Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Computers, materials & continua/Computers, materials & continua (Print)

An Extended Approach for Generating Unitary Matrices for Quantum Circuits

Zhiqiang Li, Wei Zhang, Gaoman Zhang, Juan Dai, Jiajia Hu, Marek A. Perkowski, Xiaoyu Song B

Published Jan 1, 2020• 5 citations
Publisher

Abstract

In this paper, we do research on generating unitary matrices for quantum circuits automatically. We consider that quantum circuits are divided into six types, and the unitary operator expressions for each type are offered... | Find, read and cite all the research you need on Tech Science Press
DOI
10.32604/cmc.2020.07483

Related papers

Algorithm to build quantum circuit from classical description of DFSM

Varun Puram, Dingyi Kang, K. M. George +1 more

There are several papers that define quantum finite automaton analogous to classical finite automaton. Some papers describe the power of quantum automaton. In general, in quantum automata unitary matrices are associated to input symbols. Even though quantum circuits (or unitary matrices) are shown in specific cases, there is no general method given to construct a circuit or unitary matrix is found in the literature. This research focuses on construction of unitary matrices for each input symbol from the classical definition of a Deterministic Finite State Machine (DFSM). An algorithm is developed to associate a quantum circuit to an input symbol which considered as a mapping of classical DFSM states. IBM quantum composer is used to design circuits and build examples.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 1, 2022•2 citations

Quantum Circuit Dataset Generator Approach for Deep Learning based Solutions

Níyazí Furkan Bar, Mehmet Karaköse

Today, although there are studies for the active use of quantum computers, deep learning-based studies on quantum computing are few. Since the large amount of data needed by deep learning cannot be met from the literature, using deep learning in subjects such as quantum circuit optimization, and quantum circuit/algorithm generation challenges researchers. In this study, an approach is proposed to overcome the data and dataset deficiencies related to quantum circuits in the literature. The proposed approach generates datasets consisting of quantum circuits, truth tables of quantum circuits, unitary matrices, visuals, and pairs of these data. The circuits, data, and datasets produced with this approach have already been studied and are still being actively done. The performance of the proposed approach was evaluated as dependent on different variables and as elapsed time. With the proposed approach, circuits, data, and datasets with desired properties can be generated successfully. As a result, the contribution and performance of the proposed approach have been verified.

•Feb 21, 2024•6 citations

Efficient quantum circuits for dense and non-unitary operators

Sisi Zhou, J. B. Wang

Circulant matrices are an important family of operators, which have a wide range of applications in science and engineering related fields. They are in general non-sparse and non-unitary. In this paper, we present efficient quantum circuits to implement circulant operators using fewer resources and with lower complexity than existing methods. Moreover, our quantum circuits can be readily extended to the implementation of Toeplitz, Hankel, and block circulant matrices. Efficient quantum algorithms to implement the inverses and products of circulant operators are also provided.

arXiv:1607.07149•Jul 25, 2016•2 citations

Some algorithms for calculating unitary matrices for quantum circuits

Vladimir P. Gerdt, Alexander N. Prokopenya

Algorithms for calculating unitary matrices determined by quantum circuits are discussed. The algorithms are used in the program QuantumCircuit designed for modeling quantum circuits. Practical implementations of the algorithms as functions written in the language of the Mathematica system are suggested.

Programming and Computer Software•Mar 1, 2010•8 citations

Research on Optimization of Hierarchical Quantum Circuit Scheduling Strategy

Ziao Han, Hui Li, Kai Lu +2 more

Traditional quantum circuit scheduling approaches underutilize the inherent parallelism of quantum computation in the Noisy Intermediate-Scale Quantum (NISQ) era, overlook the inter-layer operations can be further parallelize... | Find, read and cite all the research you need on Tech Science Press

Computers, materials & continua/Computers, materials & continua (Print)•Jan 1, 2025•1 citation

Efficient synthesis of quantum circuits implementing clifford group operations

Philipp Niemann, Robert Wille, Rolf Drechsler

Quantum circuits established themselves as a promising emerging technology and, hence, attracted considerable attention in the domain of computer-aided design. As a result, many approaches for synthesis of corresponding netlists have been proposed in the last decade. However, as the design of quantum circuits faces serious obstacles caused by phenomena such as superposition, entanglement, and phase shifts, automatic synthesis still represents a significant challenge. In this paper, we propose an automatic synthesis approach for quantum circuits that implement Clifford Group operations. These circuits are essential for many quantum applications and cover core aspects of quantum functionality. The proposed approach exploits specific properties of the unitary transformation matrices that are associated to quantum operations. Furthermore, Quantum Multiple-Valued Decision Diagrams (QMDDs) are employed for an efficient representation of these matrices. Experimental results confirm that this enables a compact realization of the respective quantum functionality.

Asia and South Pacific Design Automation Conference (ASP-DAC)•Jan 1, 2014•49 citations
There are several papers that define quantum finite automaton analogous to classical finite automaton. Some papers describe the power of quantum automaton. In general, in quantum automata unitary matrices are associated to input symbols. Even though quantum circuits (or unitary matrices) are shown in specific cases, there is no general method given to construct a circuit or unitary matrix is found in the literature. This research focuses on construction of unitary matrices for each input symbol from the classical definition of a Deterministic Finite State Machine (DFSM). An algorithm is developed to associate a quantum circuit to an input symbol which considered as a mapping of classical DFSM states. IBM quantum composer is used to design circuits and build examples.
Today, although there are studies for the active use of quantum computers, deep learning-based studies on quantum computing are few. Since the large amount of data needed by deep learning cannot be met from the literature, using deep learning in subjects such as quantum circuit optimization, and quantum circuit/algorithm generation challenges researchers. In this study, an approach is proposed to overcome the data and dataset deficiencies related to quantum circuits in the literature. The proposed approach generates datasets consisting of quantum circuits, truth tables of quantum circuits, unitary matrices, visuals, and pairs of these data. The circuits, data, and datasets produced with this approach have already been studied and are still being actively done. The performance of the proposed approach was evaluated as dependent on different variables and as elapsed time. With the proposed approach, circuits, data, and datasets with desired properties can be generated successfully. As a result, the contribution and performance of the proposed approach have been verified.
Circulant matrices are an important family of operators, which have a wide range of applications in science and engineering related fields. They are in general non-sparse and non-unitary. In this paper, we present efficient quantum circuits to implement circulant operators using fewer resources and with lower complexity than existing methods. Moreover, our quantum circuits can be readily extended to the implementation of Toeplitz, Hankel, and block circulant matrices. Efficient quantum algorithms to implement the inverses and products of circulant operators are also provided.
Quantum circuits established themselves as a promising emerging technology and, hence, attracted considerable attention in the domain of computer-aided design. As a result, many approaches for synthesis of corresponding netlists have been proposed in the last decade. However, as the design of quantum circuits faces serious obstacles caused by phenomena such as superposition, entanglement, and phase shifts, automatic synthesis still represents a significant challenge. In this paper, we propose an automatic synthesis approach for quantum circuits that implement Clifford Group operations. These circuits are essential for many quantum applications and cover core aspects of quantum functionality. The proposed approach exploits specific properties of the unitary transformation matrices that are associated to quantum operations. Furthermore, Quantum Multiple-Valued Decision Diagrams (QMDDs) are employed for an efficient representation of these matrices. Experimental results confirm that this enables a compact realization of the respective quantum functionality.