Deep neural networks are a powerful tool for predicting properties of quantum
states from limited measurement data. Here we develop a network model that can
simultaneously predict multiple quantum properties, including not only
expectation values of quantum observables, but also general nonlinear functions
of the quantum state, like entanglement entropies and many-body topological
invariants. Remarkably, we find that a model trained on a given set of
properties can also discover new properties outside that set. Multi-purpose
training also enables the model to infer global properties of many-body quantum
systems from local measurements, to classify symmetry protected topological
phases of matter, and to discover unknown boundaries between different phases.

采用深度神经网络可以同时预测量子态的多个特性，并可通过局部测量来推断多体量子系统的全局特性，用于识别对称保护拓扑态和发现不同相之间的未知边界。

使用多任务神经网络学习和发现量子特性

Learning and Discovering Quantum Properties with Multi-Task Neural  Networks

Designing new molecules is essential for drug discovery and material science.
Recently, deep generative models that aim to model molecule distribution have
made promising progress in narrowing down the chemical research space and
generating high-fidelity molecules. However, current generative models only
focus on modeling either 2D bonding graphs or 3D geometries, which are two
complementary descriptors for molecules. The lack of ability to jointly model
both limits the improvement of generation quality and further downstream
applications. In this paper, we propose a new joint 2D and 3D diffusion model
(JODO) that generates complete molecules with atom types, formal charges, bond
information, and 3D coordinates. To capture the correlation between molecular
graphs and geometries in the diffusion process, we develop a Diffusion Graph
Transformer to parameterize the data prediction model that recovers the
original data from noisy data. The Diffusion Graph Transformer interacts node
and edge representations based on our relational attention mechanism, while
simultaneously propagating and updating scalar features and geometric vectors.
Our model can also be extended for inverse molecular design targeting single or
multiple quantum properties. In our comprehensive evaluation pipeline for
unconditional joint generation, the results of the experiment show that JODO
remarkably outperforms the baselines on the QM9 and GEOM-Drugs datasets.
Furthermore, our model excels in few-step fast sampling, as well as in inverse
molecule design and molecular graph generation. Our code is provided in
this https URL

本文提出了一种新的联合 2D 和 3D 扩散模型 (JODO)，它通过 Diffusion Graph Transformer 参数化数据预测模型，可以生成具有原子类型、形式电荷、键信息和 3D 坐标的完整分子，表现出色，具有逆向分子设计和分子图形生成的优越性能。

学习联合 2D 和 3D 扩散模型以生成完整分子

Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation

Quantum properties, such as entanglement and coherence, are indispensable
resources in various quantum information processing tasks. However, there still
lacks an efficient and scalable way to detecting these useful features,
especially for high-dimensional and multipartite quantum systems. In this work,
we exploit the convexity of samples without the desired quantum features and
design an unsupervised machine learning method to detect the presence of such
features as anomalies. Particularly, in the context of entanglement detection,
we propose a complex-valued neural network composed of pseudo-siamese network
and generative adversarial net, and then train it with only separable states to
construct non-linear witnesses for entanglement. It is shown via numerical
examples, ranging from two-qubit to ten-qubit systems, that our network is able
to achieve high detection accuracy which is above 97.5% on average.Moreover, it
is capable of revealing rich structures of entanglement, such as partial
entanglement among subsystems. Our results are readily applicable to the
detection of other quantum resources such as Bell nonlocality and steerability,
and thus our work could provide a powerful tool to extract quantum features
hidden in multipartite quantum data.

采用无监督机器学习方法，设计了一种凸优化的复数神经网络，并利用训练好的网络进行了大量的数值实验，结果表明该网络能够高精度地检测出多体量子数据中的量子特征，如纠缠和局部纠缠。