Insider Brief
- Companies are beginning to use quantum-inspired modelling to address “wicked problems” that break traditional linear analytics.
- Case studies in logistics, pharmaceuticals, and financial risk show firms applying tools such as quantum annealing, quantum Bayesian networks, and variational feature-selection on classical hardware.
- The researchers argue that this shift signals rising quantum literacy, early enterprise adoption through modelling rather than hardware, and new partnerships forming ahead of large-scale quantum processors.
Companies wrestling with today’s most complex strategic decisions are running into a familiar bottleneck: traditional analytics tools struggle to model systems where variables don’t behave independently. In many sectors — logistics, pharmaceuticals, and financial risk among them — leaders face problems where causes interact, shift, reinforce, or cancel one another in ways classical decision trees rarely capture.
These “wicked problems” share a structure that breaks regression-based models and the linear assumptions embedded in decades of management science, according to Graham Kenny, CEO of Strategic Factors and author of Strategy Discovery, and Ganna Pogrebna, the David Trimble Chair at Queen’s University Belfast and a Lead for Behavioral Data Science at the Alan Turing Institute.
In a recent piece in Harvard Business Review, the researchers spotlight a trend emerging across industries: companies are beginning to approach strategy problems using quantum-inspired modelling — the application of quantum-native mathematical structures on classical computers.
They suggest this trend has implications for the quantum industry.
While recent headlines focused on HSBC and IBM’s bond-trading experiment showing a 34% performance improvement on a quantum processor, Kenny and Pogrebna point out a broader, less publicized shift: enterprises are already using quantum-style reasoning to capture analytical advantages — without quantum hardware.
Several firms, across unrelated sectors, are leveraging quantum annealing logic, quantum Bayesian networks and variational quantum feature-selection algorithms built entirely for classical infrastructure, according to the researchers. The trend suggests that quantum-native thinking is entering enterprise decision-making earlier than many hardware roadmaps predicted.
Why Traditional Models Break on Wicked Problems
Conventional strategy tools assume that a dependent outcome (Y) can be explained by a set of largely independent variables (X1, X2, X3). Even advanced regression techniques usually treat these variables as separable or only lightly correlated.
Real-world conditions rarely behave that way, the researchers report. Reality is messy, some might say, or reality is quantum, Richard P. Feynman might be paraphrased
For example, In logistics, fuel price volatility interacts with routing, timing, emissions rules and geopolitical risk. In pharmaceuticals, patient characteristics, dosage levels, trial locations and administration schedules influence one another in ways that invalidate clean statistical separation. In fraud detection, transaction value, account access patterns, device behavior and account age reinforce and distort one another.
Kenny and Pogrebna argue that this is precisely the analytical gap quantum modelling fills. Instead of isolating variables, quantum-inspired tools evaluate groups of them — pairs, triplets, clusters — whose combined behavior more accurately explains an outcome. The method mirrors the logic of entanglement: results depend not on single causes, but on how sets of causes behave together.
For organizations trained on deterministic decision trees, the shift is significant. It requires simultaneous evaluation of multiple overlapping states rather than linear, stepwise reasoning.
Here are a few of the case studies covered by the researchers.
Case 1: Logistics — Modelling Overlapping Realities
Global freight operator K-Logistics faced increasing instability in its end-to-end supply chain. Escalating fuel prices, tightening emissions regulations and geopolitical uncertainty made route comparisons unreliable. Traditional operations research tools, including integer linear programming, couldn’t fully capture cross-variable interdependencies.
The company reframed the task using quantum logic.
By applying quantum annealing on a classical simulator, the CTO’s team evaluated routing, timing, and fuel costs simultaneously. The tool, developed with a boutique software partner, “mimicked superposition,” allowing the team to analyze overlapping route scenarios at once.
The result surfaced an optimal route that conventional models had overlooked.
Case 2: Pharmaceuticals — Entangled Variables in Drug Absorption
At Telara Pharma, the challenge was understanding drug absorption rates across multiple trial conditions. Traditional models require treating factors like dosage or timing as independent levers.
But, as the company’s Head of Strategic Foresight told the researchers, the situation is more “entangled”.
“Our traditional modelling procedures for evaluating absorption rate assumed well-defined independent variables,” the executive said. “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.”
Telara implemented quantum Bayesian networks to analyze dozens of conditional relationships in parallel. Instead of recalculating absorption rate impacts one variable at a time, the quantum-inspired simulator evaluated interacting variables, including patient group, trial location, dosage, timing, simultaneously. The approach yielded a clearer understanding of how combined factors influenced drug absorption and helped the company prioritize clinical investments.
The executive concluded that the approach meant “letting go of traditional rigid, linear decision trees” in favor of embracing ambiguity and interdependence — attributes she connected directly to quantum reasoning.
Case 3: Risk and Fraud — Identifying Hidden Combinations
For Zentrix Capital, the challenge was detecting emerging fraud patterns in high-volume transaction flows. Traditional models treated indicators — transaction size, frequency of access, device usage, customer profile — as independent. But fraud signals tend to appear in combinations, not isolation.
Led by the firm’s Chief Data Scientist, Zentrix used a variational quantum feature-selection algorithm on classical compute. The tool analyzed combinations of factors — such as small international transfers paired with new-device logins and recent password resets — without requiring a prior ranking of which variables mattered most. The approach revealed a previously undetected synthetic-identity fraud pattern.
According to the data scientist, the quantum-inspired model enabled the team to evaluate interdependent patterns “all at once,” rather than isolating variables into rigid, predefined importance tiers.
Why This Matters for the Quantum Sector
Kenny and Pogrebna’s analysis points to a quiet but consequential trend: quantum-native modelling is entering enterprise workflows before large-scale quantum processors arrive. Three implications stand out for the quantum industry:
1. Quantum literacy is becoming a strategic competency.
Companies using these modelling tools build comfort with entangled variable structures and probabilistic reasoning — the same conceptual foundations needed for future quantum systems.
2. Early enterprise engagement is starting with modelling, not hardware.
Organizations are experimenting with small, volatile sub-systems using classical simulators, testing quantum-style formulations without capital-intensive commitments.
3. Partnerships are forming now.
Each example involves collaboration — with boutique software firms, university innovation labs, or university spin-offs — rather than large infrastructure projects.
This makes quantum-inspired modelling a practical on-ramp to quantum computing. It allows enterprises to structure problems in ways that later map to quantum environments, reducing friction when hardware matures.
A (Quantum) Competitive Advantage?
Ultimately, quantum modelling does not replace classical analytics, and it does not require quantum processors. What it does is change the structure of how companies analyze uncertainty.
As “wicked problems” become more common — defined by high interdependence and shifting outcomes — quantum-native logic offers a modelling framework suited to that reality.
Quantum thinking could be a competitive advantage, according to the researchers.
They conclude: “Build quantum fluency now and you’ll be better prepared to handle the wicked problems of the future that conventional logic simply can’t untangle. The companies that master quantum thinking early will shape the next decade. The rest will be left catching up.”
For more detail, read the entire article at Harvard Business Review.
