Shape-Guided Diffusion with Inside-Outside Attention

Park, Dong Huk; Luo, Grace; Toste, Clayton; Azadi, Samaneh; Liu, Xihui; Karalashvili, Maka; Rohrbach, Anna; Darrell, Trevor

doi:10.48550/arxiv.2212.00210

2022

DOI: 10.48550/arxiv.2212.00210

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Shape-Guided Diffusion with Inside-Outside Attention

Dong Huk Park¹,

Grace Luo²,

Clayton Toste³

et al.

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A Characterization of Quantum Generative Models

Riofrio,

Mitevski,

Jones

et al. 2024

ACM Trans. Quantum Comput.

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Quantum generative modeling is a growing area of interest for industry-relevant applications. This work systematically compares a broad range of techniques to guide quantum computing practitioners when deciding which models and methods to use in their applications. We compare fundamentally different architectural ansatzes of parametric quantum circuits: 1. A continuous architecture, which produces continuous-valued data samples, and 2. a discrete architecture, which samples on a discrete grid. We also compare the performance of different data transformations: the min-max and the probability integral transforms. We use two popular training methods: 1. quantum circuit Born machines (QCBM), and 2. quantum generative adversarial networks (QGAN). We study their performance and trade-offs as the number of model parameters increases, with a baseline comparison of similarly trained classical neural networks. The study is performed on six low-dimensional synthetic and two real financial data sets. Our two key findings are that: 1. For all data sets, our quantum models require similar or fewer parameters than their classical counterparts. In the extreme case, the quantum models require two orders of magnitude less parameters. 2. We empirically find that a variant of the discrete architecture, which learns the copula of the probability distribution, outperforms all other methods.

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A Characterization of Quantum Generative Models

Riofrio,

Mitevski,

Jones

et al. 2024

ACM Trans. Quantum Comput.

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show abstract

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Shape-Guided Diffusion with Inside-Outside Attention

Cited by 1 publication

References 0 publications

A Characterization of Quantum Generative Models

A Characterization of Quantum Generative Models

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