Quantum circuit transformation aims to produce equivalent circuits while
optimizing for various aspects such as circuit depth, gate count, and
compatibility with modern Noisy Intermediate Scale Quantum (NISQ) devices.
There are two techniques for circuit transformation. The first is a rule-based
approach that greedily cancels out pairs of gates that equate to the identity
unitary operation. Rule-based approaches are used in quantum compilers such as
Qiskit, tket, and Quilc. The second is a search-based approach that tries to
find an equivalent quantum circuit by exploring the quantum circuits search
space. Search-based approaches typically rely on machine learning techniques
such as generative models and Reinforcement Learning (RL). In this work, we
propose AltGraph, a novel search-based circuit transformation approach that
generates equivalent quantum circuits using existing generative graph models.
We use three main graph models: DAG Variational Autoencoder (D-VAE) with two
variants: Gated Recurrent Unit (GRU) and Graph Convolutional Network (GCN), and
Deep Generative Model for Graphs (DeepGMG) that take a Direct Acyclic Graph
(DAG) of the quantum circuit as input and output a new DAG from which we
reconstruct the equivalent quantum circuit. Next, we perturb the latent space
to generate equivalent quantum circuits some of which may be more compatible
with the hardware coupling map and/or enable better optimization leading to
reduced gate count and circuit depth. AltGraph achieves on average a 37.55%
reduction in the number of gates and a 37.75% reduction in the circuit depth
post-transpiling compared to the original transpiled circuit with only 0.0074
Mean Squared Error (MSE) in the density matrix.

本研究提出了一种名为 AltGraph 的搜索型电路转换方法，使用现有的生成图模型生成等效的量子电路。使用三个主要的图模型：DAG Variational Autoencoder (D-VAE)、Gated Recurrent Unit (GRU) 和 Graph Convolutional Network (GCN)，以及 Deep Generative Model for Graphs (DeepGMG)。AltGraph 比原始转换电路平均减少 37.55% 的门数量和 37.75% 的电路深度，且平均密度矩阵均方误差 (MSE) 仅为 0.0074。

AltGraph: 用生成图模型重新设计量子电路，以实现高效优化

AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for  Efficient Optimization

Past research into robotic planning with temporal logic specifications,
notably Linear Temporal Logic (LTL), was largely based on singular formulas for
individual or groups of robots. But with increasing task complexity, LTL
formulas unavoidably grow lengthy, complicating interpretation and
specification generation, and straining the computational capacities of the
planners. In order to maximize the potential of LTL specifications, we
capitalized on the intrinsic structure of tasks and introduced a hierarchical
structure to LTL specifications, and designed an algorithm to ascertain whether
they are satisfied given an input sequence. Second, we employ a search-based
approach to synthesize plans for a multi-robot system, accomplishing
simultaneous task allocation and planning. The search space is approximated by
loosely interconnected sub-spaces, with each sub-space corresponding to one LTL
specification. The search is predominantly confined to a single sub-space,
transitioning to another sub-space under certain conditions, determined by the
decomposition of automatons. Moreover, multiple heuristics are formulated to
expedite the search significantly. A theoretical analysis concerning
completeness and optimality is conducted under mild assumptions. When compared
with existing methods on service tasks, our method outperforms in terms of
execution times with comparable solution quality. Finally, scalability is
evaluated by testing a group of 30 robots and achieving reasonable runtimes.

通过引入层次结构和基于搜索的方法，利用时间逻辑规范解决了多机器人系统的任务规划问题，并在可扩展性方面得到验证。

多机器人在层次化时间逻辑规范下的同时任务分配和规划

Simultaneous Task Allocation and Planning for Multi-Robots under  Hierarchical Temporal Logic Specifications

Behavioural cloning uses a dataset of demonstrations to learn a behavioural
policy. To overcome various learning and policy adaptation problems, we propose
to use latent space to index a demonstration dataset, instantly access similar
relevant experiences, and copy behavior from these situations. Actions from a
selected similar situation can be performed by the agent until representations
of the agent's current situation and the selected experience diverge in the
latent space. Thus, we formulate our control problem as a search problem over a
dataset of experts' demonstrations. We test our approach on BASALT
MineRL-dataset in the latent representation of a Video PreTraining model. We
compare our model to state-of-the-art Minecraft agents. Our approach can
effectively recover meaningful demonstrations and show human-like behavior of
an agent in the Minecraft environment in a wide variety of scenarios.
Experimental results reveal that performance of our search-based approach is
comparable to trained models, while allowing zero-shot task adaptation by
changing the demonstration examples.

使用潜空间索引演示数据集，成功地利用搜索技术实现基于行为克隆算法的控制，因此收获了具有人类特征、可以适应各种情境的代理行为，比现有的训练模型更加高效，具有无需任务适应等优点。