SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation

• URL: http://arxiv.org/abs/2408.05435v1
• Date: Sat, 10 Aug 2024 04:39:05 GMT
• Title: SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation
• Authors: Yilun Zhao, Bingmeng Wang, Wenle Jiang, Xiwei Pan, Bing Li, Yinhe Han, Ying Wang,
• Abstract summary: We show that it is possible to leverage a pre-trained neural network to directly generate the QSP circuit for arbitrary quantum state. Our study makes a steady step towards a universal neural designer for approximate QSP.
• Score: 12.591173729459427
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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