Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914721769193472 |
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| author | Stein, Jonas Rohe, Tobias Nappi, Francesco Hager, Julian Bucher, David Zorn, Maximilian Kölle, Michael Linnhoff-Popien, Claudia |
| author_facet | Stein, Jonas Rohe, Tobias Nappi, Francesco Hager, Julian Bucher, David Zorn, Maximilian Kölle, Michael Linnhoff-Popien, Claudia |
| contents | Variational Quantum Algorithms are one of the most promising candidates to yield the first industrially relevant quantum advantage. Being capable of arbitrary function approximation, they are often referred to as Quantum Neural Networks (QNNs) when being used in analog settings as classical Artificial Neural Networks (ANNs). Similar to the early stages of classical machine learning, known schemes for efficient architectures of these networks are scarce. Exploring beyond existing design patterns, we propose a reduced-width circuit ansatz design, which is motivated by recent results gained in the analysis of dropout regularization in QNNs. More precisely, this exploits the insight, that the gates of overparameterized QNNs can be pruned substantially until their expressibility decreases. The results of our case study show, that the proposed design pattern can significantly reduce training time while maintaining the same result quality as the standard "full-width" design in the presence of noise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_05047 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern Stein, Jonas Rohe, Tobias Nappi, Francesco Hager, Julian Bucher, David Zorn, Maximilian Kölle, Michael Linnhoff-Popien, Claudia Quantum Physics Emerging Technologies Variational Quantum Algorithms are one of the most promising candidates to yield the first industrially relevant quantum advantage. Being capable of arbitrary function approximation, they are often referred to as Quantum Neural Networks (QNNs) when being used in analog settings as classical Artificial Neural Networks (ANNs). Similar to the early stages of classical machine learning, known schemes for efficient architectures of these networks are scarce. Exploring beyond existing design patterns, we propose a reduced-width circuit ansatz design, which is motivated by recent results gained in the analysis of dropout regularization in QNNs. More precisely, this exploits the insight, that the gates of overparameterized QNNs can be pruned substantially until their expressibility decreases. The results of our case study show, that the proposed design pattern can significantly reduce training time while maintaining the same result quality as the standard "full-width" design in the presence of noise. |
| title | Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern |
| topic | Quantum Physics Emerging Technologies |
| url | https://arxiv.org/abs/2306.05047 |