Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis

• URL: http://arxiv.org/abs/2504.06413v1
• Date: Tue, 08 Apr 2025 20:14:35 GMT
• Title: Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis
• Authors: Michael Kölle, Tom Bintener, Maximilian Zorn, Gerhard Stenzel, Leo Sünkel, Thomas Gabor, Claudia Linnhoff-Popien,
• Abstract summary: Genetic algorithms (GAs) provide a promising approach for efficient quantum circuit synthesis.<n>This work examines the impact of various mutation strategies within a GA framework for quantum circuit synthesis.
• Score: 6.122499977051124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum computing leverages the unique properties of qubits and quantum parallelism to solve problems intractable for classical systems, offering unparalleled computational potential. However, the optimization of quantum circuits remains critical, especially for noisy intermediate-scale quantum (NISQ) devices with limited qubits and high error rates. Genetic algorithms (GAs) provide a promising approach for efficient quantum circuit synthesis by automating optimization tasks. This work examines the impact of various mutation strategies within a GA framework for quantum circuit synthesis. By analyzing how different mutations transform circuits, it identifies strategies that enhance efficiency and performance. Experiments utilized a fitness function emphasizing fidelity, while accounting for circuit depth and T operations, to optimize circuits with four to six qubits. Comprehensive hyperparameter testing revealed that combining delete and swap strategies outperformed other approaches, demonstrating their effectiveness in developing robust GA-based quantum circuit optimizers.

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