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%.

翻译：量子计算凭借其通过叠加和纠缠在高维空间实现极强并行计算的能力，为机器学习提供了极具前景的方法。然而，现有量子学习算法（如变分量子线路VQC）在处理更复杂数据集（特别是非线性和线性不可分数据）时面临挑战。此外，在将学习模型部署至实际量子设备时，会出现严重的准确率下降问题。为克服这些局限，本文提出一种新型时空设计ST-VQC，在量子学习中集成非线性特征并提高学习模型对噪声的鲁棒性。具体而言，ST-VQC通过新型分块编码量子子电路与逐层计算量子子电路协同提取空间特征，实现时间维度的深度学习；同时设计无SWAP物理电路提升鲁棒性。这些设计引入多个超参数。通过系统分析各设计组件的设计空间，提出自动化优化框架生成ST-VQC量子电路。我们在两台IBM量子处理器（ibm_cairo 27量子比特和ibmq_lima 7量子比特）上评估了ST-VQC的有效性。在标准二分类数据集上的评估结果表明，ST-VQC在实际量子计算机上相比现有VQC可实现超过30%的准确率提升。此外，在非线性合成数据集上，ST-VQC相较线性分类器提升27.9%的性能，而采用经典计算的线性分类器相比现有VQC仅提升15.58%。