SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation
<|reference_start|>SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation: 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.<|reference_end|>
@article{zhao2024superencoder:,
    title={SuperEncoder: Towards Universal Neural Approximate Quantum State
  Preparation},
    author={Yilun Zhao, Bingmeng Wang, Wenle Jiang, Xiwei Pan, Bing Li, Yinhe Han,
  Ying Wang},
    journal={arXiv preprint arXiv:2408.05435},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05435},
    primaryClass={quant-ph cs.LG}
}