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International Artificial Intelligence and Data Processing Symposium (IDAP)

2D versus 3D Bloch Sphere Representations in Quantum-Inspired Evolutionary Algorithms: An Empirical Comparison on Benchmark Functions

Büşra Özcan, Abdullah Ateş

Published Sep 1, 2026• 0 citations
Publisher
DOI
10.1109/idap71869.2026.11728231

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Manta ray foraging optimisation (MRFO) algorithm, a recent bio-inspired technique, and quantum-motivated computing have proven effective in solving complex combinatorial optimisation problems. Leveraging their qualities, we propose a continuous space optimisation approach that offers a novel combination of encoding and evolution of the chromosomes. The qubits in the quantum individual are encoded with the phase parameters and are based on Bloch representation. The phase angle-encoded qubit simplifies the expression and evolution of an individual. The proposed algorithm can search the optimised solution simultaneously on three coordinate axes of the Bloch sphere, to possibly achieve better convergence. The performance of the proposed algorithm is examined vis-a-vis the standard MRFO algorithm for optimising the value of 20 benchmark functions. While both algorithms compete well in finding the best fitness values, the proposed approach shows better convergence for 16 out of 20 functions.
To enhance the performance of the artificial bee colony optimization by integrating the quantum computing model into bee colony optimization, we present a quantum-inspired bee colony optimization algorithm. In our method, the bees are encoded with the qubits described on the Bloch sphere. The classical bee colony algorithm is used to compute the rotation axes and rotation angles. The Pauli matrices are used to construct the rotation matrices. The evolutionary search is achieved by rotating the qubit about the rotation axis to the target qubit on the Bloch sphere. By measuring with the Pauli matrices, the Bloch coordinates of qubit can be obtained, and the optimization solutions can be presented through the solution space transformation. The proposed method can simultaneously adjust two parameters of a qubit and automatically achieve the best match between two adjustment quantities, which may accelerate the optimization process. The experimental results show that the proposed method is obviously superior to the classical one for some benchmark functions.