  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]
