Quantum Neural Network (QNN) combines the Deep Learning (DL) principle with
the fundamental theory of quantum mechanics to achieve machine learning tasks
with quantum acceleration. Recently, QNN systems have been found to manifest
robustness issues similar to classical DL systems. There is an urgent need for
ways to test their correctness and security. However, QNN systems differ
significantly from traditional quantum software and classical DL systems,
posing critical challenges for QNN testing. These challenges include the
inapplicability of traditional quantum software testing methods, the dependence
of quantum test sample generation on perturbation operators, and the absence of
effective information in quantum neurons. In this paper, we propose QuanTest, a
quantum entanglement-guided adversarial testing framework to uncover potential
erroneous behaviors in QNN systems. We design a quantum entanglement adequacy
criterion to quantify the entanglement acquired by the input quantum states
from the QNN system, along with two similarity metrics to measure the proximity
of generated quantum adversarial examples to the original inputs. Subsequently,
QuanTest formulates the problem of generating test inputs that maximize the
quantum entanglement sufficiency and capture incorrect behaviors of the QNN
system as a joint optimization problem and solves it in a gradient-based manner
to generate quantum adversarial examples. Experimental results demonstrate that
QuanTest possesses the capability to capture erroneous behaviors in QNN systems
(generating 67.48%-96.05% more test samples than the random noise under the
same perturbation size constraints). The entanglement-guided approach proves
effective in adversarial testing, generating more adversarial examples (maximum
increase reached 21.32%).

Quantum Neural Network (QNN) systems face challenges in testing due to their differences from classical DL systems, therefore this paper proposes QuanTest, a quantum entanglement-guided adversarial testing framework to uncover potential erroneous behaviors in QNN systems.

QuanTest: 量子神经网络系统的纠缠引导测试

QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems

Spurious correlations in training data often lead to robustness issues since
models learn to use them as shortcuts. For example, when predicting whether an
object is a cow, a model might learn to rely on its green background, so it
would do poorly on a cow on a sandy background. A standard dataset for
measuring state-of-the-art on methods mitigating this problem is Waterbirds.
The best method (Group Distributionally Robust Optimization - GroupDRO)
currently achieves 89\% worst group accuracy and standard training from scratch
on raw images only gets 72\%. GroupDRO requires training a model in an
end-to-end manner with subgroup labels. In this paper, we show that we can
achieve up to 90\% accuracy without using any sub-group information in the
training set by simply using embeddings from a large pre-trained vision model
extractor and training a linear classifier on top of it. With experiments on a
wide range of pre-trained models and pre-training datasets, we show that the
capacity of the pre-training model and the size of the pre-training dataset
matters. Our experiments reveal that high capacity vision transformers perform
better compared to high capacity convolutional neural networks, and larger
pre-training dataset leads to better worst-group accuracy on the spurious
correlation dataset.

本文研究了深度神经网络在处理非真实相关数据时的稳健性问题，提出了一种不需要子分组信息训练、只需要将预训练模型的嵌入向量作为特征的线性分类器，实现了 90% 的准确率。实验表明，预训练模型的容量和数据集大小是影响效果的因素。

只需要一个好的嵌入提取器就能解决虚假相关性

You Only Need a Good Embeddings Extractor to Fix Spurious Correlations

NLP models are shown to suffer from robustness issues, i.e., a model's
prediction can be easily changed under small perturbations to the input. In
this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model
that, given an input text, generates adversarial texts through controllable
attributes that are known to be invariant to task labels. For example, in order
to attack a model for sentiment classification over product reviews, we can use
the product categories as the controllable attribute which would not change the
sentiment of the reviews. Experiments on real-world NLP datasets demonstrate
that our method can generate more diverse and fluent adversarial texts,
compared to many existing adversarial text generation approaches. We further
use our generated adversarial examples to improve models through adversarial
training, and we demonstrate that our generated attacks are more robust against
model re-training and different model architectures.

本文提出一种名为 CAT-Gen 的 NLP 模型，通过可控属性控制生成对抗文本，并将生成的对抗例子用于提高模型的鲁棒性。实验表明，与已有的对抗文本生成方法相比，CAT-Gen 能够生成更多样化、更流畅的对抗文本，并且生成的攻击能够更好地抵御模型重新训练和不同的模型结构。