SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation
Abstract
Numerous quantum algorithms operate under the assumption that classical data has already been converted into quantum states, a process termed Quantum State Preparation (QSP). However, achieving precise QSP requires a circuit depth that scales exponentially with the number of qubits, making it a substantial obstacle in harnessing quantum advantage. Recent research suggests using a Parameterized Quantum Circuit (PQC) to approximate a target state, offering a more scalable solution with reduced circuit depth compared to precise QSP. Despite this, the need for iterative updates of circuit parameters results in a lengthy runtime, limiting its practical application. In this work, we demonstrate that it is possible to leverage a pre-trained neural network to directly generate the QSP circuit for arbitrary quantum state, thereby eliminating the significant overhead of online iterations. Our study makes a steady step towards a universal neural designer for approximate QSP.
Cite
@article{arxiv.2408.05435,
title = {SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation},
author = {Yilun Zhao and Bingmeng Wang and Wenle Jiang and Xiwei Pan and Bing Li and Yinhe Han and Ying Wang},
journal= {arXiv preprint arXiv:2408.05435},
year = {2024}
}