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

Distributed training across several quantum computers could significantly
improve the training time and if we could share the learned model, not the
data, it could potentially improve the data privacy as the training would
happen where the data is located. However, to the best of our knowledge, no
work has been done in quantum machine learning (QML) in federation setting yet.
In this work, we present the federated training on hybrid quantum-classical
machine learning models although our framework could be generalized to pure
quantum machine learning model. Specifically, we consider the quantum neural
network (QNN) coupled with classical pre-trained convolutional model. Our
distributed federated learning scheme demonstrated almost the same level of
trained model accuracies and yet significantly faster distributed training. It
demonstrates a promising future research direction for scaling and privacy
aspects.

本文主要介绍了基于量子可逆神经网络（QNN）和经典卷积神经网络 (CNN) 的混合模型，基于联邦学习的分布式训练可以极大地提高训练效率和数据隐私保护，并展示了分布式联邦学习方案对于扩展和隐私方面的有前景的未来研究方向。

联邦量子机器学习

Federated Quantum Machine Learning

Network quantization has rapidly become one of the most widely used methods
to compress and accelerate deep neural networks. Recent efforts propose to
quantize weights and activations from different layers with different precision
to improve the overall performance. However, it is challenging to find the
optimal bitwidth (i.e., precision) for weights and activations of each layer
efficiently. Meanwhile, it is yet unclear how to perform convolution for
weights and activations of different precision efficiently on generic hardware
platforms. To resolve these two issues, in this paper, we first propose an
Efficient Bitwidth Search (EBS) algorithm, which reuses the meta weights for
different quantization bitwidth and thus the strength for each candidate
precision can be optimized directly w.r.t the objective without superfluous
copies, reducing both the memory and computational cost significantly. Second,
we propose a binary decomposition algorithm that converts weights and
activations of different precision into binary matrices to make the mixed
precision convolution efficient and practical. Experiment results on CIFAR10
and ImageNet datasets demonstrate our mixed precision QNN outperforms the
handcrafted uniform bitwidth counterparts and other mixed precision techniques.

本文提出了一种用于网络量化的 Efficient Bitwidth Search（EBS）算法和一种二进制分解算法，以实现不同精度的权重和激活的混合精度卷积，并证明了该方法在 CIFAR10 和 ImageNet 数据集上的表现优于已有方法。