DeepQMLP：一种用于分类的可扩展量子-经典混合DeepNeural网络结构
DeepQMLP: A Scalable Quantum-Classical Hybrid DeepNeural Network Architecture for Classification
摘要：
量子机器学习（QML）有望在传统机器学习（ML）任务（例如，分类/回归）。寻找理想的QML模型是一个活跃的研究领域。这包括识别有效的经典到量子数据编码方案，构建具有最佳表现力和纠缠能力的参数量子线路（PQC），以及有效的输出解码方案以最小化所需的测量次数，仅举几例。然而，大多数的经验/数值研究缺乏一个明确的路径可扩展性。在模拟环境中观察到的任何潜在益处在实际应用中可能由于噪声量子硬件的限制而减少（例如，在去相干、门误差和串扰下）。我们提出了一种受经典深度神经网络架构启发的可扩展量子经典混合深度神经网络（DeepQMLP）架构。在DeepQMLP中，堆叠的浅量子神经网络（QNN）模型模拟了经典前馈多层感知器网络的隐藏层。每个QNN层为下一层生成输入数据的新的和潜在的丰富表示。这种新的表示可以通过电路的参数来调整。浅QNN模型经历较少的退相干、门误差等，这使得它们（和网络）对量子噪声更具弹性。我们对各种分类问题进行了数值研究，以显示DeepQMLP的可训练性。我们还表明，DeepQMLP在看不见的数据上表现得相当好，并且比使用深量子线路的QNN模型对噪声具有更大的弹性。DeepQMLP在噪声下的推理过程中比QMLP提供了高达25.3%的低损失和7.92%的高准确性。
英文摘要：
Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks (e.g., classification/regression). The search for ideal QML models is an active research field. This includes identification of efficient classical-to-quantum data encoding scheme, construction of parametric quantum circuits (PQC) with optimal expressivity and entanglement capability, and efficient output decoding scheme to minimize the required number of measurements, to name a few. However, most of the empirical/numerical studies lack a clear path towards scalability. Any potential benefit observed in a simulated environment may diminish in practical applications due to the limitations of noisy quantum hardware (e.g., under decoherence, gate-errors, and crosstalk). We present a scalable quantum-classical hybrid deep neural network (DeepQMLP) architecture inspired by classical deep neural network architectures. In DeepQMLP, stacked shallow Quantum Neural Network (QNN) models mimic the hidden layers of a classical feed-forward multi-layer perceptron network. Each QNN layer produces a new and potentially rich representation of the input data for the next layer. This new representation can be tuned by the parameters of the circuit. Shallow QNN models experience less decoherence, gate errors, etc. which make them (and the network) more resilient to quantum noise. We present numerical studies on a variety of classification problems to show the trainability of DeepQMLP. We also show that DeepQMLP performs reasonably well on unseen data and exhibits greater resilience to noise over QNN models that use a deep quantum circuit. DeepQMLP provided up to 25.3% lower loss and 7.92% higher accuracy during inference under noise than QMLP.
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