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arXiv 2405.04401quant-phhep-ph
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基于风格的量子生成对抗网络在超导和离子阱处理器上的跨平台硬件基准测试

Cross-platform hardware benchmark of style-based quantum GANs for data augmentation on superconducting and trapped-ion processors

University of Basel(巴塞尔大学)

Julien Baglio

AI总结:

在超导(IBM)和离子阱(IonQ)量子处理器上,对基于风格的qGAN进行高能物理数据增强任务的基准测试,比较质量与运行时,发现IonQ质量略优但IBM速度更快。

摘要:

在含噪中等规模量子时代,需要跨硬件模态的量子机器学习工作负载的受控基准测试,以量化给定算法在原生提供商执行堆栈下的行为。本文针对高能物理数据增强任务,对基于风格的量子生成对抗网络(qGAN)进行了这样的基准测试。我们比较了两个商用门模型量子计算机:基于Heron芯片超导transmon量子比特的IBM ibm_torino硬件和基于离子阱量子比特的IonQ aria-1硬件。生成器架构和训练参数保持不变,并尽可能禁用内置缓解措施。我们报告了每个提供商原生堆栈下的质量和运行时指标。工作流程利用可用量子比特上的电路复制(IBM最多48个,IonQ最多24个),以减少目标样本集所需的提交作业数量。据我们所知,这是针对此数据增强任务的首次受控的基于风格的qGAN硬件间比较之一。我们观察到两个平台都成功完成了任务,aria-1上的边际Kullback-Leibler散度略低,而ibm_torino上的端到端运行时显著更短。这些结果是特定于应用的权衡基准,而非算法新颖性的声明。

英文摘要:

In the noisy intermediate-scale quantum era, controlled benchmarks of quantum machine-learning workloads across hardware modalities are needed to quantify how given algorithms behave under native provider execution stacks. This work presents such a benchmark for the style-based quantum generative adversarial network (qGAN) on a high-energy physics data-augmentation task. We compare two commercially available gate-model quantum computers: the IBM ibm_torino hardware, based on superconducting transmon qubits from the Heron chip and the IonQ aria-1 hardware, based on trapped-ion qubits. The generator architecture and trained parameters are kept fixed, and built-in mitigation is disabled when possible. We report quality and runtime metrics under each provider's native stack. The workflow uses circuit replication across available qubits, up to 48 on IBM and 24 on IonQ, to reduce the number of submitted jobs required for the target sample set. To our knowledge, this is one of the first controlled style-based qGAN hardware-to-hardware comparisons for this data-augmentation task. We observe that both platforms complete the task successfully, with marginal Kullback-Leibler divergences somewhat lower on aria-1, while end-to-end runtime is significantly shorter on ibm_torino. These results are an application-specific tradeoff benchmark, not a claim of algorithmic novelty.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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