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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2302.07409 |
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| _version_ | 1866909709517193216 |
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| author | Mohan, Preetham Tewari, Ambuj |
| author_facet | Mohan, Preetham Tewari, Ambuj |
| contents | Arunachalam and de Wolf (2018) showed that the sample complexity of quantum batch learning of boolean functions, in the realizable and agnostic settings, has the same form and order as the corresponding classical sample complexities. In this paper, we extend this, ostensibly surprising, message to batch multiclass learning, online boolean learning, and online multiclass learning. For our online learning results, we first consider an adaptive adversary variant of the classical model of Dawid and Tewari (2022). Then, we introduce the first (to the best of our knowledge) model of online learning with quantum examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_07409 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Quantum Learning Theory Beyond Batch Binary Classification Mohan, Preetham Tewari, Ambuj Machine Learning Computational Complexity Quantum Physics Arunachalam and de Wolf (2018) showed that the sample complexity of quantum batch learning of boolean functions, in the realizable and agnostic settings, has the same form and order as the corresponding classical sample complexities. In this paper, we extend this, ostensibly surprising, message to batch multiclass learning, online boolean learning, and online multiclass learning. For our online learning results, we first consider an adaptive adversary variant of the classical model of Dawid and Tewari (2022). Then, we introduce the first (to the best of our knowledge) model of online learning with quantum examples. |
| title | Quantum Learning Theory Beyond Batch Binary Classification |
| topic | Machine Learning Computational Complexity Quantum Physics |
| url | https://arxiv.org/abs/2302.07409 |