IBM and Maersk Explore Quantum Risk Modeling in Global Shipping

Introduction: Tackling Uncertainty in Global Logistics with Quantum Models

Uncertainty has always been a central challenge in global shipping—whether from weather, political disruptions, port congestion, or supplier delays. But in the wake of COVID-19 and escalating trade volatility, traditional forecasting models often fell short of helping logistics giants plan with precision.

In January 2021, IBM Research and global shipping leader Maersk quietly launched an exploratory initiative: applying quantum computing to risk modeling in intercontinental shipping routes. This collaboration aimed to determine whether quantum probabilistic algorithms could help anticipate, simulate, and mitigate risk across one of the world’s most complex logistics networks.

The Context: Growing Need for Resilient Risk Modeling

By early 2021, Maersk was dealing with cascading logistical risks:

• Pandemic-induced crew change delays

• Port congestion from stimulus-driven e-commerce booms

• Climate-related disruptions (typhoons, cyclones, Arctic sea route volatility)

• Regulatory friction from shifting trade policies

Each of these variables affects not only one shipment, but entire supply chains.

Maersk, with a fleet that moves 12 million containers annually across 130 countries, wanted a next-gen modeling toolkit capable of capturing dynamic, multi-variable global risk. IBM suggested quantum computing as a promising path.

Quantum Probabilistic Models: A New Way to See Risk

IBM's proposal focused on using quantum-enhanced Monte Carlo simulations to estimate risk exposure and propagation across global shipping corridors.

Traditional Monte Carlo methods rely on repeated simulations to estimate the probability of complex events. But these are computationally expensive when working with:

• Multimodal transit systems

• Nonlinear interdependencies

• High-dimensional parameter spaces (e.g., hundreds of ports, weather systems, tariffs)

Quantum computing can accelerate and enrich this process through quantum amplitude estimation (QAE)—a technique that can reduce the number of simulations required while still maintaining high confidence in results.

Prototype Deployment: Mapping Risk on the Asia-Europe Corridor

The joint IBM-Maersk team selected the Asia-Europe shipping corridor—one of the busiest and most volatile trade routes—for a six-week simulation testbed in January 2021.

They modeled:

• Port delays in Singapore, Rotterdam, and Felixstowe

• Weather systems across the Indian Ocean and Bay of Bengal

• COVID-related bottlenecks at customs in Southern Europe

• Container imbalances from peak-to-trough demand cycles

Using IBM’s Qiskit application stack and a hybrid classical-quantum workflow on IBM’s superconducting quantum processors, they constructed risk maps highlighting:

• Port-level delay probabilities

• Expected container arrival deviations (in days)

• Cascading impact on downstream nodes (e.g., rail depots, warehouses)

Early Results: Quantum Advantage in Scenario Planning

Though still exploratory, the simulations yielded compelling indicators:

• 30–40% reduction in simulation steps compared to classical Monte Carlo for similar accuracy

• Faster convergence in high-dimensional risk scenarios, particularly when modeling weather volatility

• Probabilistic forecasts with 15–20% tighter confidence intervals

This meant Maersk planners could better understand the likelihood of late arrivals, disrupted warehouse schedules, or misaligned inland transport—before the disruptions unfolded.

Notably, quantum-enhanced models also allowed scenario generation under extreme but plausible conditions, enabling stress tests not feasible with classical tools alone.

Technical Framework: How It Worked

The joint framework integrated the following:

• IBM Qiskit + Aer simulator: Used to design and test quantum circuits encoding risk variables.

• Quantum amplitude estimation (QAE): Implemented to estimate distribution tails—critical for high-risk, low-probability events.

• Hybrid compute orchestration: Risk vectors were processed using both IBM quantum backends and classical HPC nodes for efficient blending.

• Data inputs: Sourced from Maersk’s TradeLens blockchain platform, maritime AIS data, global weather APIs, and port throughput records.

This hybrid system allowed teams to simulate thousands of route disruptions and delay chains across the corridor, visualized through a custom-built IBM dashboard.

Real-World Use Cases Explored

During the January 2021 pilot, three specific logistics applications were prototyped:

1. Early Warning System for Port Congestion
   Quantum simulations were used to predict ripple effects from port slowdowns (e.g., in Singapore) three hops ahead on the logistics chain.

2. Resilience Scoring for Route Alternatives
   Each shipping route was scored based on simulated risk propagation. This helped planners evaluate route swaps proactively (e.g., via Suez vs. Cape of Good Hope).

3. Insurance Pricing Inputs
   Output from quantum models was fed into actuarial models to dynamically price marine cargo insurance for high-risk corridors.

While none of these went immediately into production, they provided a glimpse of how quantum computing could reshape predictive logistics.

Organizational Integration: Culture Meets Quantum

IBM and Maersk also recognized that deploying quantum was not just a technical challenge—it was an organizational transformation.

• Maersk’s operations teams were engaged in modeling workshops to define key risk variables.

• IBM’s quantum researchers translated those into parameterized circuits, ensuring business interpretability.

• Visualization tools were designed to make probabilistic outputs digestible to operations planners unfamiliar with quantum mechanics.

This focus on human-machine collaboration was a key pillar of the pilot’s success.

Strategic Implications and Broader Impact

Maersk is not alone in facing multi-factor, multi-continent risk in logistics. The pilot holds potential relevance for:

• Retailers managing multi-sourced inventories

• Pharma companies with temperature-sensitive supply chains

• Aerospace firms reliant on just-in-time manufacturing

Quantum-enhanced risk modeling offers a way to see around corners—especially in an era of black swan events and fragile supply networks.

Remaining Hurdles

Despite strong early signals, key limitations were acknowledged:

• Hardware scale: IBM’s quantum systems were still in the 27-65 qubit range in early 2021, limiting scenario complexity.

• Model generalizability: Risk models had to be manually tuned for each corridor; scalable automation remained a challenge.

• Interpretability: Translating quantum outputs into actionable insights for operators still required custom interfaces and support.

To address these, IBM launched a logistics-focused quantum toolkit in Qiskit later in 2021, while Maersk began training its data science team on quantum concepts.

Conclusion: Toward Quantum-Resilient Shipping Networks

The January 2021 exploration by IBM and Maersk signaled a bold shift toward probabilistic, quantum-enhanced decision-making in logistics. While still pre-commercial, their work illustrates how quantum computing can augment human intuition in planning for uncertainty.

As quantum hardware matures and software toolchains improve, the ability to forecast risk with speed and nuance could become a decisive factor in global supply chain leadership.

Maersk’s quantum venture represents not just a tech upgrade, but a philosophical one—where planning doesn’t fight uncertainty, but embraces it with new tools from the quantum age.