The field of Quantum Machine Learning (QML) has emerged recently in the hopes
of finding new machine learning protocols or exponential speedups for classical
ones. Apart from problems with vanishing gradients and efficient encoding
methods, these speedups are hard to find because the sampling nature of quantum
computers promotes either simulating computations classically or running them
many times on quantum computers in order to use approximate expectation values
in gradient calculations. In this paper, we make a case for setting high
single-sample accuracy as a primary goal. We discuss the statistical theory
which enables highly accurate and precise sample inference, and propose a
method of reversed training towards this end. We show the effectiveness of this
training method by assessing several effective variational quantum circuits
(VQCs), trained in both the standard and reversed directions, on random binary
subsets of the MNIST and MNIST Fashion datasets, on which our method provides
an increase of $10-15\%$ in single-sample inference accuracy.

量子机器学习研究了快速化和精确推断方法，通过对 MNIST 和 MNIST Fashion 数据集进行实验，证明了反向训练对于提高单个样本推断准确性的有效性。

确定性进，确定性出：用于量子机器学习的 REVQC

Certainty In, Certainty Out: REVQCs for Quantum Machine Learning

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。