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arXiv:2107.01186 · Leibniz international proceedings in informatics

Quantum Multiple-Valued Decision Diagrams in Graphical Calculi

Renaud Vilmart

Published Jan 1, 2021• 0 citations
arXivHTMLPDF

Abstract

Graphical calculi such as the ZH-calculus are powerful tools in the study and analysis of quantum processes, with links to other models of quantum computation such as quantum circuits, measurement-based computing, etc. A somewhat compact but systematic way to describe a quantum process is through the use of quantum multiple-valued decision diagrams (QMDDs), which have already been used for the synthesis of quantum circuits as well as for verification. We show in this paper how to turn a QMDD into an equivalent ZH-diagram, and vice-versa, and show how reducing a QMDD translates in the ZH-Calculus, hence allowing tools from one formalism to be used into the other.

DOI
10.4230/lipics.mfcs.2021.89

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Graphical calculi such as the ZH-calculus are powerful tools in the study and analysis of quantum processes, with links to other models of quantum computation such as quantum circuits, measurement-based computing, etc. A somewhat compact but systematic way to describe a quantum process is through the use of quantum multiple-valued decision diagrams (QMDDs), which have already been used for the synthesis of quantum circuits as well as for verification. We show in this paper how to turn a QMDD into an equivalent ZH-diagram, and vice-versa, and show how reducing a QMDD translates in the ZH-Calculus, hence allowing tools from one formalism to be used into the other.
Quantum mechanical phenomena such as phase shifts, superposition, and entanglement show promise in use for computation. Suitable technologies for the modeling and design of quantum computers and other information processing techniques that exploit quantum mechanical principles are in the range of vision. Quantum algorithms that significantly speed up the process of solving several important computation problems have been proposed in the past. The most common representation of quantum mechanical phenomena are transformation matrices. However, the transformation matrices grow exponentially with the size of a quantum system and, thus, pose significant challenges for efficient representation and manipulation of quantum functionality. In order to address this problem, first approaches for the representation of quantum systems in terms of decision diagrams have been proposed. One very promising approach is given by Quantum Multiple-Valued Decision Diagrams (QMDDs) which are able to efficiently represent transformation matrices and also inherently support multiple-valued basis states offered by many physical quantum systems. However, the initial proposal of QMDDs was lacking in a formal basis and did not allow, e.g., the change of the variable order-an established core functionality in decision diagrams which is crucial for determining more compact representations. Because of this, the full potential of QMDDs or decision diagrams for quantum functionality in general has not been fully exploited yet. In this paper, we present a refined definition of QMDDs for the general quantum case. Furthermore, we provide significantly improved computational methods for their use and manipulation and show that the resulting representation satisfies important criteria for a decision diagram, i.e., compactness and canonicity. An experimental evaluation confirms the efficiency of QMDDs.
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.
In this paper, we present a novel structure, QuantumMultiple- valued Decision Diagrams (QMDD), specifically designed to represent and manipulate the matrices encountered in the specification of reversible and quantum gates and circuits, both binary and multiple-valued. QMDD use many common decision diagram techniques, ideas introduced in QuIDDPro and novel techniques introduced here. Building the QMDD for the matrices for individual gates and the subsequent construction of the QMDD for the matrix describing a circuit are discussed. A prototype C implementation is described and experimental results are given that show the new structure is a promising and compact representation for reversible and quantum logic circuits.
Quantum multiple-valued decision diagrams (QMDD) are data structures that enable efficient representation and manipulation of unitary matrices representing reversible and quantum circuits. QMDDs are of the form of a rooted, directed, acyclic graph with initial and terminal vertices where each vertex is annotated with a variable name representing a circuit line. Directed paths from the intial to a terminal vertex can be represented as a sequence of variable names in the order in which they appear in the path. The existence of QMDD paths that do not contain all variable names, or “skipped variables”, has direct ramifications in the formula-tion of synthesis and other algorithms for reversible and quantum logic. Skipped variables are generally rare and tend to appear in quantum circuits that are intended to operate using superimposed values on the control lines. We have found that a unitary matrix representing a circuit whose QMDD contains skipped variables is likely to exhibit a specific anomaly when decomposed into a cascade of unitary matrices using the Reck-Zeilinger-Bernstein-Bertani algorithm.
The data structure referred to as quantum multiple-valued decision diagrams (QMDD) is used to efficiently represent the unitary matrices describing reversible and quantum circuits. This paper investigates the conditions that cause skipped variables to appear in the QMDD of some binary and ternary quantum circuits. We have found that a unitary matrix that produces a skipped variable in a QMDD is likely to cause a specific anomaly when it is decomposed into a cascade of two-level unitary matrices by the Beck-Zeilinger-Bernstein-Bertani algorithm.
With the emergence of more and more applications for quantum computing, also the development of corresponding methods for design automation is receiving increasing interest. In this respect, decision diagrams provide a promising basis for many design tasks such as simulation, synthesis, verification, and more. However, users of the corresponding tools often do not have a proper background or an intuition about how these methods based on decision diagrams work and what their strengths and limits are. In an effort to make decision diagrams for quantum computing more accessible, we present a visualization tool which visualizes quantum decision diagrams and allows to explore their behavior when used in the design tasks mentioned above. The installation-free web-tool allows users to interactively learn how decision diagrams can be used in quantum computing, e.g., to (1) compactly represent quantum states and the functionality of quantum circuits, (2) to efficiently simulate quantum circuits, and (3) to verify the equivalence of two circuits. The tool is available at https://iic.jku.at/eda/research/quantum_dd/tool