Here we introduce an improved approach to Variational Quantum Attack
Algorithms (VQAA) on crytographic protocols. Our methods provide robust quantum
attacks to well-known cryptographic algorithms, more efficiently and with
remarkably fewer qubits than previous approaches. We implement simulations of
our attacks for symmetric-key protocols such as S-DES, S-AES and Blowfish. For
instance, we show how our attack allows a classical simulation of a small
8-qubit quantum computer to find the secret key of one 32-bit Blowfish instance
with 24 times fewer number of iterations than a brute-force attack. Our work
also shows improvements in attack success rates for lightweight ciphers such as
S-DES and S-AES. Further applications beyond symmetric-key cryptography are
also discussed, including asymmetric-key protocols and hash functions. In
addition, we also comment on potential future improvements of our methods. Our
results bring one step closer assessing the vulnerability of large-size
classical cryptographic protocols with Noisy Intermediate-Scale Quantum (NISQ)
devices, and set the stage for future research in quantum cybersecurity.

改进的变分量子攻击算法 (VQAA) 提供了针对众所周知的加密算法的强大的量子攻击方法，效率更高，所需量子位数明显较少。我们对对称密钥协议（如 S-DES，S-AES 和 Blowfish）的攻击进行了模拟，结果表明我们的攻击允许一个小型 8 量子比特量子计算机在 24 次迭代次数更少的情况下，通过经典模拟找到一个 32 比特 Blowfish 实例的秘密密钥。我们的工作还展示了轻量级密码（如 S-DES 和 S-AES）的攻击成功率的改进。此外，我们还讨论了超过对称密钥密码学的应用，包括非对称密钥协议和哈希函数。我们的结果使得使用嘈杂中间规模量子（NISQ）设备评估大型经典加密协议的易受攻击性更加接近，并为量子网络安全性的未来研究奠定了基础。

使用高级变分量子攻击破解密码协议

Hacking Cryptographic Protocols with Advanced Variational Quantum  Attacks

We propose a security verification framework for cryptographic protocols
using machine learning. In recent years, as cryptographic protocols have become
more complex, research on automatic verification techniques has been focused
on. The main technique is formal verification. However, the formal verification
has two problems: it requires a large amount of computational time and does not
guarantee decidability. We propose a method that allows security verification
with computational time on the order of linear with respect to the size of the
protocol using machine learning. In training machine learning models for
security verification of cryptographic protocols, a sufficient amount of data,
i.e., a set of protocol data with security labels, is difficult to collect from
academic papers and other sources. To overcome this issue, we propose a way to
create arbitrarily large datasets by automatically generating random protocols
and assigning security labels to them using formal verification tools.
Furthermore, to exploit structural features of protocols, we construct a neural
network that processes a protocol along its series and tree structures. We
evaluate the proposed method by applying it to verification of practical
cryptographic protocols.

本文提出了一种利用机器学习进行密码协议安全验证的框架，使用神经网络处理密码协议的结构特征，自动生成数据并利用形式化验证工具分配安全标签。经测试，该方法适用于实际密码协议的验证。

基于机器学习的密码协议安全验证框架

A Security Verification Framework of Cryptographic Protocols Using  Machine Learning

Due to increasing privacy concerns, neural network (NN) based secure
inference (SI) schemes that simultaneously hide the client inputs and server
models attract major research interests. While existing works focused on
developing secure protocols for NN-based SI, in this work, we take a different
approach. We propose NASS, an integrated framework to search for tailored NN
architectures designed specifically for SI. In particular, we propose to model
cryptographic protocols as design elements with associated reward functions.
The characterized models are then adopted in a joint optimization with
predicted hyperparameters in identifying the best NN architectures that balance
prediction accuracy and execution efficiency. In the experiment, it is
demonstrated that we can achieve the best of both worlds by using NASS, where
the prediction accuracy can be improved from 81.6% to 84.6%, while the
inference runtime is reduced by 2x and communication bandwidth by 1.9x on the
CIFAR-10 dataset.

本文提出了 NASS 框架，将加密协议建模为设计元素并与预测的超参数一起优化以找到平衡预测精度和执行效率的最佳 NN 架构。在实验中，使用 NASS 可以将预测精度从 81.6％提高到 84.6％，同时在 CIFAR-10 数据集上减少推理运行时间 2 倍，通信带宽减少 1.9 倍。

NASS: 通过神经结构搜索优化安全推理

NASS: Optimizing Secure Inference via Neural Architecture Search

Quantum state discrimination underlies various applications in quantum
information processing tasks. It essentially describes the distinguishability
of quantum systems in different states, and the general process of extracting
classical information from quantum systems. It is also useful in quantum
information applications, such as the characterisation of mutual information in
cryptographic protocols, or as a technique to derive fundamental theorems in
quantum foundations. It has deep connections to physical principles such as
relativistic causality. Quantum state discrimination traces a long history of
several decades, starting with the early attempts to formalise information
processing of physical systems such as optical communication with photons.
Nevertheless, in most cases, optimal strategies of quantum state discrimination
remain unsolved, and related applications are valid in some limited cases only.
The present review aims to provide an overview on quantum state discrimination,
covering some recent progress, and addressing applications in some selected
topics. This review serves to strengthen the link between results in quantum
state discrimination and quantum information applications, by showing the ways
in which the fundamental results are exploited in applications and vice versa.

本综述旨在提供关于量子态判别的概述，涵盖最近的进展以及在一些选定主题中的应用，并展示基本结果在应用中的利用方式及反之，以加强量子态判别和量子信息应用结果之间的联系。