Quantum algorithms, represented as quantum circuits, can be used as
benchmarks for assessing the performance of quantum systems. Existing datasets,
widely utilized in the field, suffer from limitations in size and versatility,
leading researchers to employ randomly generated circuits. Random circuits are,
however, not representative benchmarks as they lack the inherent properties of
real quantum algorithms for which the quantum systems are manufactured. This
shortage of `useful' quantum benchmarks poses a challenge to advancing the
development and comparison of quantum compilers and hardware.
This research aims to enhance the existing quantum circuit datasets by
generating what we refer to as `realistic-looking' circuits by employing the
Transformer machine learning architecture. For this purpose, we introduce
KetGPT, a tool that generates synthetic circuits in OpenQASM language, whose
structure is based on quantum circuits derived from existing quantum algorithms
and follows the typical patterns of human-written algorithm-based code (e.g.,
order of gates and qubits). Our three-fold verification process, involving
manual inspection and Qiskit framework execution, transformer-based
classification, and structural analysis, demonstrates the efficacy of KetGPT in
producing large amounts of additional circuits that closely align with
algorithm-based structures. Beyond benchmarking, we envision KetGPT
contributing substantially to AI-driven quantum compilers and systems.

利用 Transformer 机器学习架构生成更真实的量子线路数据集，通过 KetGPT 工具生成以人类编写的基于算法的代码结构为基础的 OpenQASM 语言的合成线路，可用于评估量子系统的性能，并为 AI 驱动的量子编译器和系统做出重要贡献。

KetGPT -- 基于 Transformer 的量子电路数据增强

KetGPT -- Dataset Augmentation of Quantum Circuits using Transformers

"Qubit routing" refers to the task of modifying quantum circuits so that they
satisfy the connectivity constraints of a target quantum computer. This
involves inserting SWAP gates into the circuit so that the logical gates only
ever occur between adjacent physical qubits. The goal is to minimise the
circuit depth added by the SWAP gates.
In this paper, we propose a qubit routing procedure that uses a modified
version of the deep Q-learning paradigm. The system is able to outperform the
qubit routing procedures from two of the most advanced quantum compilers
currently available, on both random and realistic circuits, across near-term
architecture sizes.

该论文提出了一种使用改进的深度 Q 学习范例的量子比特路由程序，能够在近期架构大小的随机和现实电路上优于目前两种最先进的量子编译器的比特路由程序。