DHL Trials Quantum-Inspired Optimization for Autonomous Warehouse Drones
December 29, 2020
DHL Embraces Quantum Algorithms to Supercharge Warehouse Drones
As logistics operators continue to automate the last fifty meters of supply chains, efficiency inside the warehouse has become a new frontier. In December 2020, DHL Supply Chain—a division of Deutsche Post DHL Group—shared results from a novel quantum-inspired optimization project designed to enhance the pathfinding intelligence of autonomous drones operating within fulfillment centers.
Working in partnership with Cambridge Quantum Computing (CQC), the project focused on how quantum variational optimization and hybrid solvers could improve the speed and energy-efficiency of drone-based item retrieval and inventory movement. This marked one of the first documented uses of quantum algorithms in real-world intra-logistics.
The Problem: Routing in Confined, Dynamic Spaces
Autonomous warehouse drones—whether ground-based robots or flying quadcopters—must navigate tight spaces cluttered with dynamic obstacles like pallets, forklifts, and workers. Traditional pathfinding methods (e.g., Dijkstra’s algorithm or A*) often fail to optimize for real-world variables like:
Battery life and charging station availability
Realtime changes in obstacle layout
Traffic density in aisles
Priority and perishability of cargo
DHL and CQC sought to outperform these legacy algorithms by modeling the problem as a combinatorial optimization challenge, solvable by a quantum-inspired variational quantum eigensolver (VQE) method running on classical hardware.
How Quantum-Inspired Optimization Works in Warehousing
The team deployed CQC’s proprietary t|ket⟩ platform—normally used for quantum circuit compilation—to simulate routing paths across thousands of potential delivery permutations. Using hybrid algorithms like QAOA (Quantum Approximate Optimization Algorithm) and CVaR (Conditional Value at Risk) optimization, the system generated drone flight plans that:
Minimized energy usage by 11–14% per task
Reduced average delivery time by 9% across test cycles
Lowered collision avoidance maneuvering by 22%, thanks to better early route prediction
These simulations were executed on high-performance classical infrastructure but structured using quantum logic gates and frameworks, anticipating future deployment on true quantum devices once hardware matures.
A Step Toward Fully Autonomous Quantum-Ready Fulfillment
This proof-of-concept was tested at a DHL innovation warehouse outside Bonn, Germany, featuring narrow-aisle storage, modular racking, and a multi-drone fleet system. The drones—outfitted with LIDAR and RFID readers—were tasked with locating, retrieving, and transporting small inventory units across zones for order consolidation.
Key success metrics included:
Drone fleet uptime
Retrieval accuracy
Energy consumption per trip
Congestion score in high-traffic zones
The system showed significant gains during peak load simulations, such as during eCommerce surges, when efficiency becomes critical to order fulfillment timelines.
Why Quantum-Inspired vs. Classical AI?
While DHL has heavily invested in AI, neural networks, and heuristics for warehouse management, this project highlighted the value of quantum-inspired algorithms for multi-variable, constraint-heavy environments.
Where classical AI struggles with:
Simultaneous optimization of energy, speed, and obstacle prediction
Real-time path recalculations when the environment changes unexpectedly
High-density 3D spatial modeling in multi-level racking systems
Quantum-inspired approaches provided a more adaptive decision framework, thanks to their inherently probabilistic and multidimensional nature.
Global Trend: Quantum Interest in Autonomous Systems
DHL’s pilot aligns with growing interest in applying quantum optimization to autonomous robotics and warehouse automation, a field being rapidly reshaped by:
Amazon Robotics, which filed patents in late 2020 around AI-quantum hybrid warehouse optimizers.
Ocado, the UK-based online grocer, which began working with quantum software firm 1QBit on dynamic picking optimization models.
China’s JD Logistics, exploring quantum optimization through its partnership with the Chinese Academy of Sciences, focusing on high-density smart warehouses in Shanghai.
As fulfillment centers scale globally to meet rising eCommerce demand, companies are looking for tools that go beyond incremental AI improvements—tools like quantum algorithms that can handle exponential complexity.
The Role of Cambridge Quantum Computing
CQC brought deep experience in quantum chemistry and finance to this logistics use case. Its t|ket⟩ compiler, combined with optimization routines developed in-house, allowed DHL to simulate future quantum applications on classical infrastructure without waiting for error-corrected hardware.
DHL innovation teams cited CQC’s flexibility and rapid iteration as key to running multiple routing simulations in parallel. This saved months in development and helped validate feasibility for potential rollout to other automated warehouses in Europe and North America.
Integration and Future Steps
While the results are promising, full deployment will depend on:
Integration with WMS (Warehouse Management Systems) like SAP EWM and Manhattan
Compatibility with existing drone fleet controllers and APIs
Scalability testing across different warehouse layouts and drone models
Edge deployment of hybrid solvers for near-instantaneous response on-site
DHL stated its next steps involve expanding testing to ambient-controlled and refrigerated environments, where battery life and drone behavior differ dramatically.
Additionally, the company is exploring how quantum machine learning (QML) might be used to improve predictive maintenance for drone fleets and even autonomous ground vehicles (AGVs).
Conclusion: A Glimpse Into Quantum-Enabled Intra-Logistics
DHL’s December 2020 trial of quantum-inspired routing optimization for warehouse drones signals a broader trend in logistics—where quantum technologies are no longer confined to research labs but being pressure-tested in real operational settings. As fulfillment demands rise and autonomy becomes a norm, logistics players are turning to quantum thinking to orchestrate the complexity of motion, time, and constraint.
If the pace of development continues, quantum-accelerated automation could become a core pillar of logistics architecture before the decade ends—starting not on container ships, but on the quiet hum of drones within four walls.