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Quantum’s balancing act: Exploring three common conflicts within the quantum roadmap for life sciences organizations

Gireesh Kumar Neelakantaiah
9 Dec 2022

To many life sciences organizations, 2030 may seem like a lifetime away. But when it comes to quantum technology, is it really?

There are investments to be made, teams to be assembled, use cases to be defined, partnerships to be forged, and, most importantly, a strategy to be set. Within this context, the road to 2030 suddenly looks a lot shorter.

In quantum, as with any nascent technology, finding the right approach is a matter of balance. The opportunity provided by quantum computing across the drug development lifecycle is undeniable, but the technology is simply not ready to be used at scale today. Companies need to make investments, but with so much uncertainty, where should they focus their efforts to generate a near-term value and be well-positioned in the future?

In this post, we outline three common conflicts within the quantum roadmap and how life sciences organizations should approach these issues to balance short-term return with long-term success.

Conflict 1: Where should the quantum computing team “live” within the life sciences organization?

As life sciences organizations build out their quantum computing capabilities, many struggle to decide where these teams and people should live within the business. Should the quantum team be part of R&D? Should these professionals be embedded within different discovery teams? Should they operate as a stand-alone function?

To some extent, the answer – at least in the short-term – depends on the nature of the organization. For example, some big pharma companies may find it helpful to set up specialist quantum groups within R&D or an innovation stream. Startups, on the other hand, may integrate quantum specialists directly within drug discovery teams. 

While there may be some variety in the early-stage strategy, companies should recognize that capturing the full value of the quantum investment over the long term will likely require the support of a centralized, formal team – as opposed to siloed groups or individual enthusiasts sprinkled throughout the organization.

To that end, companies should work towards establishing a quantum technology center of excellence (COE) to:

  • Bring together early-stage teams, as well as new resources, and unite them under a common strategy;
  • Serve as an organizing body for all quantum applications within the business and take a transversal view of all use cases; and
  • Help the team share resources and best practices in a more efficient way.

At the same time, life sciences organizations should acknowledge that the structure of the quantum team will evolve over time. For example, while the COE model might be optimal over the next several years, as the company matures, it may make more sense to embed specialist teams in multiple different areas of the business. Over the long term, it’s possible that quantum will become a core competency of many groups across R&D, as well as the innovation team, making the need for a COE or specialty team obsolete.

Acknowledging that the structure and organization of the quantum computing team will evolve over time is an important consideration to keep in mind even at the earliest stages of the program. This will help ensure that the company designs and manages the team in a fluid way, allowing the quantum function to adapt in time with the organization’s changing needs, level of maturity, and technology advances.  

Conflict 2: Leading with use cases vs. being technology driven

From accelerating drug development through molecular design to creating manufacturing capacity at scale, quantum computing represents a strong opportunity for life sciences companies in many aspects of the business.

And therein lies the problem: with the world of possibility so great, it can be difficult to focus limited resources in the right place at the right time to generate the maximum return now and in the future.

Practically speaking, companies need to be clear at the outset of their quantum program about the problems they want to solve – and realistic about when the technology will be available to help them do so. Some use cases, such as molecular design, look promising today and are likely to demonstrate a return in the nearer term. Others, like large-scale optimization of manufacturing and supply, are more speculative in nature; investments in these areas likely won’t demonstrate a return for up to a decade or more.

It may be helpful to develop a quantum radar that tracks the application of quantum technology according to feasibility and timescale. This exercise could help the company crystalize what it wants to achieve, as well as assess if there is technology that could help them deliver and, if so, when it will be available. Of course, estimating the timescale when different applications could deliver value is not always straightforward and may itself require detailed research. Involving quantum and domain subject matter experts or a cross-disciplinary COE could help improve the accuracy of such estimates and better evaluate the use of the technology with respect to specific use cases.

At the same time, while it is essential to ground the strategy in specific use cases, especially in the short term, it’s equally important to continue to experiment with the technology. This means that even as companies focus on nearer-term applications, they should make a calibrated investment in the technology and build expertise so that the organization can execute more advanced use cases as time goes on. This helps ensure the company is ready to scale and grow as advances in quantum technology are made. This is especially important for life sciences companies that ultimately want to become leaders in this technology – and not just treat quantum as a fringe effort or tactical response.  

Conflict 3: Building out the quantum team without investing massive resources

Forging a quantum future will require new teams, new roles, and new talent. But it is unlikely that even extremely large companies need – or want – to recruit for hundreds of new positions in this area today.

Instead, it may be possible to reorient existing R&D teams around this capability. This will involve identifying staff with relevant backgrounds and experience, such as experts in data science or artificial intelligence.

For example, one common idea is to take mathematical experts with experience in the life sciences domain and integrate them within the quantum team to translate business problems into the space of quantum algorithms.

In addition, it’s important to develop a partner network that can help fill the gaps that exist within the current quantum team. As part of this process, it will be important to select a company that has the requisite technical skills, as well as relevant domain expertise. This is essential for understanding how the technology can be applied to sector-specific use cases and other skills specific to the life sciences domain. Given the limited number of quantum physicists and quantum scientists at the PhD level available within the talent pool today, it is imperative that organizations be pragmatic about this issue.

That said, computing is but one aspect of the drug development process. Competitive advantage will not be found in quantum hardware but the people who work with it, developing the algorithms and applications that run on new machines. Organizations may need to endorse a variety of methods to make sure they have the talent they need to capitalize on this technology.

Remember: In the life sciences industry, real success is based on patient outcomes. Ultimately, companies need to harness quantum computing to introduce new therapies more quickly, as opposed to simply proving out the capabilities of the technology.

Charting the quantum future through conflicts and challenges

As organizations define their quantum technology strategy, they will certainly run into many challenges and conflicts along the way. With so much uncertainty within the field about the rate at which the technology will mature and when it will be ready for modern applications, it can be extremely difficult for organizations to know where, when, and how to invest resources.

Given this landscape, it’s important for organizations to approach this task with flexibility and adaptability in mind from the very outset of the program. As discussed above, three of the biggest program elements – structuring the team, defining use cases, and building capabilities – are likely to require a multi-phased and multi-faceted approach. This means that the organization must balance short- and long-term needs and constantly evaluate their program to generate the maximum return from this technology.

Author: Gireesh Kumar Neelakantaiah, with contributions from Sam Genway, James Hinchliffe, and Clément Brauner.

Gireesh Kumar Neelakantaiah

Global Strategy, Capgemini’s Quantum Lab
Leading go-to-market initiatives for the Quantum Lab, including solution development, strategic planning, business and commercial model innovation, and ecosystem partner and IP licensing management; Skilled in Quantum computing (IBM Qiskit), Data science, AI/ML/Deep learning, Digital manufacturing & Industrial IoT, Cloud computing.