Quantum classifiers are trainable quantum circuits used as machine learning
models. The first part of the circuit implements a quantum feature map that
encodes classical inputs into quantum states, embedding the data in a
high-dimensional Hilbert space; the second part of the circuit executes a
quantum measurement interpreted as the output of the model. Usually, the
measurement is trained to distinguish quantum-embedded data. We propose to
instead train the first part of the circuit -- the embedding -- with the
objective of maximally separating data classes in Hilbert space, a strategy we
call quantum metric learning. As a result, the measurement minimizing a linear
classification loss is already known and depends on the metric used: for
embeddings separating data using the l1 or trace distance, this is the Helstrom
measurement, while for the l2 or Hilbert-Schmidt distance, it is a simple
overlap measurement. This approach provides a powerful analytic framework for
quantum machine learning and eliminates a major component in current models,
freeing up more precious resources to best leverage the capabilities of
near-term quantum information processors.

该论文提出了一种称为量子度量学习的新策略，通过训练可训练的量子电路的第一部分来实现数据类最大分离的嵌入，从而消除了当前模型中的一个主要部分。

机器学习的量子嵌入

Quantum embeddings for machine learning

Adaptive feedback schemes are promising for quantum-enhanced measurements yet
are complicated to design. Machine learning can autonomously generate
algorithms in a classical setting. Here we adapt machine learning for quantum
information and use our framework to generate autonomous adaptive feedback
schemes for quantum measurement. In particular our approach replaces guesswork
in quantum measurement by a logical, fully-automatic, programmable routine. We
show that our method yields schemes that outperform the best known adaptive
scheme for interferometric phase estimation.

采用机器学习自主生成自适应反馈计划的方法适用于量子信息，有效取代了猜测性的量子测量方法，实现了在干涉测量中最佳的自适应方案。