The potential impact of quantum machine learning algorithms on industrial
applications remains an exciting open question. Conventional methods for
encoding classical data into quantum computers are not only too costly for a
potential quantum advantage in the algorithms but also severely limit the scale
of feasible experiments on current hardware. Therefore, recent works, despite
claiming the near-term suitability of their algorithms, do not provide
experimental benchmarking on standard machine learning datasets. We attempt to
solve the data encoding problem by improving a recently proposed variational
algorithm [1] that approximately prepares the encoded data, using
asymptotically shallow circuits that fit the native gate set and topology of
currently available quantum computers. We apply the improved algorithm to
encode the Fashion-MNIST dataset [2], which can be directly used in future
empirical studies of quantum machine learning algorithms. We deploy simple
quantum variational classifiers trained on the encoded dataset on a current
quantum computer ibmq-kolkata [3] and achieve moderate accuracies, providing a
proof of concept for the near-term usability of our data encoding method.

量子机器学习算法对工业应用的潜在影响仍然是一个激动人心的待解问题，我们尝试通过改进一种近期提出的变分算法，用当前可用的量子计算机的本机门集合和拓扑结构逼近准备编码数据，将该算法应用于 Fashion-MNIST 数据集，并在当前量子计算机上部署简单的量子变分分类器对编码的数据集进行训练，实现了中等精度的结果，为我们的数据编码方法的近期可用性提供了概念验证。

在量子计算机上对时尚 - MNIST 数据集进行分类

Classification of the Fashion-MNIST Dataset on a Quantum Computer

Radio signal classification plays a pivotal role in identifying the
modulation scheme used in received radio signals, which is essential for
demodulation and proper interpretation of the transmitted information.
Researchers have underscored the high susceptibility of ML algorithms for radio
signal classification to adversarial attacks. Such vulnerability could result
in severe consequences, including misinterpretation of critical messages,
interception of classified information, or disruption of communication
channels. Recent advancements in quantum computing have revolutionized theories
and implementations of computation, bringing the unprecedented development of
Quantum Machine Learning (QML). It is shown that quantum variational
classifiers (QVCs) provide notably enhanced robustness against classical
adversarial attacks in image classification. However, no research has yet
explored whether QML can similarly mitigate adversarial threats in the context
of radio signal classification. This work applies QVCs to radio signal
classification and studies their robustness to various adversarial attacks. We
also propose the novel application of the approximate amplitude encoding (AAE)
technique to encode radio signal data efficiently. Our extensive simulation
results present that attacks generated on QVCs transfer well to CNN models,
indicating that these adversarial examples can fool neural networks that they
are not explicitly designed to attack. However, the converse is not true. QVCs
primarily resist the attacks generated on CNNs. Overall, with comprehensive
simulations, our results shed new light on the growing field of QML by bridging
knowledge gaps in QAML in radio signal classification and uncovering the
advantages of applying QML methods in practical applications.

通过广泛的模拟实验，我们的结果填补了在无线电信号分类中的 QAML 领域的知识空白，并揭示了在实际应用中应用 QML 方法的优点。