Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU
This work addresses a bottleneck in quantum machine learning by making activation functions more efficient for fault-tolerant architectures, though it is incremental in improving existing methods.
The paper tackled the problem of implementing activation functions for quantum machine learning with minimal T-depth, achieving constant T-depths of 4 for ReLU and 8 for leaky ReLU using quantum lookup tables.
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.