.png){kind=icon}
返回
MQT Predictor: Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing
.png){kind=icon}
OA
AI
DOI：10.1145/3673241.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Fueled by recent accomplishments in quantum computing hardware and software, an increasing number of problems from various application domains are being explored as potential use cases for this new technology. Similarly to classical computing, realizing an application on a particular quantum device requires the corresponding (quantum) circuit to be compiled so that it can be executed on the device. With a steadily growing number of available devices-each with their own advantages and disadvantages-and a wide variety of different compilation tools, the number of choices to consider when trying to realize an application is quickly exploding. Due to missing tool support and automation, especially end-users who are not quantum computing experts are easily left unsupported and overwhelmed. In this work, we propose a methodology that allows one to automatically select a suitable quantum device for a particular application and provides an optimized compiler for the selected device. The resulting framework-called the MQT Predictor-not only supports end-users in navigating the vast landscape of choices, it also allows mixing and matching compiler passes from various tools to create optimized compilers that transcend the individual tools. Evaluations of an exemplary framework instantiation based on more than 500 quantum circuits and seven devices have shown that-compared with both Qiskit's and TKET's most optimized compilation flows for all devices-the MQT Predictor produces circuits within the top-3 out of 14 baselines in more than 98% of cases while frequently outperforming any tested combination by up to 53% when optimizing for expected fidelity. Additionally, the framework is trained and evaluated for critical depth as another figure of merit to showcase its flexibility and generalizability-producing circuits within the top-3 in 89% of cases while frequently outperforming any tested combination by up to 400%. MQT Predictor is part of the Munich Quantum Toolkit (MQT) and publicly available as open-source on GitHub (https://github.com/cdatum/mqt-predictor) and as an easy-to-use Python package (https://pypi.org/p/mqt.predictor).
Keyword:
Quantum circuit compilation
quantum device selection
machine learning for quantum computing
quantum computing software
期刊
A
IF:
6.8
论文数:
539
被引数:
508
机构
引用论文
A variational eigenvalue solver on a photonic quantum processor光子量子处理器上的变分特征值求解器.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏THE VOLUNTARY INTAKE AND DIGESTIBILITY BY SHEEP OE LEAF AND STEM FRACTIONS OF LOLIUM PERENNE绵羊对Lolium perenne叶和茎组分的选择性采食量及消化率
PREAI
分享收藏Quantum Generative Adversarial Networks for learning and loading random distributions用于学习和加载随机分布的量子生成对抗网络OAAI
OAAI分享收藏分享收藏
查看更多内容