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.
@article{arxiv.2404.06059,
title = {Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU},
author = {Wei Zi and Siyi Wang and Hyunji Kim and Xiaoming Sun and Anupam Chattopadhyay and Patrick Rebentrost},
journal= {arXiv preprint arXiv:2404.06059},
year = {2024}
}