AZoQuantum speaks with Albert Meige and Rick Eagar about separating the hype around quantum computing from reality, identifying credible commercial milestones, and understanding how businesses can prepare for a quantum-enabled future.
What signals should executives watch to distinguish real quantum computing inflection points from hype?
First of all, executives need to be aware that there may be more than one inflection point, as we are still at least 5 years away from a usable FTQC device. The first FTQC with 1000+ logical qubits could well be an inflection point in terms of development speed, but there is still likely to be a substantial time lag before a commercial market is established to enable FTQC device access at scale. The next inflection point after this could be the point at which scale-up takes off. The lesson from AI is useful here: while the launch of ChatGPT certainly marked an inflection point, 2.5 years later, most businesses are still not seeing major top- and bottom-line business impacts.
In the meantime, as mentioned in our recent Viewpoint article, the key thing executives need to do is to take time to evaluate critically the milestone claims being made by developers, not just take them at face value. When announcements are made, executives need to maintain a healthy scepticism. In the report, we advise focusing on the following:
- Scale: Is the case study using data at the scale required for real-world applications?
- Compute power: Is the QC device size (for example, the number of logical qubits) enough to solve real-world problems?
- Verifiability: How meaningful are the claimed advantages? Are they just theoretical or also practical? Could conventional devices solve the same problem? Is the case general enough or too specific?
- Robustness: Is the demo stable enough to support real-world applications?
- Economics: What is the economic case? Does it include the total cost of ownership (TCOO)? How would the economics compare with conventional solutions?
Image Credit: Bartolomiej K. Wroblewski/Shutterstock.com
How should companies invest in quantum computing today without committing too early?
In our previous report, “Unleashing the business potential of quantum computing”, we set out strategic actions for potential end-users who were considering investing time or money in QC. The first thing is to be very clear on potential applications and use cases in your business before considering investing.
Explore applicability in your business: Where could quantum applications in simulation, optimization and cryptography deliver advantage in your current business operations (e.g., in terms of cost, speed, performance, efficiency, or quality)? Where do you currently use or plan to use high-performance computers but struggle with their limitations? Where could you deliver real disruption to your business model or industry by solving intractable problems, or problems that you never before considered solvable? You need to think creatively, not just assume that QC will only be applied today’s products/processes/services. The applications can then be screened and ranked (for instance: scale of business impact, maturity, likely timescale ranges, level of investment, level of difficulty, etc.)
If worthwhile, then monitor developments: develop or acquire the capability to understand the technology, set up the channels to stay abreast and rapidly evaluate potential and limitations. Screen and assess quantum algorithm developments that are targeted specifically at your industry vertical.
Engage with the ecosystem: Understand the roles, activities, and level of involvement of different ecosystem players, including government, academia, research institutes, vendors, startups, and investors. Identify those players that are focusing on your sector and establish how your business could become involved in some way, for example through pilots, co-funding initiatives, staff secondments, or sponsorship of PhD projects.
Develop capability: invest in some capability development, including educating and training at least some in-house resources in quantum technologies, quantum programming, and quantum algorithms, even if this is only at a basic level. This will enable them (and the executive) to participate more effectively in the ecosystem and monitor and evaluate developments. Creating a motivated and enthused community internally is one of the best ways to ensure that you stay abreast of developments in a natural, rather than wholly process-driven, manner.
If you’re an investor, of course, it’s a different type of question and a different answer, but we assume that we are referring to potential end-users here.
Where could quantum computing deliver meaningful business value before full fault tolerance?
If the question means, before a FTQC device of 1000+ logical qubits is available, then one answer is quantum annealing (e.g D-Wave’s hybrid quantum solver), which is already being applied to specific production sequencing, shift rostering, warehouse picking/slotting, fleet scheduling, and planning problems. Examples include Ford Otosan production sequencing, VW traffic optimization and logistics scheduling.
One limitation of quantum annealing is that the solution is often “good enough” rather than fully accurate. It is suitable for combinatorial analysis problems, but is not applicable for general arithmetic computation or complex simulation tasks. In some cases, classical approaches can also be applied to these optimization problems, so the quantum advantage is not necessarily so significant. Moreover, the approach is not really “plug-in” and requires a degree of pre- and post-calculation effort. Overall, it’s often not a major step-change improvement.
For NISQ gate-based devices, there are some limited applications, for example, in specific small molecule simulations (e.g. catalyst, pharma, drug discovery). However, these applications are generally still limited to R&D contexts. In the last three years, the emphasis has shifted away from scaling up NISQ solutions towards solving the engineering challenges of developing a usable FTQC.
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Which technical bottleneck will most determine whether quantum commercialization arrives around 2030 or much later?
We think there is more than one technical bottleneck. Some of the main candidates include the ability to manufacture various chip technologies at scale with high quality and low variability, the potential emergence of new sources of noise at larger scales, and the development of reliable quantum interconnects between quantum processing units (QPUs), given the inherent limits on the number of physical qubits per QPU.
What distinguishes a credible quantum commercial roadmap from a largely speculative vendor narrative?
The features of a credible quantum commercial roadmap are not really any different to any emerging technology commercial roadmap, for example:
- Technically, does it address all the critical factors? (e.g. for QC, engineering challenges are key, not just qubit scaling). See also our answer to the first question on critically evaluating milestone claims.
- Economically, is there a credible target economic case to support commercialization? What assumptions are being made about market development, pace of scale-up, access and pricing etc.?
- Capabilities: is the vendor accessing the best available capabilities? Who are the technical and other partners?
- Transparency: How open is the vendor to robust peer review? Are they publishing enough data on milestones and progress?
As classical computing and AI improve, do they strengthen the case for quantum or push practical advantage further out?
This is an interesting question. On balance, we would say that neither is likely to materially affect the overall advantage offered by QCs.
It is true that advances in classical computing may, in some cases, make it harder to demonstrate initial “quantum advantage.” However, we see this more as a matter of hype than a meaningful milestone. There are already multiple claims of quantum advantage, and what matters more is the availability of a broadly usable device. Advances in classical computing are also unlikely to diminish the long-term advantage of QCs, as most early commercial applications will likely be hybrid. Many problems benefit from this approach, where the quantum processing unit handles only those aspects with an exponentially increasing solution space. In this sense, QCs are unlikely to substitute classical computing; rather, they will complement it for specific classes of problems.
We address the AI question in more detail in our Viewpoint (parts of which are summarized here). It is useful to consider it in two parts: first, how AI may affect QC development, and second, how QC availability may influence AI development and usage.
On the first point, AI has the potential to help tackle some of the most challenging aspects of QC development and engineering. From a hardware perspective, it could support improved calibration, error mitigation and control, as well as material and component discovery. On the software side, it may enhance algorithm design, optimisation, and data analysis. Taken together, this suggests AI could help accelerate progress towards quantum advantage.
On the second point, the use of quantum processing to improve AI remains more speculative. In principle, quantum parallelism could accelerate AI training and inference, potentially reducing its significant energy demands. However, a major constraint is the requirement to load large volumes of data, which is currently inefficient on quantum systems. This challenge may be mitigated where the data is already in quantum form, such as from quantum sensors. Overall, AI and QC integration is still at an early stage.
What should real “quantum readiness” look like for industries like pharma, chemicals, finance, and logistics over the next three to five years?
We would say that quantum readiness should involve having potential QC application areas already identified, assessed, and screened; being actively engaged in the ecosystem, with specific key partnerships in place; and developing in-house capability, particularly in “quantum application engineering,” which differs from quantum science itself and may also include some involvement in quantum algorithm development.
If advising a board today, would you recommend preparing now for a long-term quantum strategy or waiting for clearer economic proof?
This will depend on the industry. For those where activities such as drug discovery, molecular simulation, and operational simulation are central to business success (for example, pharma, chemicals, logistics, finance, and defence), we would advise boards to start preparing now in the way outlined in 2 above.
For other industries, we suggest placing greater emphasis on application identification, screening, and ongoing monitoring. There is still some uncertainty, so any investment should be considered within the context of the broader R&D portfolio. Companies should also continue to track developments in conventional computing and AI in parallel.
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About the Speakers
Albert Meige is Associate Director and Global Director of Blue Shift at Arthur D. Little, where he focuses on innovation and digital transformation. He is an entrepreneur, academic, and author with a background in computational physics, telecom engineering, and business.
Rick Eagar has 30 years of experience in management consulting with Arthur D. Little, holding senior positions including Managing Partner of the firm's global Innovation practice, and Chief Innovation Officer. For the last 6 years he has acted as a senior adviser to Arthur D. Little's Blue Shift future technologies institute.
Disclaimer: The views expressed here are those of the interviewee and do not necessarily represent the views of AZoM.com Limited (T/A) AZoNetwork, the owner and operator of this website. This disclaimer forms part of the Terms and Conditions of use of this website.