Quantum Multiple-Valued Decision Diagrams with Linear Transformations

TL;DR

This paper introduces linearly transformed quantum multiple-valued decision diagrams (LTQMDDs) with a linear sifting algorithm, improving the compactness of quantum function representations and outperforming traditional methods in certain quantum circuit types.

Contribution

The paper presents a novel canonical form for QMDDs using linear transformations and a linear sifting algorithm to optimize decision diagram size.

Findings

Linear sifting algorithm produces significantly smaller decision diagrams.

LTQMDDs outperform original QMDDs in certain quantum circuits.

The approach enhances compactness and efficiency of quantum operation representations.

Abstract

Due to the rapid development of quantum computing, the compact representation of quantum operations based on decision diagrams has been received more and more attraction. Since variable orders have a significant impact on the size of the decision diagram, identifying a good variable order is of paramount importance. In this paper, we integrate linear transformations into an efficient and canonical form of quantum computing: Quantum Multiple-Valued Decision Diagrams (QMDDs) and develop a novel canonical representation, namely linearly transformed QMDDs (LTQMDDs). We design a linear sifting algorithm for LTQMDDs that search a good linear transformation to obtain a more compact form of quantum function. Experimental results show that the linear sifting algorithm is able to generate decision diagrams that are significantly improved compared with the original sifting algorithm. Moreover, for certain types of circuits, linear sifting algorithm have good performance whereas sifting algorithm does not decrease the size of QMDDs.