Quantum computers can solve problems that are beyond the capabilities of
conventional computers. As quantum computers are expensive and hard to
maintain, the typical model for performing quantum computation is to send the
circuit to a quantum cloud provider. This leads to privacy concerns for
commercial entities as an untrusted server can learn protected information from
the provided circuit. Current proposals for Secure Quantum Computing (SQC)
either rely on emerging technologies (such as quantum networks) or incur
prohibitive overheads (for Quantum Homomorphic Encryption). The goal of our
paper is to enable low-cost privacy-preserving quantum computation that can be
used with current systems.
We propose Enigma, a suite of privacy-preserving schemes specifically
designed for the Quantum Approximate Optimization Algorithm (QAOA). Unlike
previous SQC techniques that obfuscate quantum circuits, Enigma transforms the
input problem of QAOA, such that the resulting circuit and the outcomes are
unintelligible to the server. We introduce three variants of Enigma. Enigma-I
protects the coefficients of QAOA using random phase flipping and fudging of
values. Enigma-II protects the nodes of the graph by introducing decoy qubits,
which are indistinguishable from primary ones. Enigma-III protects the edge
information of the graph by modifying the graph such that each node has an
identical number of connections. For all variants of Enigma, we demonstrate
that we can still obtain the solution for the original problem. We evaluate
Enigma using IBM quantum devices and show that the privacy improvements of
Enigma come at only a small reduction in fidelity (1%-13%).

我们提出了 Enigma，这是一套专门为量子近似优化算法（QAOA）设计的隐私保护方案，通过转变 QAOA 的输入问题使得输出电路和结果对服务器来说是无法理解的。我们介绍了 Enigma 的三个变体，通过在不同层面上进行隐私保护来获得原问题的解决方案，并在 IBM 量子设备上评估了 Enigma 的性能。

隐秘：在不可信任的量子计算机上保护隐私执行 QAOA

Enigma: Privacy-Preserving Execution of QAOA on Untrusted Quantum  Computers

We determine the accuracy with which machine learning and deep learning
techniques can classify selected World War II era ciphers when only ciphertext
is available. The specific ciphers considered are Enigma, M-209, Sigaba,
Purple, and Typex. We experiment with three classic machine learning models,
namely, Support Vector Machines (SVM), $k$-Nearest Neighbors ($k$-NN), and
Random Forest (RF). We also experiment with four deep learning neural
network-based models: Multi-Layer Perceptrons (MLP), Long Short-Term Memory
(LSTM), Extreme Learning Machines (ELM), and Convolutional Neural Networks
(CNN). Each model is trained on features consisting of histograms, digrams, and
raw ciphertext letter sequences. Furthermore, the classification problem is
considered under four distinct scenarios: Fixed plaintext with fixed keys,
random plaintext with fixed keys, fixed plaintext with random keys, and random
plaintext with random keys. Under the most realistic scenario, given 1000
characters per ciphertext, we are able to distinguish the ciphers with greater
than 97% accuracy. In addition, we consider the accuracy of a subset of the
learning techniques as a function of the length of the ciphertext messages.
Somewhat surprisingly, our classic machine learning models perform at least as
well as our deep learning models. We also find that ciphers that are more
similar in design are somewhat more challenging to distinguish, but not as
difficult as might be expected.

通过机器学习和深度学习技术识别二战期间选择的密码学中 Enigma，M-209，Sigaba，Purple 和 Typex 等密码的准确性

利用机器学习对二战时期密码进行分类

Classifying World War II Era Ciphers with Machine Learning

Reliable automatic evaluation of dialogue systems under an interactive
environment has long been overdue. An ideal environment for evaluating dialog
systems, also known as the Turing test, needs to involve human interaction,
which is usually not affordable for large-scale experiments. Though researchers
have attempted to use metrics (e.g., perplexity, BLEU) in language generation
tasks or some model-based reinforcement learning methods (e.g., self-play
evaluation) for automatic evaluation, these methods only show a very weak
correlation with the actual human evaluation in practice. To bridge such a gap,
we propose a new framework named ENIGMA for estimating human evaluation scores
based on recent advances of off-policy evaluation in reinforcement learning.
ENIGMA only requires a handful of pre-collected experience data, and therefore
does not involve human interaction with the target policy during the
evaluation, making automatic evaluations feasible. More importantly, ENIGMA is
model-free and agnostic to the behavior policies for collecting the experience
data (see details in Section 2), which significantly alleviates the technical
difficulties of modeling complex dialogue environments and human behaviors. Our
experiments show that ENIGMA significantly outperforms existing methods in
terms of correlation with human evaluation scores.

提出了一种名为 ENIGMA 的自动对话系统评估框架，该框架基于策略评估的最新进展，并且相较于现有的自动评估方法有更强的人类评估得分相关性。

自动对话系统评估：一种无模型离线策略评估方法

Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy  Evaluation Approach

We present $\texttt{ENIGMA}$, a time domain, inspiral-merger-ringdown
waveform model that describes non-spinning binary black holes systems that
evolve on moderately eccentric orbits. The inspiral evolution is described
using a consistent combination of post-Newtonian theory, self-force and black
hole perturbation theory. Assuming eccentric binaries that circularize prior to
coalescence, we smoothly match the eccentric inspiral with a stand-alone,
quasi-circular merger, which is constructed using machine learning algorithms
that are trained with quasi-circular numerical relativity waveforms. We show
that $\texttt{ENIGMA}$ reproduces with excellent accuracy the dynamics of
quasi-circular compact binaries. We validate $\texttt{ENIGMA}$ using a set of
$\texttt{Einstein Toolkit}$ eccentric numerical relativity waveforms, which
describe eccentric binary black hole mergers with mass-ratios between $1 \leq q
\leq 5.5$, and eccentricities $e_0 \lesssim 0.2$ ten orbits before merger. We
use this model to explore in detail the physics that can be extracted with
moderately eccentric, non-spinning binary black hole mergers. We use
$\texttt{ENIGMA}$ to show that GW150914, GW151226, GW170104, GW170814 and
GW170608 can be effectively recovered with spinning, quasi-circular templates
if the eccentricity of these events at a gravitational wave frequency of 10Hz
satisfies $e_0\leq \{0.175,\, 0.125,\,0.175,\,0.175,\, 0.125\}$, respectively.
We show that if these systems have eccentricities $e_0\sim 0.1$ at a
gravitational wave frequency of 10Hz, they can be misclassified as
quasi-circular binaries due to parameter space degeneracies between
eccentricity and spin corrections. Using our catalog of eccentric numerical
relativity simulations, we discuss the importance of including higher-order
waveform multipoles in gravitational wave searches of eccentric binary black
hole mergers.

使用特定模型 ENIGMA 模拟黑洞的演化，经过验证，ENIGMA 可以模拟旋转非常准确的复杂中等偏心度的情况。探讨了在中等偏心度情况下物理模型的适用性以及在椭圆形情况下波形多极的重要性。