Using Evolutionary Algorithms for Quantum Circuit Optimization under Noise

Using Evolutionary Algorithms for Quantum Circuit Optimization under Noise

Abstract:

Noise poses a prevalent challenge in the NISQ era of quantum computing. Its strong impact on the hardware of a quantum computer distorts the results of quantum circuits, especially with an increasing number of qubits and circuit depths. However, different circuit architectures that produce similar states can be exposed varying degrees of noise. This work presents an evolutionary algorithm aimed at finding an equivalent and less noisy circuit for a given quantum circuit. The algorithm’s fitness function evaluates the circuits based on their fidelity under noise compared to the noise-free state of the target circuit. With that, the evolutionary process is directed towards a noise-reduced solution. The results of the experiments show that the algorithm generally outperformed the randomly generated baseline and, in some cases, was able to find an optimized circuit compared to the target circuit. This demonstrates the potential of evolutionary algorithms for noise reduction. However, the scalability of the proposed evolutionary algorithm is severely limited.

Author:

Maria Trainer

Advisors:

Leo Sünkel, Maximilian Zorn, Thomas Gabor, Claudia Linnhoff-Popien

Student Thesis | Published May 2025 | Copyright © QAR-Lab
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