核心概念
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.
要約
The paper focuses on developing efficient quantum circuits for implementing various machine learning activation functions, with a focus on minimizing the T-depth to enhance the practicality and application of quantum machine learning.
Key highlights:
- Proposed quantum circuits for ReLU and Leaky ReLU with constant T-depths of 4 and 8 respectively, without using ancillary qubits.
- Demonstrated that the circuit depth and size for the ReLU implementation are O(log n) and O(n) respectively, and provided lower bounds.
- Extended the ReLU circuit to work on a 2D grid architecture, maintaining the constant T-depth while achieving O(√n) depth and O(n) size.
- Utilized quantum lookup tables (QLUT) to implement other activation functions like Sigmoid, SoftMax, Tanh, Swish, ELU, and GELU, enabling trade-offs between number of qubits, implementation accuracy, and T-depth.
- Provided detailed analysis and open-sourced the Qiskit implementation of the quantum circuits.
統計
The paper does not contain any explicit numerical data or metrics. The key results are the T-depth and circuit complexity analysis for the proposed quantum circuits.
引用
"We specifically focus on minimizing the T-depth of the circuits, considering the high cost associated with fault-tolerant implementations of the T gate and the limitation imposed by the coherence time of the quantum device."
"We have specifically designed quantum circuits to implement them, significantly reducing the T-depth."
"QLUT allows us to reduce the T-depth of the circuit by increasing the ancilla count."