  Parameterized Quantum Circuits (PQC) are promising towards quantum advantage
on near-term quantum hardware. However, due to the large quantum noises
(errors), the performance of PQC models has a severe degradation on real
quantum devices. Take Quantum Neural Network (QNN) as an example, the accuracy
gap between noise-free simulation and noisy results on IBMQ-Yorktown for
MNIST-4 classification is over 60%. Existing noise mitigation methods are
general ones without leveraging unique characteristics of PQC; on the other
hand, existing PQC work does not consider noise effect. To this end, we present
QuantumNAT, a PQC-specific framework to perform noise-aware optimizations in
both training and inference stages to improve robustness. We experimentally
observe that the effect of quantum noise to PQC measurement outcome is a linear
map from noise-free outcome with a scaling and a shift factor. Motivated by
that, we propose post-measurement normalization to mitigate the feature
distribution differences between noise-free and noisy scenarios. Furthermore,
to improve the robustness against noise, we propose noise injection to the
training process by inserting quantum error gates to PQC according to realistic
noise models of quantum hardware. Finally, post-measurement quantization is
introduced to quantize the measurement outcomes to discrete values, achieving
the denoising effect. Extensive experiments on 8 classification tasks using 6
quantum devices demonstrate that QuantumNAT improves accuracy by up to 43%, and
achieves over 94% 2-class, 80% 4-class, and 34% 10-class classification
accuracy measured on real quantum computers. The code for construction and
noise-aware training of PQC is available in the TorchQuantum library.
