AltGraph：使用生成式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
AI总结：
提出AltGraph方法，利用D-VAE和DeepGMG等生成式图模型对量子电路DAG的潜在空间进行扰动以生成等效电路，在保持高保真度的同时显著减少了门数量和电路深度。
摘要：
量子电路转换旨在生成等效电路，同时优化电路深度、门数量以及与现代噪声中等规模量子（NISQ）设备的兼容性等多个方面。电路转换有两种技术。第一种是基于规则的方法，贪婪地抵消等同于单位酉操作的门对。基于规则的方法用于Qiskit、tket和Quilc等量子编译器。第二种是基于搜索的方法，通过探索量子电路搜索空间来寻找等效量子电路。基于搜索的方法通常依赖于生成模型和强化学习（RL）等机器学习技术。在本工作中，我们提出了AltGraph，一种新颖的基于搜索的电路转换方法，使用现有的生成式图模型生成等效量子电路。我们使用三个主要图模型：DAG变分自编码器（D-VAE）的两个变体——门控循环单元（GRU）和图卷积网络（GCN），以及图深度生成模型（DeepGMG），它们将量子电路的有向无环图（DAG）作为输入，并输出新的DAG，据此我们重构等效电路。接着，我们扰动潜在空间以生成等效量子电路，其中部分电路可能更兼容硬件耦合映射，或能实现更好的优化，从而减少门数量和电路深度。与原始转换后的电路相比，AltGraph在转换后平均减少了37.55%的门数量和37.75%的电路深度，密度矩阵的均方误差（MSE）仅为0.0074。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
