Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU
<|reference_start|>Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU: 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.<|reference_end|>
@article{zi2024efficient,
    title={Efficient Quantum Circuits for Machine Learning Activation Functions
  including Constant T-depth ReLU},
    author={Wei Zi, Siyi Wang, Hyunji Kim, Xiaoming Sun, Anupam Chattopadhyay,
  Patrick Rebentrost},
    journal={arXiv preprint arXiv:2404.06059},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06059},
    primaryClass={quant-ph cs.AI}
}