首个在量子计算机上实现连续相位值的非二进制神经元模型
Non-binary artificial neuron with phase variation implemented on a quantum computer
- 用复数相位表示连续值，突破传统二进制量子神经元限制
- 模拟验证可支持梯度下降的混合训练方案，具备可学习性
- 为近中期量子设备高效运行神经网络提供新路径
早期人工量子神经元模型沿用经典模型路径，仅处理离散值。本文提出一种新算法，将二进制模型推广至利用复数相位的连续值表示。我们设计、测试并实现了可在量子计算机上运行的连续值神经元模型。通过仿真证明，该模型可在结合梯度下降的混合训练框架中有效工作。本研究是推动人工神经网络在近中期量子设备上高效实现的重要一步。
原文摘要 · Abstract (English)
The first artificial quantum neuron models followed a similar path to classic models, as they work only with discrete values. Here we introduce an algorithm that generalizes the binary model manipulating the phase of complex numbers. We propose, test, and implement a neuron model that works with continuous values in a quantum computer. Through simulations, we demonstrate that our model may work in a hybrid training scheme utilizing gradient descent as a learning algorithm. This work represents another step in the direction of evaluation of the use of artificial neural networks efficiently implemented on near-term quantum devices.
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