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.

我们提出了一种真正的量子神经网络模型，其与传统的呈适应性中间层和节点的前馈神经网络具有相同的灵活性，并且具有较低的电路深度和所需的 C-NOT 门数量，从而优于现有的量子神经网络模型。通过对基准数据集进行测试，我们证明了我们的方法的优越性能，并且使用更少的量子资源要求，为将量子神经网络应用于实际相关的机器学习问题铺平了道路。

相干前馈量子神经网络

Coherent Feed Forward Quantum Neural Network

Quantum supervised learning, utilizing variational circuits, stands out as a
promising technology for NISQ devices due to its efficiency in hardware
resource utilization during the creation of quantum feature maps and the
implementation of hardware-efficient ansatz with trainable parameters. Despite
these advantages, the training of quantum models encounters challenges, notably
the barren plateau phenomenon, leading to stagnation in learning during
optimization iterations. This study proposes an innovative approach: an
evolutionary-enhanced ansatz-free supervised learning model. In contrast to
parametrized circuits, our model employs circuits with variable topology that
evolves through an elitist method, mitigating the barren plateau issue.
Additionally, we introduce a novel concept, the superposition of multi-hot
encodings, facilitating the treatment of multi-classification problems. Our
framework successfully avoids barren plateaus, resulting in enhanced model
accuracy. Comparative analysis with variational quantum classifiers from the
technology's state-of-the-art reveal a substantial improvement in training
efficiency and precision. Furthermore, we conduct tests on a challenging
dataset class, traditionally problematic for conventional kernel machines,
demonstrating a potential alternative path for achieving quantum advantage in
supervised learning for NISQ era.

本研究提出了一种创新的方法，利用进化增强的无参数化电路进行监督学习模型，该模型采用可变拓扑的电路经由精英方法演化，从而减轻了贫瘠高原问题，并引入了多热编码的叠加概念，方便多分类问题的处理。通过与当前技术最先进的变分量子分类器进行比较分析，我们的框架在训练效率和精确度方面得到了显著的提升。此外，我们对传统上常常困扰传统核心机器的具有挑战性的数据集类进行了测试，展示了在 NISQ 时代实现量子优势的潜在替代路径。

进化增强的量子监督学习模型

Evolutionary-enhanced quantum supervised learning model

The current generation of quantum computing technologies call for quantum
algorithms that require a limited number of qubits and quantum gates, and which
are robust against errors. A suitable design approach are variational circuits
where the parameters of gates are learnt, an approach that is particularly
fruitful for applications in machine learning. In this paper, we propose a
low-depth variational quantum algorithm for supervised learning. The input
feature vectors are encoded into the amplitudes of a quantum system, and a
quantum circuit of parametrised single and two-qubit gates together with a
single-qubit measurement is used to classify the inputs. This circuit
architecture ensures that the number of learnable parameters is
poly-logarithmic in the input dimension. We propose a quantum-classical
training scheme where the analytical gradients of the model can be estimated by
running several slightly adapted versions of the variational circuit. We show
with simulations that the circuit-centric quantum classifier performs well on
standard classical benchmark datasets while requiring dramatically fewer
parameters than other methods. We also evaluate sensitivity of the
classification to state preparation and parameter noise, introduce a quantum
version of dropout regularisation and provide a graphical representation of
quantum gates as highly symmetric linear layers of a neural network.

本文提出了一种用于监督学习的低深度可变量量子算法，其使用参数化单个和双量子位门的量子电路以及单量子位测量来编码输入特征向量并进行分类，从而实现了可学习参数数量与输入维度的多项式对数关系。通过模拟，该电路为标准经典基准数据集提供了很好的分类性能，而且需要的参数数量极少。