific research, and technology. McKinsey’s latest roundtable with approximately 30 quantum industry leaders and academics reveals not only the complex and multifaceted nature of the industry but also how its rapid growth underscores the sector’s robust development and increasing maturity.
This blog post presents discussions regarding economic opportunities for QC, its symbiotic relationship with AI, and the wider quantum ecosystem.
Economic landscape: Current state and progress of QC
The economic landscape for QC has seen promising growth in the past year, with our latest survey of quantum industry leaders showing significant shifts in company sizes. Notably, the number of companies with fewer than six employees surged to 11 percent from 0 percent. Conversely, the number of companies with 16 to 30 employees and with 31 to 100 employees decreased. This reflects a transition of many firms into the more-than-100-employees category, which saw the most striking change, increasing to 39 percent from 9 percent (Exhibit 1).
These trends suggest a dynamic landscape in which some new companies are still entering the market and existing companies are scaling up as they mature. This observed growth might also indicate that the development of in-production use cases and progress toward fault-tolerant QC is accelerating.
Current state of fault-tolerant QC
Discussions with academics and industry leaders reveal that more than 65 percent anticipate the arrival of fault-tolerant QC (FTQC) by 2030 (Exhibit 2). However, there is an ongoing debate about the definition of actual fault tolerance and whether it must be achieved at scale to be considered truly fault tolerant. Some experts argue that even high error tolerance with minimal correction could enable useful applications before full logical qubit scale correction is reached.
The perceived progress toward FTQC is reflected in the observed increase of in-production QC use cases. Last year, only approximately 33 percent of respondents had a QC use case in production. This year, that number rose to approximately 55 percent. The definition of in-production use cases was widely debated, but respondents agreed that it refers to a use case that has been developed and shows an advantage over alternative classical approaches. In this definition, a use case is not necessarily limited to being fully developed and commercially viable at scale. Multiple industries such as aerospace and defense, automotive assembly, oil and gas, and medical technology could benefit from these developing use cases as early adopters of quantum technologies.
Cybersecurity in the quantum era
QC raises a new set of cybersecurity challenges that businesses must address early on to safeguard their data and systems against future attacks. Most quantum industry leaders surveyed suggested preparing for the quantum era by gradually implementing cybersecurity protocols. Conversely, there were a few leaders who emphasized the need for immediate action, citing the “harvest now, decrypt later” doctrine, which would expose business vulnerabilities later.
Businesses can mitigate quantum cybersecurity threats by adopting a safer, modular approach called “crypto agility.” The concept of crypto agility refers to the ability of a system to quickly and easily switch between different cryptographic algorithms or protocols. Adopting crypto agility can have multiple benefits, such as the following:
- modular design
- algorithm independence
- future-proofing
- redundancy and resilience
- transition pathways
- standards and interoperability
Quantum computing and AI
QC and AI can benefit from a symbiotic relationship to advance each other’s capabilities and respective fields. This relationship contributes to realizing the most ambitious, long-term objective of artificial general intelligence (AGI). In this regard, it is important to distinguish between using QC for AI versus AI for QC.
Quantum computing for AI
QC for AI uses QC to rapidly process extensive data sets, thereby accelerating AI processes and training models. Scaling up through dedicated quantum algorithms that exploit quantum advantages can significantly contribute to achieving AGI in several ways, including the following:
- Enhanced computational power. Quantum algorithms solve complex problems faster than classical ones, speeding up data processing and computations essential for enabling AGI.
- Efficient problem-solving. Quantum algorithms handle optimization problems and simulations more efficiently, boosting AGI’s advanced reasoning and decision-making tasks.
- Improved learning capabilities. Quantum machine learning (QML) accelerates AI learning processes, which is crucial for AGI’s adaptability across diverse tasks and environments.
- Parallel processing. QC’s parallelism speeds up AGI’s training and inference stages, improving information analysis and synthesis.
- Easier handling of complex data structures. Quantum algorithms excel with complex, high-dimensional data, providing AGI with tools to process and understand intricate information.
- Potential breakthroughs in AI research. QC’s unique properties can lead to new AI insights, fostering novel algorithms and architectures that are vital for AGI.
AI for quantum computing
Conversely, AI can enhance the development, optimization, and practical applications of QC. Discussions with QC experts and industry leaders highlighted the following areas that could benefit from AI:
- Error correction. Machine learning predicts and corrects quantum computation errors, improving reliability.
- Noise reduction. AI analyzes noise patterns to develop noise-reduction strategies.
- Quantum algorithm design and optimization. Using AI-like reinforcement learning can efficiently discover new quantum algorithms and optimize circuits by minimizing gate operations and reducing errors.
- Quantum hardware control. AI can automate quantum device calibration for optimal performance and dynamically adjust control parameters to maintain system stability.
- Resource management. AI can be used to efficiently allocate qubits and optimize scheduling and compilation of quantum tasks.
- Simulation and emulation. AI can enhance quantum system simulations for algorithm and hardware testing and emulate quantum processes on classical hardware, aiding research.
- Benchmarking and performance analysis. AI can develop sophisticated benchmarking tools to evaluate and compare different quantum devices and algorithms.
- Hybrid quantum–classical systems. AI can optimize the distribution of tasks between quantum and classical processors, maximizing the efficiency of hybrid computation and facilitating the integration of the quantum–classical system.
- QML. AI techniques can improve the training of QML models, enhancing their performance and applicability.
Building a sustainable quantum ecosystem
Several key themes are essential for developing a robust QC ecosystem: talent development, effective collaboration models, and expectation management. Addressing these will create a sustainable environment that fosters innovation, inclusivity, and practical advances.
Talent development. Attracting and nurturing a skilled workforce requires outreach programs to draw talent from diverse backgrounds beyond traditional PhDs in physics. Some academics advocate for introducing quantum concepts to students before university to avoid the need to “unlearn” classical physics. Early exposure to QC formalism can create a more prepared future workforce.
Collaboration models. Effective collaboration between academia, industry, and government is crucial for deploying QC technologies. Bridging these sectors can reduce bureaucracy and secure venture capital, which is vital for sustained innovation. Open-source tools can further global engagement and drive innovation through shared resources and collective expertise.
Expectation management and clear communication. Setting realistic expectations about quantum technology’s current state and future potential is critical to avoid industry alienation. Clear communication can manage hype, ensuring stakeholders understand both opportunities and limitations. Regular, transparent updates can build trust, align efforts with achievable goals, and foster a sustainable quantum ecosystem.
The journey through quantum’s economic landscape, the interplay with AI, and the development of the quantum ecosystem unveils transformative potential and complex challenges. An inclusive, coordinated, and transparent quantum ecosystem could herald a new era of technological and economic advancement.
Henning Soller is a partner in McKinsey’s Frankfurt office, Hussein Hijazi is a consultant in the Dubai office, Martina Gschwendtner is a consultant in the Munich office, and Reem Taibah is a capabilities and insights analyst in the Riyadh office.