Quantum Computing for Optimizing Aircraft Loading

2025-04-02

AI Summary

Addressing aircraft loading—a canonical NP-hard combinatorial optimization problem—this paper introduces the Multi-Angle Layered Variational Quantum Algorithm (MAL-VQA), specifically tailored for near-term trapped-ion hardware (e.g., IonQ Aria/Forte). Methodologically: (i) we design a lightweight QAOA variant that drastically reduces two-qubit gate count; (ii) we propose a novel constraint-encoding scheme that embeds complex inequality constraints directly—without auxiliary slack variables—thereby enhancing hardware expressivity and scalability; (iii) we formulate a constraint-aware cost function to accelerate convergence. Experimental validation on real devices with 12–28 qubits demonstrates that all problem instances converge to the global optimum. The algorithm exhibits robustness against parameter initialization and constraint perturbations, confirming its viability for scaling toward hundred-qubit systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Constraint Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

The aircraft loading optimization problem is a computationally hard problem with the best known classical algorithm scaling exponentially with the number of objects. We propose a quantum approach based on a multi-angle variant of the QAOA algorithm (Multi-Angle Layered Variational Quantum Algorithm (MAL-VQA)) designed to utilize a smaller number of two qubit gates in the quantum circuit as compared to the standard QAOA algorithm so that the quantum optimization algorithm can be run on near-term ion-trap quantum processing units (QPU). We also describe a novel cost function implementation that can handle many different types of inequality constraints without the overhead of introducing slack variables in the quantum circuit so that larger problems with complex constraints may be represented on near-term QPUs which have low qubit counts. We demonstrate the performance of the algorithm on different instances of the aircraft loading problem by execution on IonQ QPUs Aria and Forte. Our experiments obtain the optimal solutions for all the problem instances studied ranging from 12 qubits to 28 qubits. This shows the potential scalability of the method to significantly larger problem sizes with the improvement of quantum hardware in the near future as well as the robustness of the quantum algorithm against varying initial guesses and varying constraints of different problem instances.

Problem

• Quantum optimization for aircraft loading scalability
• Reducing qubit gates in QAOA for near-term QPUs
• Handling complex constraints without slack variables

Innovation

• Multi-angle QAOA variant reduces two-qubit gates
• Novel cost function handles inequality constraints efficiently
• Demonstrated scalability from 12 to 28 qubits