
The authors present novel quantum circuit implementations for ReLU and Leaky ReLU activation functions, achieving constant T-depths of 4 and 8 respectively. They also utilize quantum lookup tables to implement other activation functions like Sigmoid, SoftMax, Tanh, Swish, ELU, and GELU, enabling customization of precision and T-depth.


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efficient-quantum-circuits-for-implementing-machine-learning-activation-functions-with-constant-t-depth


Efficient Quantum Circuits for Implementing Machine Learning Activation Functions with Constant T-depth
