Learning Quantum Phase Estimation by Variational Quantum Circuits

TL;DR

This paper introduces a variational quantum circuit approach to approximate quantum phase estimation, reducing circuit depth and noise, thereby improving performance on noisy hardware and enhancing quantum algorithm efficiency.

Contribution

The paper presents a novel variational quantum circuit method to approximate QPE, significantly reducing circuit depth and noise impact compared to traditional methods.

Findings

VQC outperforms standard QPE on real hardware

Reduced circuit noise with VQC in noisy simulations

VQC integrated into quantum compilers improves algorithm performance

Abstract

Quantum Phase Estimation (QPE) stands as a pivotal quantum computing subroutine that necessitates an inverse Quantum Fourier Transform (QFT). However, it is imperative to recognize that enhancing the precision of the estimation inevitably results in a significantly deeper circuit. We developed a variational quantum circuit (VQC) approximation to reduce the depth of the QPE circuit, yielding enhanced performance in noisy simulations and real hardware. Our experiments demonstrated that the VQC outperformed both Noisy QPE and standard QPE on real hardware by reducing circuit noise. This VQC integration into quantum compilers as an intermediate step between input and transpiled circuits holds significant promise for quantum algorithms with deep circuits. Future research will explore its potential applicability across various quantum computing hardware architectures.