用于量子架构搜索的元训练生成器

IF 5.8 2区 物理与天体物理 Q1 OPTICS
Zhimin He, Chuangtao Chen, Zhengjiang Li, Haozhen Situ, Fei Zhang, Shenggen Zheng, Lvzhou Li
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引用次数: 0

摘要

变分量子算法(Variational Quantum Algorithms,VQAs)在噪声中量子(Noisy Intermediate-Scale Quantum,NISQ)时代取得了巨大的成功,这得益于其对噪声的相对弹性以及相对于量子资源的高灵活性。量子架构搜索(QAS)旨在通过改进所采用的参数化量子电路(PQC)的结构来提高 VQAs 的性能。QAS 自动化程度高,减少了对专家经验的依赖,而且与人工设计的电路相比,QAS 需要的量子门更少,却能实现更高的性能,因此受到越来越多的关注。然而,现有的 QAS 算法在不利用任何先前经验的情况下,从头开始优化每个 VQA 的结构,这使得整个过程既低效又耗时。此外,确定量子门的数量(这些算法中的一个关键超参数)是一项具有挑战性且耗时的任务。为了减轻这些挑战,我们通过元训练生成器加速了 QAS 算法。所提出的算法通过利用元训练变异自动编码器(VAE),直接为新的 VQA 生成高性能电路。设计电路所需的量子门数量是根据从各种训练任务中学到的元知识自动确定的。此外,我们还开发了一种元预测器,用于过滤性能不佳的电路,从而加速算法。变量子编译和量子逼近优化算法(QAOA)的仿真结果表明,我们的方法比最先进的算法(即可微分量子架构搜索(DQAS))性能更优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A meta-trained generator for quantum architecture search

Variational Quantum Algorithms (VQAs) have made great success in the Noisy Intermediate-Scale Quantum (NISQ) era due to their relative resilience to noise and high flexibility relative to quantum resources. Quantum Architecture Search (QAS) aims to enhance the performance of VQAs by refining the structure of the adopted Parameterized Quantum Circuit (PQC). QAS is garnering increased attention owing to its automation, reduced reliance on expert experience, and its ability to achieve better performance while requiring fewer quantum gates than manually designed circuits. However, existing QAS algorithms optimize the structure from scratch for each VQA without using any prior experience, rendering the process inefficient and time-consuming. Moreover, determining the number of quantum gates, a crucial hyper-parameter in these algorithms is a challenging and time-consuming task. To mitigate these challenges, we accelerate the QAS algorithm via a meta-trained generator. The proposed algorithm directly generates high-performance circuits for a new VQA by utilizing a meta-trained Variational AutoEncoder (VAE). The number of quantum gates required in the designed circuit is automatically determined based on meta-knowledge learned from a variety of training tasks. Furthermore, we have developed a meta-predictor to filter out circuits with suboptimal performance, thereby accelerating the algorithm. Simulation results on variational quantum compiling and Quantum Approximation Optimization Algorithm (QAOA) demonstrate the superior performance of our method over a state-of-the-art algorithm, namely Differentiable Quantum Architecture Search (DQAS).

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来源期刊
EPJ Quantum Technology
EPJ Quantum Technology Physics and Astronomy-Atomic and Molecular Physics, and Optics
CiteScore
7.70
自引率
7.50%
发文量
28
审稿时长
71 days
期刊介绍: Driven by advances in technology and experimental capability, the last decade has seen the emergence of quantum technology: a new praxis for controlling the quantum world. It is now possible to engineer complex, multi-component systems that merge the once distinct fields of quantum optics and condensed matter physics. EPJ Quantum Technology covers theoretical and experimental advances in subjects including but not limited to the following: Quantum measurement, metrology and lithography Quantum complex systems, networks and cellular automata Quantum electromechanical systems Quantum optomechanical systems Quantum machines, engineering and nanorobotics Quantum control theory Quantum information, communication and computation Quantum thermodynamics Quantum metamaterials The effect of Casimir forces on micro- and nano-electromechanical systems Quantum biology Quantum sensing Hybrid quantum systems Quantum simulations.
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