Quantum Thinking Can Help You Solve Complex Strategy Challenges
by Graham Kenny and Ganna Pogrebna
November 21, 2025
A wicked problem in strategy is one that defies a straightforward solution. The term is used to describe complex problems that refuse conventional problem-solving.
Many of todayâs strategic challenges qualify as wicked problems. Traditional analytics breaks under the complexity of clashing stakeholder interests, intertwined underlying causes and outcomes that shift with each intervention. The good news is that these problems have intensified in the same moment that a new conceptual framework has arrived which absolutely thrives on complexity: quantum modelling.
When HSBC and IBM recently reported that an experiment in bond trading using a quantum computer had indicated that the technology could improve performance by 34%, the news made global headlines as âground-breakingâ and proof that we are âon the cusp of a new frontier.â But perhaps a bigger story that hasnât generated the same headlines is that you donât need a quantum computer (still likely years away and very expensive) to harness quantum advantage. Businesses are already applying modelling concepts based on the theories and math of quantum mechanics using conventional computers.
What sets quantum modelling apart is its ability to work with interdependence at a scale that traditional modelling struggles to handle. Traditional strategy depends on regression analysis, for instance, to find the relationship between a dependent variable or outcome (Y) and a set of independently observable variables (X1, X2, etc.) that cause Y. As a rule, all the independent variables are largely assumed to be causally independent of each other, which in real life is often not the case.
Thatâs where quantum modeling helps. It uses a variety of mathematical methods to not only evaluate the relationships between outcome (Y) and causes (Xs) but also accounts for groupings of causesâall pairs, triplets, and clusters of entangled X variables that best explain the dependent variable. Quantum modelling looks for the best ways to explain the outcome when multiple causes become entwined; its strength is in modelling a vast array of cause combinations.
The quantum revolution has huge implications for how we make decisions into the future. Here we demonstrate, via three typical business problems, how quantum modelling is being applied to wicked strategy problems using conventional computing technology.
Problem 1: Optimizing logistics
A global freight and supply chain operator weâll call K-Logistics, faced a wicked strategy problem. It wanted to investigate alternate supply routes in terms of the total cost of its end-to-end logistics, which were becoming increasingly unpredictable thanks to escalating fuel prices, tightening emissions regulations, and a volatile geopolitical environment. Traditional planning tools from operations research, like integer linear programming, were not up to the task of handling the complex interdependencies of the variables involved.
How quantum helped
Liam, the Chief Technology Officer, decided to reframe the total-cost problem in quantum terms, viewing cost as a set of dynamic outcomes involving multiple interdependent variables evaluated simultaneously. Applying a tool called quantum annealing, he identified a recommended route that was lower in cost than the recommendations generated by traditional operating models.
As Liam explained: âThe traditional model had us comparing the cost of two seemingly similar East Asia-to-Europe shipping routes. But the quantum logic simulator (quantum annealing) which we developed in partnership with a boutique software provider, flagged a different route as optimal by considering routing, timing, and fuel costs and their downstream consequences all at the same time. The simulator mimicked superposition, allowing us to explore overlapping realities.â
Problem 2: Assessing drug effectiveness
Marta is Head of Strategic Foresight at a global pharmaceutical company weâll call Telara Pharma. The companyâs wicked problem involved analyzing field data to assess drug effectiveness in terms of absorption rate with a view to prioritizing investments in clinical research and development. As Marta explained: âOur traditional modelling procedures for evaluating absorption rate assumed well-defined independent variables. But in drug trials, everything is entangled. Absorption rate is affected by target patient group which interacts with other variables like trial location, different dosage levels and when the drug is taken.â
How quantum helped
Telara Pharma partnered with a business-university innovation accelerator lab and implemented quantum modelling for data analysis to evaluate many possible variable combinations simultaneously. As Marta told us: âTo improve the absorption rate using a traditional model we would have recalculated these impacts mathematically one at a time, e.g., different dosages or intakes at different times of the day. The quantum reasoning simulator, quantum Bayesian networks, allowed us to evaluate the ripple effects of dozens of interdependencies from a series of conditional options. This helped us to analyze a drugâs absorption rate much more effectively.â
She concluded. âAdopting quantum modelling for testing drug effectiveness means letting go of traditional rigid, linear decision trees. It involves embracing ambiguity, holding multiple possible outcomes in tension and acknowledging that decisions in one part of the system affect all others. These are the hallmarks of entanglement in modern strategic environments.â
Problem 3: Strengthening risk modelling
A financial services firm specializing in risk and fraud detection, weâve labelled Zentrix Capital, found itself facing a wicked problem. The firm wanted to decrease the incidence of fraudulent activity for clients and wished to detect new fraud trends in high-volume transaction environments. The usual analysis was falling short.
Traditional models treated variables such as transaction size, frequency of account access, access location, account age, and customer profile as independent. This often obscured the subtle, complex interrelationships that underpin fraudulent behaviorâfor example, the frequency of access to an account is often related to where the account is located.
How quantum helped
The senior team, led by Gustavo, the firmâs Chief Data Scientist, applied a variational quantum feature selection algorithm, leveraging quantum simulators on traditional computing. The quantum-inspired approach enabled the interaction of multiple variables such as transaction size, frequency, location, account age, and customer profile to be considered concurrently.
As Gustavo told us: âWe partnered with a local university to examine a case involving a burst of small international transactions originating from dormant accounts. Traditionally, we would assess variables like transaction frequency or account activity one at a time. But the quantum logic model considered combinations like transaction value with customer activity (new device login, recent password reset) and other network-level anomalies all at once. It didnât force us to decide upfront which variables mattered most.â
He explained that the results obtained via quantum modelling caught a new type of synthetic identity fraud that would have slipped through conventional safeguarding protocols. Gustavo concluded: âQuantum modelling helped surface variable combinations we hadnât previously considered by evaluating multiple interdependent patterns simultaneously rather than isolating variables one at a time.â
Your Quantum Modelling Future
Many executives hear âquantumâ and think of cryptography, cyber resilience, or physics labs â not strategy. Yet, quantum modelling doesnât require a quantum computer, a PhD, or a lab. It begins with changing how your organization thinks about uncertainty.
- Start smallâand learn fast. Pick one messy, high-stakes area where conventional analytics falterâa supply-chain disruption, a regulatory forecast, or a risk situation. Treat it as a pilot. The aim isnât initially to replace your current tools but to see how interdependent variables behave when analyzed together, rather than one after another. Thatâs exactly how HSBC approached quantum modeling: instead of rebuilding its whole bond trading system, the bank isolated one volatile sub-model and ran it through a quantum-inspired simulator. The resultâa 30 per cent performance gainâcame not from more data, but from modelling relationships between data points differently. Telara Pharma did the same with compound absorption rates, KâLogistics with shipping routes, and Zentrix with fraud detection. Each started with a small and volatile subset of the system to explore complex interdependencies where traditional models fell short.
- Partner before you invest. Most advances in this field come through collaboration rather than solo experimentation. HSBC, for example, joined forces with IBM to access specialized expertise and quantum-inspired simulation tools without committing to major infrastructure costs. That partnership let the bank test its ideas quickly, learn from the results and scale what worked. Yet large technology providers arenât the only option. As K-Logistics did, you can engage boutique analytics firms; or follow the path of Telara Pharma by working with business-university innovation labs; or take Zentrixâs approach by partnering with university spin-offs already testing quantum simulators on classical infrastructure.
- Develop quantum literacy. Just as leaders had to learn data science a decade ago, tomorrowâs leaders will need fluency in quantum reasoning, i.e., understanding entanglement, probabilistic thinking, and dynamic optimization. HSBCâs experiment was less about adopting new technology and more about building quantum literacy by developing the capacity to think in terms of connected and evolving probabilities. Telara Pharma, KâLogistics, and Zentrix each used their pilots not just for results, but also to build internal fluency in quantum concepts. So, assemble a cross-functional team of strategists, data scientists, and operational leads to interpret quantum modellingâs divergent outcomes together. The real value lies not in the novelty of the method itself, but in learning together and discovering insights that traditional tools consistently miss.
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Build quantum fluency now and youâll be better prepared to handle the wicked problems of the future that conventional logic simply canât untangle. The companies that master quantum thinking early will shape the next decade. The rest will be left catching up.
Graham Kenny is the CEO of Strategic Factors and author of Strategy Discovery. He is a recognized expert in strategy and performance measurement who helps managers, executives, and boards create successful organizations in the private, public, and not-for-profit sectors. He has been a professor of management in universities in the U.S. and Canada.
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Ganna Pogrebna is the David Trimble Chair at Queens University Belfast in Northern Ireland and a Lead for Behavioral Data Science at the UKâs Alan Turing Institute.
Source: https://hbr.org/2025/11/quantum-thinking-can-help-you-solve-complex-strategy-challenges
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1 Comments
I guess tge solution start from the stare of mind as we count on quantum thinking to deal with the wicked probelm then choosing the convenient quantum tool , and finally Showing the results after tested via Quantum Modelling