IBM Partners with ExxonMobil and SAP to Simulate Quantum Logistics Routing Models

Bridging Quantum Algorithms with Global Logistics Realities

In a pioneering move that underscored quantum computing’s relevance to large-scale supply chains, IBM in June 2020 revealed a strategic partnership with ExxonMobil and SAP. The effort aimed to model and simulate global shipping logistics, specifically optimizing routing of fuel transport vessels across intercontinental networks.

The initiative, anchored by IBM’s Q Network, tapped into the company’s open-source quantum development framework Qiskit and its then state-of-the-art 53-qubit quantum computer. The collaboration’s objective was to validate quantum computing’s potential for real-world logistics route optimization, especially in scenarios involving volatile fuel prices, unpredictable weather patterns, and emissions targets.

The Optimization Challenge: Crude Oil and LNG Transport

Shipping oil, gas, and refined products globally involves an array of variables: port availability, vessel speed constraints, tides, customs regulations, and real-time demand fluctuations. Classical route optimization, while effective, often hits a wall when layering multiple constraints across numerous nodes.

In ExxonMobil’s case, each shipment route must consider:

• Market-driven pricing across endpoints (e.g., Brent vs. WTI crude differentials)

• Fuel consumption rates impacted by weather

• Timing of vessel maintenance windows

• Environmental compliance (e.g., IMO 2020 sulfur regulations)

By encoding these logistics decisions into Quadratic Unconstrained Binary Optimization (QUBO) models, IBM and SAP researchers began testing hybrid quantum-classical algorithms on simulators and real quantum hardware.

A Look Inside the Quantum Pilot

1. Encoding Real Variables into Quantum Representations

The Qiskit team worked closely with SAP’s business logistics engineers to convert classical inputs—such as port turnaround times, bunker fuel costs, and geopolitical risk weights—into binary form suitable for quantum optimization engines. They used variational quantum algorithms (VQAs) for smaller subproblems and mapped complex routing trees to Ising models solvable on gate-model devices.

2. Hybrid Simulation and Edge Deployment

Given current qubit limitations, most quantum execution happened via hybrid co-processing. IBM’s simulator ran part of the combinatorial problem on classical hardware, while delegating optimization-heavy segments to actual quantum devices for sampling potential solutions. These outputs were then compared with SAP's classical route engines to assess improvements.

Key Findings from the Q2 2020 Experimentation

While the project remained in the early stages, some takeaways by June 2020 included:

• Faster constraint evaluation: Quantum-enhanced solvers explored larger option spaces for vessel rerouting under port closures due to COVID-19 without significantly increasing compute time.

• Fuel cost minimization: Simulations showed potential 3–6% gains in cost reduction under dynamic fuel pricing scenarios, though still within error margins.

• Proof-of-concept for hybrid execution: The use of VQE (Variational Quantum Eigensolver) algorithms for certain decision trees proved useful in managing turnaround and scheduling overlaps.

SAP contributed by visualizing the data in its logistics dashboard tools, linking back quantum-generated routing options into familiar enterprise formats.

Global Implications: Quantum in Oil & Gas Logistics

This initiative, although still in exploratory mode, revealed significant strategic alignment:

• ExxonMobil’s stake: As one of the world’s largest energy movers, any marginal gain in efficiency has multi-million-dollar implications. Even 1% improvement in fleet utilization can result in major operational savings.

• SAP’s interest: As an enterprise software leader in supply chain management, SAP sees quantum as a long-term differentiator for clients demanding tighter forecasting and real-time adaptability.

• IBM’s vision: By building use cases across industries, IBM reinforces the universality of its quantum platform beyond chemistry and materials science.

COVID-19 Context: Stress-Testing Supply Chains

The timing of the initiative could not have been more appropriate. In mid-2020, as the COVID-19 pandemic disrupted global shipping routes, many energy companies sought new modeling tools to forecast port delays, crew quarantines, and regulatory bottlenecks.

Quantum logistics simulation, while not yet commercially deployed, provided a new layer of optionality planning—a way to run multiple “what-if” scenarios at once.

Other Parallel Developments in June 2020

The IBM-ExxonMobil-SAP pilot wasn’t alone in advancing quantum logistics during June:

• Xanadu and DHL (Canada): The Toronto-based photonic quantum startup began discussions with DHL’s Toronto logistics hub on using its PennyLane platform to model last-mile delivery optimization under traffic and fuel constraints.

• Nippon Yusen Kaisha (NYK Line, Japan): Partnered with Denso and NICT to begin exploring secure vessel communications using post-quantum encryption, aimed at building resilience into maritime data flows.

• U.S. Air Force Logistics Command: Issued a grant to a consortium of startups to evaluate quantum-enhanced MRO (maintenance, repair, overhaul) decision frameworks for aircraft part logistics under wartime constraints.

Conclusion: A Cautious But Steady Shift

June 2020 marked a milestone where quantum computing, still largely theoretical in many industries, began showing operational utility in logistics—particularly in high-value, high-complexity networks such as global fuel shipping.

The IBM-led pilot didn’t claim full commercial readiness. Yet it delivered something equally valuable: validation that hybrid quantum-classical systems can address real-world logistics constraints in ways classical systems alone struggle with.

As quantum hardware scales and hybrid software layers mature, the energy logistics sector—facing price volatility, regulatory pressure, and digital transformation—may emerge as a proving ground for quantum-enhanced planning. The groundwork laid in 2020 will likely inform quantum deployment strategies for the rest of the decade.