Quantum-Assisted Space Logistics Mission Planning

TL;DR

This work tackles the challenge of long-horizon, multi-commodity space logistics planning by formulating it as a time-expanded MCNF and solving it with entropy quantum computing (EQC). A Hamiltonian encoding is constructed to represent the objective and constraints, leveraging EQC’s photonic time-bin qudits to explore large solution spaces via adiabatic-like dynamics. On a test network with Earth, lunar, and Mars nodes, the Dirac-3 EQC hardware produced feasible vehicle schedules and commodity flows, achieving a best energy solution within a few seconds per sample, though not globally optimal due to a residual extra vehicle. The study demonstrates the viability of quantum-assisted frameworks for complex aerospace logistics and points to future scalability and cross-domain applications in mission planning and supply-chain optimization.

Abstract

Quantum computing provides a novel approach to addressing conventionally intractable issues in large-scale optimization. Space logistics missions require the efficient routing of payloads, spacecraft, and resources across complex networks, often resulting in an exponential growth of the solution space that classical methods cannot efficiently solve. This paper leverages entropy quantum computing to model and solve the space logistics problem as a time-dependent multicommodity network flow, enabling the exploration of large solution spaces. The findings highlight quantum computing's potential to address complex aerospace logistics, demonstrating its suitability for complex interplanetary mission planning.

Figures (3)

• Figure 1: Network model
• Figure 2: Distribution of Sample Energies
• Figure 3: Distribution of Sample Runtimes