Quantum Logistics Enters the AI Era: Singapore’s NTU and Alibaba Unveil Hybrid Optimization Model

NTU and Alibaba Introduce New Quantum-Classical Logistics Framework

In a major step toward practical quantum logistics, researchers from Singapore’s Nanyang Technological University (NTU), in partnership with Alibaba’s Damo Academy, announced in mid-June 2019 the development of a hybrid optimization algorithm that leverages quantum annealing and classical reinforcement learning to tackle complex logistics routing problems.

The research was conducted under Alibaba’s broader investment in quantum computing, headquartered partly in Hangzhou, China, and supported by NTU’s School of Computer Science and Engineering. The hybrid approach was tested on vehicle routing problems common to last-mile delivery in dense urban environments like Singapore and Shanghai.

“By combining quantum annealing’s strength in global optimization with classical reinforcement learning’s adaptability, we’ve created a system that can make smarter, more sustainable logistics decisions in real time,” said Dr. Wei Feng, the NTU lead researcher on the project.

Tackling the Urban Routing Challenge with Quantum-Classical Synergy

The heart of the breakthrough lies in combining a quantum annealer — specifically, one of D-Wave’s Advantage QPUs leased via Alibaba Cloud — with a deep reinforcement learning model trained on dynamic city maps.

In the simulation tests, the hybrid system planned delivery routes for a fleet of electric vehicles across an urban grid with live traffic data and constraints on vehicle range, time windows, and emissions goals. The results showed an average reduction of 21% in delivery time and a 15% drop in estimated emissions compared to traditional routing algorithms.

This approach reflects a trend seen in global logistics players: quantum-classical systems are increasingly preferred over purely quantum solutions due to current hardware limitations. Instead of waiting for fully fault-tolerant quantum computers, researchers are finding tangible gains in combining today’s quantum tech with advanced AI.

Alibaba’s Quantum Logistics Agenda

Alibaba has been steadily building its quantum computing and AI infrastructure since the mid-2010s. The company’s Damo Academy launched in 2017 with a $15 billion investment commitment into emerging tech, including quantum computing, AI, and IoT.

This June 2019 project is the latest in a string of efforts to make Alibaba’s logistics arm, Cainiao, one of the most intelligent and efficient supply chain platforms globally. Cainiao already operates smart warehouses and autonomous vehicles, and now aims to include quantum-enhanced optimization in its route planning systems for Southeast Asia.

“Quantum logistics optimization isn’t just about speed — it’s about resilience,” said Professor Xiang Li, a senior scientist at Damo Academy. “Our models can adapt to uncertainty, traffic shocks, and real-world data noise better than classical-only systems.”

Global Implications and Competitive Response

NTU and Alibaba’s announcement drew interest from international logistics giants and researchers. UPS and FedEx — both already experimenting with quantum-inspired solutions — are reported to be exploring similar hybrid models for hub-and-spoke optimization and air freight routing.

In Germany, DHL’s Innovation Center in Bonn released a white paper in June 2019 outlining their initial steps into hybrid quantum computing, citing Alibaba’s results as a motivator.

Meanwhile, Japan’s logistics innovation startup “LogiQTech” announced a $7 million Series A funding round to build a similar hybrid system using Fujitsu’s Digital Annealer and reinforcement learning to optimize Tokyo’s complex delivery networks.

Research Validation and Technical Milestones

The NTU-Alibaba project’s findings were peer-reviewed and accepted at the 2019 ACM Symposium on Quantum Algorithms in Logistics (QAL’19), where the team presented their methodology, architecture, and results.

The quantum-classical model employed a reinforcement learning agent trained in Python using TensorFlow, while the quantum component used the D-Wave Ocean SDK to formulate QUBO problems for real-time processing.

Key innovations included:

• Adaptive reward shaping to guide quantum sampling.

• Dynamic re-weighting of QUBO constraints based on vehicle energy use.

• Integration with LiDAR-based traffic detection APIs for real-world inputs.

A Glimpse into Asia’s Quantum-Enabled 

Smart Cities

Singapore and Hangzhou are quickly becoming Asia’s premier quantum logistics testbeds. With government support, research funding, and close proximity to eCommerce giants, these cities are ideal environments for piloting hybrid quantum-classical systems.

Singapore’s Land Transport Authority (LTA) has expressed interest in testing such systems on its electric shuttle programs, while Alibaba is reportedly in talks with Malaysian authorities to deploy quantum-optimized logistics routing for its Southeast Asia delivery operations.

As global supply chains continue to digitize and decentralize, the NTU-Alibaba collaboration offers a glimpse into the near-future: where logistics routes aren’t just mapped but strategically computed through hybrid intelligence systems.

Conclusion: From Research to Real-World Results

The NTU-Alibaba breakthrough in June 2019 underscores the growing maturity of hybrid quantum-classical logistics models. While fully quantum supply chain optimization is still years away, this collaboration shows that incremental integration — using the best of classical AI and near-term quantum computing — can already yield measurable operational improvements.

As quantum hardware improves and AI continues to evolve, partnerships like this one will likely define the next decade of smart, sustainable logistics across Asia and beyond.