  Quantum computing presents a promising approach for machine learning with its
capability for extremely parallel computation in high-dimension through
superposition and entanglement. Despite its potential, existing quantum
learning algorithms, such as Variational Quantum Circuits(VQCs), face
challenges in handling more complex datasets, particularly those that are not
linearly separable. What's more, it encounters the deployability issue, making
the learning models suffer a drastic accuracy drop after deploying them to the
actual quantum devices. To overcome these limitations, this paper proposes a
novel spatial-temporal design, namely ST-VQC, to integrate non-linearity in
quantum learning and improve the robustness of the learning model to noise.
Specifically, ST-VQC can extract spatial features via a novel block-based
encoding quantum sub-circuit coupled with a layer-wise computation quantum
sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free
physical circuit design is devised to improve robustness. These designs bring a
number of hyperparameters. After a systematic analysis of the design space for
each design component, an automated optimization framework is proposed to
generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on
two IBM quantum processors, ibm_cairo with 27 qubits and ibmq_lima with 7
qubits to assess its effectiveness. The results of the evaluation on the
standard dataset for binary classification show that ST-VQC can achieve over
30% accuracy improvement compared with existing VQCs on actual quantum
computers. Moreover, on a non-linear synthetic dataset, the ST-VQC outperforms
a linear classifier by 27.9%, while the linear classifier using classical
computing outperforms the existing VQC by 15.58%.
