Quantum machine learning, focusing on quantum neural networks (QNNs), remains a vastly uncharted field of study. Current QNN models primarily employ variational circuits on an ansatz or a quantum feature map, often requiring multiple entanglement layers. This methodology not only increases the computational cost of the circuit beyond what is practical on near-term quantum devices but also misleadingly labels these models as neural networks, given their divergence from the structure of a typical feed-forward neural network (FFNN). Moreover, the circuit depth and qubit needs of these models scale poorly with the number of data features, resulting in an efficiency challenge for real-world machine-learning tasks. We introduce a bona fide QNN model, which seamlessly aligns with the versatility of a traditional FFNN in terms of its adaptable intermediate layers and nodes, absent from intermediate measurements such that our entire model is coherent. This model stands out with its reduced circuit depth and number of requisite C-NOT gates to outperform prevailing QNN models. Furthermore, the qubit count in our model remains unaffected by the data's feature quantity. We test our proposed model on various benchmarking datasets such as the diagnostic breast cancer (Wisconsin) and credit card fraud detection datasets. We compare the outcomes of our model with the existing QNN methods to showcase the advantageous efficacy of our approach, even with a reduced requirement on quantum resources. Our model paves the way for application of quantum neural networks to real relevant machine learning problems.

翻译：量子机器学习（尤其是量子神经网络）仍是一个尚未充分探索的研究领域。当前量子神经网络模型主要基于变分电路在假设或量子特征映射上的应用，常需多层纠缠结构。这种方法不仅导致电路计算成本超出近期量子设备的实际承载能力，且由于其与传统前馈神经网络（FFNN）结构的差异，容易造成对"神经网络"这一术语的误用。此外，这些模型所需的电路深度和量子比特数量随数据特征维度扩展性较差，在真实机器学习任务中存在效率挑战。我们提出一种真正的量子神经网络模型，该模型在可调整的中间层和节点设计上与传统FFNN完美对齐，无需中间测量即可实现整个模型的相干性。该模型通过更浅的电路深度和更少的C-NOT门数量，在性能上超越现有量子神经网络模型。值得注意的是，本模型的量子比特数量不受数据特征维度影响。我们在乳腺肿瘤诊断（威斯康星数据集）和信用卡欺诈检测等多个基准数据集上测试该模型，通过与现有量子神经网络方法的对比，展示了本方法在降低量子资源消耗的同时仍具优越性能。本模型为量子神经网络在真实机器学习问题中的应用开辟了新路径。