Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866917752845893632 |
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| author | Kiwit, Florian J. Marso, Marwa Ross, Philipp Riofrío, Carlos A. Klepsch, Johannes Luckow, Andre |
| author_facet | Kiwit, Florian J. Marso, Marwa Ross, Philipp Riofrío, Carlos A. Klepsch, Johannes Luckow, Andre |
| contents | Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e.g., regarding model ansatzes, data sets, training techniques, and hyper-parameters selection. The QUantum computing Application benchmaRK (QUARK) framework simplifies and standardizes benchmarking studies for quantum computing applications. Here, we propose several extensions of QUARK to include the ability to evaluate the training and deployment of quantum generative models. We describe the updated software architecture and illustrate its flexibility through several example applications: (1) We trained different quantum generative models using several circuit ansatzes, data sets, and data transformations. (2) We evaluated our models on GPU and real quantum hardware. (3) We assessed the generalization capabilities of our generative models using a broad set of metrics that capture, e.g., the novelty and validity of the generated data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_04082 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK Kiwit, Florian J. Marso, Marwa Ross, Philipp Riofrío, Carlos A. Klepsch, Johannes Luckow, Andre Quantum Physics Distributed, Parallel, and Cluster Computing Machine Learning 81-04 C.4 Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e.g., regarding model ansatzes, data sets, training techniques, and hyper-parameters selection. The QUantum computing Application benchmaRK (QUARK) framework simplifies and standardizes benchmarking studies for quantum computing applications. Here, we propose several extensions of QUARK to include the ability to evaluate the training and deployment of quantum generative models. We describe the updated software architecture and illustrate its flexibility through several example applications: (1) We trained different quantum generative models using several circuit ansatzes, data sets, and data transformations. (2) We evaluated our models on GPU and real quantum hardware. (3) We assessed the generalization capabilities of our generative models using a broad set of metrics that capture, e.g., the novelty and validity of the generated data. |
| title | Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK |
| topic | Quantum Physics Distributed, Parallel, and Cluster Computing Machine Learning 81-04 C.4 |
| url | https://arxiv.org/abs/2308.04082 |