Quantum Computing and especially Quantum Machine Learning, in a short period
of time, has gained a lot of interest through research groups around the world.
This can be seen in the increasing number of proposed models for pattern
classification applying quantum principles to a certain degree. Despise the
increasing volume of models, there is a void in testing these models on real
datasets and not only on synthetic ones. The objective of this work is to
classify patterns with binary attributes using a quantum classifier. Specially,
we show results of a complete quantum classifier applied to image datasets. The
experiments show favorable output while dealing with balanced classification
problems as well as with imbalanced classes where the minority class is the
most relevant. This is promising in medical areas, where usually the important
class is also the minority class.

本文探讨使用量子分类器对二进制属性进行模式分类，特别是对图像数据集的分类表现良好，对医学领域来说是具有很大潜力的。

量子机器学习图像分类

Image Classification via Quantum Machine Learning

A single qubit provides sufficient computational capabilities to construct a
universal quantum classifier when assisted with a classical subroutine. This
fact may be surprising since a single qubit only offers a simple superposition
of two states and single-qubit gates only make a rotation in the Bloch sphere.
The key ingredient to circumvent these limitations is to allow for multiple
data re-uploading. A quantum circuit can then be organized as a series of data
re-uploading and single-qubit processing units. Furthermore, both data
re-uploading and measurements can accommodate multiple dimensions in the input
and several categories in the output, to conform to a universal quantum
classifier. The extension of this idea to several qubits enhances the
efficiency of the strategy as entanglement expands the superpositions carried
along with the classification. Extensive benchmarking on different examples of
the single- and multi-qubit quantum classifier validates its ability to
describe and classify complex data.

利用单量子比特与多重数据重传，以及多维输入和输出，构建了一个严格的通用量子分类器，并且可以进行针对单量子比特以及多量子比特的量子分类器的检验。

通用量子分类器的数据重新上传

Data re-uploading for a universal quantum classifier

Quantum machine learning carries the promise to revolutionize information and
communication technologies. While a number of quantum algorithms with potential
exponential speedups have been proposed already, it is quite difficult to
provide convincing evidence that quantum computers with quantum memories will
be in fact useful to solve real-world problems. Our work makes considerable
progress towards this goal.
We design quantum techniques for Dimensionality Reduction and for
Classification, and combine them to provide an efficient and high accuracy
quantum classifier that we test on the MNIST dataset. More precisely, we
propose a quantum version of Slow Feature Analysis (QSFA), a dimensionality
reduction technique that maps the dataset in a lower dimensional space where we
can apply a novel quantum classification procedure, the Quantum Frobenius
Distance (QFD). We simulate the quantum classifier (including errors) and show
that it can provide classification of the MNIST handwritten digit dataset, a
widely used dataset for benchmarking classification algorithms, with $98.5\%$
accuracy, similar to the classical case. The running time of the quantum
classifier is polylogarithmic in the dimension and number of data points. We
also provide evidence that the other parameters on which the running time
depends (condition number, Frobenius norm, error threshold, etc.) scale
favorably in practice, thus ascertaining the efficiency of our algorithm.

本文提出了一个高效和高准确度的量子分类器，它利用降维和分类的量子技术，被测试在 MNIST 数据集上，具有类似经典方法的 98.5％准确度，并且在数据点和维度上的运行时间是多对数的。