论文标题
基于张量网络的决策图，用于表示量子电路
A Tensor Network based Decision Diagram for Representation of Quantum Circuits
论文作者
论文摘要
数十年来，张量网络已成功地应用于量子物理系统中。最近，它们也用于量子计算的经典模拟，特别是随机量子电路。本文提出了一个名为TDD（张量决策图）的决策图样式数据结构，以实现张量网络的更原则和方便的应用。这种新的数据结构为量子电路提供了紧凑而规范的表示。通过利用电路分区，可以有效地计算量子电路的TDD。此外，我们表明张量网络在其应用中必不可少的（例如加法和收缩）的操作也可以在TDD中有效地实施。提出了TDD的概念验证实施，并在一组基准量子电路上评估其效率。预计TDD将在与量子电路有关的各种设计自动化任务中发挥重要作用，包括但不限于等效检查，错误检测，合成，仿真和验证。
Tensor networks have been successfully applied in simulation of quantum physical systems for decades. Recently, they have also been employed in classical simulation of quantum computing, in particular, random quantum circuits. This paper proposes a decision diagram style data structure, called TDD (Tensor Decision Diagram), for more principled and convenient applications of tensor networks. This new data structure provides a compact and canonical representation for quantum circuits. By exploiting circuit partition, the TDD of a quantum circuit can be computed efficiently. Furthermore, we show that the operations of tensor networks essential in their applications (e.g., addition and contraction), can also be implemented efficiently in TDDs. A proof-of-concept implementation of TDDs is presented and its efficiency is evaluated on a set of benchmark quantum circuits. It is expected that TDDs will play an important role in various design automation tasks related to quantum circuits, including but not limited to equivalence checking, error detection, synthesis, simulation, and verification.
