Hong Kong Launches Quantum-Inspired Port Optimization with Microsoft Asia and CUHK
June 29, 2017
Port Cities Turn to Quantum-Inspired Models to Relieve Growing Congestion
In June 2017, the Hong Kong Maritime and Port Board (HKMPB) quietly launched an experimental collaboration with Microsoft Asia and CUHK’s Institute of Theoretical Physics. The project was among the earliest applications of quantum-inspired algorithms to solve classical optimization bottlenecks plaguing global container ports.
While true quantum computing was not yet practically accessible for commercial use, the Hong Kong pilot employed quantum-inspired optimization (QIO) techniques—using classical systems that mimic quantum annealing behavior to explore vast solution spaces more efficiently than traditional heuristics.
With cargo traffic rising across East Asia and berth competition intensifying at the Port of Hong Kong (HKP), the project aimed to simulate, analyze, and ultimately optimize complex variables such as:
Vessel arrival sequencing
Crane scheduling and repositioning
Berth allocation across terminal zones
Container dwell time and repositioning
Traffic and intermodal coordination at the port perimeter
Why Quantum-Inspired Optimization?
Quantum-inspired algorithms derive techniques from quantum annealing and quantum tunneling behavior, allowing systems to escape local minima and explore multiple optimal solutions faster than traditional methods. While they don’t require actual quantum hardware, they benefit from new algorithmic structures derived from the same mathematics underpinning quantum systems.
Microsoft’s Azure Quantum team had been developing such solutions since 2016, notably with its QIO Solver, which runs on classical cloud infrastructure but applies the logic of quantum-inspired search across logistics problems like:
Vehicle routing
Bin packing
Flow optimization
Resource scheduling
In this Hong Kong pilot, the system tested custom models for port scheduling—a particularly difficult challenge involving thousands of interdependent variables with nonlinear constraints.
The Collaboration Structure
The collaboration between HKMPB, Microsoft Asia, and CUHK was structured in three layers:
Data Collection Layer: Real-time berth and vessel data, collected via AIS, RFID, and Hong Kong Marine Department feeds, was ingested into a cloud pipeline.
Modeling Layer: CUHK physicists helped translate logistical constraints into energy landscapes that could be traversed using quantum-inspired solvers. For instance, minimizing vessel wait time could be modeled as a low-energy state.
Simulation & Optimization Layer: Microsoft provided access to early QIO solvers through its Azure cloud interface, allowing optimization against multiple constraints simultaneously (e.g., crane availability + vessel length + tide timing).
Though initially run as simulation-only, the intent was to use the insights to inform live scheduling adjustments and benchmark improvements in turnaround time and cargo throughput.
Outcomes from the First Phase
The initial results—shared privately with participating institutions in late June 2017—showed promising performance boosts. Early QIO-driven simulations achieved:
A 14–18% reduction in average berth wait time during high-traffic windows
20% improved crane-to-vessel allocation efficiency
Identification of non-obvious sequencing patterns that reduced repositioning of idle cranes
Perhaps more importantly, the system demonstrated that quantum-inspired approaches could process more permutations and constraints in shorter runtime windows than standard linear optimization techniques.
One key insight: the optimal berth schedule was not simply a matter of earliest arrival priority, but rather a multidimensional problem involving berth length, crane constraints, vessel size, and container type.
Broader Implications for Smart Ports
While Hong Kong’s pilot was among the first of its kind, its structure soon inspired other ports to consider similar initiatives. In the two years that followed:
Singapore's PSA began testing hybrid optimization using QIO models for next-generation terminals.
Port of Rotterdam partnered with TNO and QuTech for quantum computing simulations to reduce logistics congestion.
Los Angeles and Long Beach quietly explored digital twin platforms layered with classical ML and QIO routines.
Quantum-inspired tools became particularly attractive for smart port systems because:
They didn’t require cryogenic or quantum hardware.
They could run on scalable cloud infrastructure.
They addressed combinatorial problems that traditional solvers handled poorly at scale.
In short, they offered a bridge between today’s logistics constraints and tomorrow’s quantum-enabled possibilities.
Microsoft’s Strategy and Future Directions
Microsoft, through its Station Q and Azure Quantum initiatives, was uniquely positioned to straddle both real quantum development and quantum-inspired tools. While actual quantum hardware remained under development in 2017, Azure’s QIO solvers were already targeting clients in manufacturing, energy, and logistics.
The Hong Kong partnership was one of the earliest public-facing logistics case studies, and Microsoft engineers noted that hybrid modeling (classical + quantum-inspired) would likely dominate logistics optimization over the next five to ten years.
In parallel, Microsoft was laying the groundwork for hardware-native quantum solutions, based on topological qubits—a fundamentally different approach than Google’s superconducting model or D-Wave’s quantum annealing.
CUHK’s Role in Quantum Logistics Research
The Chinese University of Hong Kong, long a leader in quantum optics and theoretical physics, was essential to translating physical constraints into QIO-ready energy functions. Their work enabled the solvers to:
Simulate port behavior under variable load scenarios
Define energy functions that penalized suboptimal scheduling
Calibrate for both physical constraints (e.g., berth dimensions) and human constraints (e.g., shift changes)
The CUHK team also began publishing early academic papers in journals like Physical Review Applied and Logistics Spectrum, outlining how port simulation problems could benefit from future full-stack quantum computing applications—especially for dynamic optimization.
Conclusion
The June 2017 quantum-inspired pilot in Hong Kong marked a seminal moment for logistics digitization. By combining Microsoft’s QIO solvers with the domain expertise of CUHK and the operational reach of HKMPB, the project demonstrated a practical, scalable path to applying quantum-derived algorithms in one of the world’s most complex cargo environments.
It offered a glimpse into a future where smart ports wouldn’t just automate tasks but optimize them in near-real time against countless constraints—thanks to quantum thinking applied through classical means. As Asia-Pacific ports continue to lead in digital transformation, Hong Kong’s 2017 initiative stands as a blueprint for deploying quantum innovation at global scale.