论文标题
QMLP：使用参数化的两个Quibent Gates的易耐受性非线性量子MLP体系结构
QMLP: An Error-Tolerant Nonlinear Quantum MLP Architecture using Parameterized Two-Qubit Gates
论文作者
论文摘要
尽管潜在的量子至高无上，但最先进的量子神经网络（QNN）的推理准确性低。首先，高误差率为0.001至0.01的当前噪声中间尺度量子（NISQ）设备显着降低了QNN的精度。其次，尽管最近提出的重新上传单元（RUUS）将一些非线性引入了QNN电路，但其背后的理论尚未完全理解。此外，以前反复上传原始数据的RUU只能提供边际准确性的提高。第三，当前的QNN电路ANSATZ使用固定的两倍大门来强制执行最大的纠缠能力，从而使特定于任务的纠缠不可能调整，从而导致整体性能差。在本文中，我们提出了一个量子多层感知器（QMLP）体系结构，具有容忍度的输入嵌入，丰富的非线性和增强的变异电路ANSATZ，具有参数化的两倍符号纠缠的门。与先前的艺术相比，QMLP将10级MNIST数据集的推理精度提高了10％，而量子门更少2倍，而参数降低了3倍。我们的源代码可用，可以在[1]中找到
Despite potential quantum supremacy, state-of-the-art quantum neural networks (QNNs) suffer from low inference accuracy. First, the current Noisy Intermediate-Scale Quantum (NISQ) devices with high error rates of 0.001 to 0.01 significantly degrade the accuracy of a QNN. Second, although recently proposed Re-Uploading Units (RUUs) introduce some non-linearity into the QNN circuits, the theory behind it is not fully understood. Furthermore, previous RUUs that repeatedly upload original data can only provide marginal accuracy improvements. Third, current QNN circuit ansatz uses fixed two-qubit gates to enforce maximum entanglement capability, making task-specific entanglement tuning impossible, resulting in poor overall performance. In this paper, we propose a Quantum Multilayer Perceptron (QMLP) architecture featured by error-tolerant input embedding, rich nonlinearity, and enhanced variational circuit ansatz with parameterized two-qubit entangling gates. Compared to prior arts, QMLP increases the inference accuracy on the 10-class MNIST dataset by 10% with 2 times fewer quantum gates and 3 times reduced parameters. Our source code is available and can be found in [1]
