CGI Federal, the US Defense Logistics Agency (DLA), and the University of Tennessee have initiated a collaborative research program called Quantum Pathfinder. The aim is to explore how agent-based AI and quantum computing can enhance warehouse efficiency and the resilience of military supply chains.

In the initial phase, the partners will concentrate on three key areas: dynamic management of "smart" warehouses, reverse logistics, and inventory distribution amidst disrupted infrastructure. Specific use cases for the technologies are expected to be identified in the coming months.

The project is designed to evolve over the next fiscal year, with CGI Federal hosting Quantum Pathfinder at its Knoxville facility. Research findings, including benchmarks, will be shared at industry events and through various other formats.

The participants intend for quantum computing and agent-based AI systems to tackle challenges that traditional methods struggle with due to numerous variables and changing conditions. This includes the reconfiguration of logistics processes when ports, suppliers, or certain routes become unavailable. However, the program does not currently plan to deploy a fully operational quantum system within military infrastructure; it focuses on researching potential applications.

This initiative is also linked to the development of a quantum ecosystem in Tennessee. The University plans to advance its work through the Knoxville Quantum Accelerator and the Tennessee Quantum Initiative, while simultaneously enhancing research infrastructure and training specialists.

For the DLA, this focus is not entirely new, as the agency's budget materials for the 2026 fiscal year already outlined plans for researching quantum computing and quantum machine learning, along with further integration of AI/ML into supply chain management processes.

Notably, at the DefenseTalks conference in September, General Randall Reed, head of the US Army Transportation Command, discussed the advantages of employing AI in military logistics.