Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929307314552832 |
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| author | Zi, Wei Wang, Siyi Kim, Hyunji Sun, Xiaoming Chattopadhyay, Anupam Rebentrost, Patrick |
| author_facet | Zi, Wei Wang, Siyi Kim, Hyunji Sun, Xiaoming Chattopadhyay, Anupam Rebentrost, Patrick |
| contents | In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among the components in this domain, activation functions hold a fundamental and indispensable role. Our research focuses on the development of activation functions quantum circuits for integration into fault-tolerant quantum computing architectures, with an emphasis on minimizing $T$-depth. Specifically, we present novel implementations of ReLU and leaky ReLU activation functions, achieving constant $T$-depths of 4 and 8, respectively. Leveraging quantum lookup tables, we extend our exploration to other activation functions such as the sigmoid. This approach enables us to customize precision and $T$-depth by adjusting the number of qubits, making our results more adaptable to various application scenarios. This study represents a significant advancement towards enhancing the practicality and application of quantum machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06059 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU Zi, Wei Wang, Siyi Kim, Hyunji Sun, Xiaoming Chattopadhyay, Anupam Rebentrost, Patrick Quantum Physics Artificial Intelligence In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among the components in this domain, activation functions hold a fundamental and indispensable role. Our research focuses on the development of activation functions quantum circuits for integration into fault-tolerant quantum computing architectures, with an emphasis on minimizing $T$-depth. Specifically, we present novel implementations of ReLU and leaky ReLU activation functions, achieving constant $T$-depths of 4 and 8, respectively. Leveraging quantum lookup tables, we extend our exploration to other activation functions such as the sigmoid. This approach enables us to customize precision and $T$-depth by adjusting the number of qubits, making our results more adaptable to various application scenarios. This study represents a significant advancement towards enhancing the practicality and application of quantum machine learning. |
| title | Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU |
| topic | Quantum Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2404.06059 |