Learning quantum phase estimation by variational quantum circuits

Published in 2024 International Joint Conference on Neural Networks (IJCNN), 2024

Quantum Phase Estimation (QPE) traditionally depends on an inverse Quantum Fourier Transform, making circuit depth grow rapidly with desired precision. This paper proposes a variational quantum circuit (VQC) approximation that:

  • Reduces QPE depth while preserving estimation accuracy.
  • Outperforms noisy QPE simulations and standard QPE on real IBM hardware by mitigating gate errors.
  • Integrates as an intermediate compiler pass, promising depth savings for algorithms whose complexity is dominated by QPE.

The results show that adaptive, trainable circuits can improve near-term quantum algorithm performance and remain compatible with future, larger-scale devices.

Figure: VQC approximation of QPE with training results and cost convergence on IBM simulators.

Recommended citation: Liu, Chen-Yu; Chen, Kuan-Cheng; & Lin, Chu-Hsuan Abraham. (2024). "Learning quantum phase estimation by variational quantum circuits." Proceedings of the 2024 International Joint Conference on Neural Networks (IJCNN), 1–6.
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