Quantum Algorithm Design and Its Implementation for Solving Test Sheet Composition Optimization Using a Quantum Annealing Approach

In testing systems, Item Response Theory (IRT) is a widely used model for accurately synthesizing user response information. However, compared to Classical Test Theory (CTT) approaches, it imposes a higher computational burden and increases system design complexity. Quantum computing has shown promise in alleviating these computational challenges. Currently, general-purpose quantum computers are still in a relatively early stage of development. However, special-purpose quantum computing architectures have been designed to solve combinatorial optimization problems, attracting significant attention across various fields. These systems enable researchers to tackle domain-specific optimization problems with reduced computational time. To the best of our knowledge, no applications of quantum computing have been proposed in the field of educational technology. This study therefore aimed to design a quantum QUBO (Quadratic Unconstrained Binary Optimization) formulation for optimizing test sheet composition. The proposed model can be implemented on practical quantum Ising machines (or digital quantum Ising machines for larger qubit usage) to evaluate system efficiency. Simulation results demonstrate that the proposed approach outperforms traditional methods, including the genetic algorithm and particle swarm optimization algorithm, in terms of computational efficiency.