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

Visualization

QuFlow: Visualizing Parameter Flow in Quantum Circuits for Understanding Quantum Computation

Siyuan Lin, Hao Jiang, Lingyun Sun

Published Oct 1, 2018• 6 citations
Publisher

Abstract

With the rapid progress of quantum computation recently, it attracts a substantial amount of people to learn quantum computation. Quantum computation differs a lot from classical computation. To help novices learn quantum computation, we conducted a interview with people who are learning quantum computation and found that novices feel confused about 1) how quantum gates contribute to the final results in a complicated quantum circuit and 2) how the final results generate steps along the quantum circuits. Thus, we present QuFlow, an interactive visualization tool for teaching the fundamentals of quantum computation. Users can use it to build a quantum circuit, then QuFlow will simulate the quantum circuit in a classical computer. After simulation, QuFlow will not only present the final output results of quantum circuits but also shows how the parameters change along the quantum circuits. A qualitative user study was carried out among target users, and the results suggested that QuFlow could be effective for learning quantum computation.

DOI
10.1109/scivis.2018.8823602

Related papers

QCVis: A Quantum Circuit Visualization and Education Platform for Novices

Milan Marie Williams

QCVis is a quantum circuit visualization and education platform for novices. The rapid growth of the quantum computing field surpasses the educational capacity of existing systems, which makes novices particularly in need of accessible tools. Often, novices struggle to understand two fundamental concepts: how quantum gates affect both qubit and quantum circuit state probabilities over time. QCVis contributes two novel visualizations to develop this intuition. One visualization teaches users about the relationship between gates and single qubit probabilities. This feature integrates stacked bar charts into the traditional quantum circuit diagram to display the state probability of a single qubit after each gate application. The other visualization provides insight into the intermediate steps that contribute to the final state probability distribution. The combination of a radial bar chart and a time slider helps users see the state probability of a quantum circuit at a user-specified execution step and observe how it changes over time. We evaluated the design of QCVis in a user study with novices, who successfully used our interface to understand these key quantum computing concepts.

•Jun 3, 2021•3 citations

QNotation: An Interactive Visual Tool to Lower Learning Barriers in Quantum Computing

Samantha Norrie, Anthony Estey

One of the first challenges that new learners of Quantum Computing face is learning how to use and interpret quantum circuits represented in circuit, Dirac, and matrix notation. It is necessary for learners to understand the different notations used in Quantum Computing in order to ensure that they have a robust comprehension of the field and can make use of a variety of resources. Depending on their technical background, some learners may struggle with certain notations more than others. We present QNotation, a visual tool that helps learners explore the aforementioned notations. Circuit, Dirac, and matrix notation. This allows them to be able to identify the differences and similarities between the different notations. QNotation was built to be used at multiple stages throughout the Quantum Computer learning journey. While one may start using QNotation to help them become familiar with the different notations used in Quantum Computing, the tool can also be used as a visual quantum circuit debugging tool. In addition to QNotation being a tool that can be used by learners, it can also be used by instructors to support the teachings of quantum computing concepts and building blocks, including entanglement and Quantum Fourier Transform.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 17, 2023•4 citations

Quantum Image Visualizer: Visual Debugging of Quantum Image Processing Circuits

Anja Heim, Lang, Thomas, Alexander Gall +2 more

Quantum computing is an emerging field that utilizes the unique principles of quantum mechanics to offer significant advantages in algorithm execution over classical approaches. This potential is particularly promising in the domain of quantum image processing, which aims to manipulate all pixels simultaneously. However, the process of designing and verifying these algorithms remains a complex and error-prone task. To address this challenge, new methods are needed to support effective debugging of quantum circuits. The Quantum Image Visualizer is an interactive visual analysis tool that allows for the examination of quantum images and their transformation throughout quantum circuits. The framework incorporates two overview visualizations that trace image evolution across a sequence of gates based on the most probable outcomes. Interactive exploration allows users to focus on relevant gates, and select pixels of interest. Upon selection, detailed visualizations enable in-depth inspection of individual pixels and their probability distributions, revealing how specific gates influence the likelihood of pixel color values and the magnitude of these changes. An evaluation of the Quantum Image Visualizer was conducted through in-depth interviews with eight domain experts. The findings demonstrate the effectiveness and practical value of our approach in supporting visual debugging of quantum image processing circuits.

arXiv:2504.09902•Apr 14, 2025•0 citations

A Visual Composer for Quantum Circuits

Anas Tamer Osman, Milad M. Ghantous, Hassan Soubra

Designing and prototyping quantum circuits remains challenging for beginners and intermediate users due to steep learning curves in text-based frameworks and limited features in existing visual tools. This paper introduces QuantumFlow, a web-based platform that addresses these challenges through an accessible drag-and-drop interface for constructing and simulating quantum circuits. The platform provides realtime validation, state visualization, and interactive Bloch-sphere rendering to enable both conceptual exploration and rapid prototyping. Key features include modular subcircuit design, mid-circuit measurements, export to Qiskit, Cirq, and JSON, and hardware-aware transpilation for processors such as IBM's Falcon and Google's Sycamore. Implemented with React and Redux, the platform runs entirely client-side without installation. A structured evaluation with$\mathbf{1 4}$participants found that QuantumFlow users completed circuit construction tasks$\mathbf{4 5 \%}$faster and scored 38 % higher on comprehension assessments than peers relying on textbook workflows. Exported code aligns with framework standards and incorporates optimizations including gate fusion, commutation, and device-specific mapping. Current limitations involve scalability constraints and the absence of direct backend connectivity. Overall, QuantumFlow enhances accessibility to quantum computing education, supports interactive learning, and accelerates early-stage development, effectively bridging instructional platforms with advanced frameworks.

International Conference on Future Telecommunications and Artificial Intelligence (IC-FTAI)•Nov 24, 2025•0 citations

Quantum Simulator as a Training Tool

Darkhan Mami, Azamat Amirov, Murat G. Kozhanov +2 more

n the field of quantum computing, the properties of quantum superposition offer a promising potential for solving complex problems in a few minutes, while classical computers would take years to solve the same problem. Therefore, many institutions have introduced courses related to quantum mechanics and quantum computing into their programs. However, there is currently no known web-based tool or PC application dedicated to the study of quantum computing. Quantum computers are not available for educational purposes. In the early stages of learning, students may benefit from a way to practice quantum computing with a visual tool. Quantum computing is usually expressed by a quantum circuit with its sequences of quantum gates. The article talks about the creation of an educational simulator for the study of quantum circuits with various gates, functions and transformation rules. This quantum simulator will help students learn the principles of quantum circuitry in their undergraduate and graduate courses. This tool will offer functions that allow you to build a quantum circuit, add and remove quantum gates, apply transformation rules, and observe the operation of a quantum circuit with its unitary matrix. By practicing with the quantum circuit simulator, students can strengthen their theoretical knowledge base about quantum computing and linear algebraic functions

Trudy Universiteta•Dec 26, 2023•0 citations

QuantumEyes: Towards Better Interpretability of Quantum Circuits

Shaolun Ruan, Qiang Guan, Paul Griffin +2 more

Quantum computing offers significant speedup compared to classical computing, which has led to a growing interest among users in learning and applying quantum computing across various applications. However, quantum circuits, which are fundamental for implementing quantum algorithms, can be challenging for users to understand due to their underlying logic, such as the temporal evolution of quantum states and the effect of quantum amplitudes on the probability of basis quantum states. To fill this research gap, we propose QuantumEyes, an interactive visual analytics system to enhance the interpretability of quantum circuits through both global and local levels. For the global-level analysis, we present three coupled visualizations to delineate the changes of quantum states and the underlying reasons: a Probability Summary View to overview the probability evolution of quantum states; a State Evolution View to enable an in-depth analysis of the influence of quantum gates on the quantum states; a Gate Explanation View to show the individual qubit states and facilitate a better understanding of the effect of quantum gates. For the local-level analysis, we design a novel geometrical visualization dandelion chart to explicitly reveal how the quantum amplitudes affect the probability of the quantum state. We thoroughly evaluated QuantumEyes as well as the novel dandelion chart integrated into it through two case studies on different types of quantum algorithms and in-depth expert interviews with 12 domain experts. The results demonstrate the effectiveness and usability of our approach in enhancing the interpretability of quantum circuits.

arXiv:2311.07980•IEEE Transactions on Visualization and Computer Graphics•Nov 15, 2023•12 citations
With the rapid progress of quantum computation recently, it attracts a substantial amount of people to learn quantum computation. Quantum computation differs a lot from classical computation. To help novices learn quantum computation, we conducted a interview with people who are learning quantum computation and found that novices feel confused about 1) how quantum gates contribute to the final results in a complicated quantum circuit and 2) how the final results generate steps along the quantum circuits. Thus, we present QuFlow, an interactive visualization tool for teaching the fundamentals of quantum computation. Users can use it to build a quantum circuit, then QuFlow will simulate the quantum circuit in a classical computer. After simulation, QuFlow will not only present the final output results of quantum circuits but also shows how the parameters change along the quantum circuits. A qualitative user study was carried out among target users, and the results suggested that QuFlow could be effective for learning quantum computation.
QCVis is a quantum circuit visualization and education platform for novices. The rapid growth of the quantum computing field surpasses the educational capacity of existing systems, which makes novices particularly in need of accessible tools. Often, novices struggle to understand two fundamental concepts: how quantum gates affect both qubit and quantum circuit state probabilities over time. QCVis contributes two novel visualizations to develop this intuition. One visualization teaches users about the relationship between gates and single qubit probabilities. This feature integrates stacked bar charts into the traditional quantum circuit diagram to display the state probability of a single qubit after each gate application. The other visualization provides insight into the intermediate steps that contribute to the final state probability distribution. The combination of a radial bar chart and a time slider helps users see the state probability of a quantum circuit at a user-specified execution step and observe how it changes over time. We evaluated the design of QCVis in a user study with novices, who successfully used our interface to understand these key quantum computing concepts.
One of the first challenges that new learners of Quantum Computing face is learning how to use and interpret quantum circuits represented in circuit, Dirac, and matrix notation. It is necessary for learners to understand the different notations used in Quantum Computing in order to ensure that they have a robust comprehension of the field and can make use of a variety of resources. Depending on their technical background, some learners may struggle with certain notations more than others. We present QNotation, a visual tool that helps learners explore the aforementioned notations. Circuit, Dirac, and matrix notation. This allows them to be able to identify the differences and similarities between the different notations. QNotation was built to be used at multiple stages throughout the Quantum Computer learning journey. While one may start using QNotation to help them become familiar with the different notations used in Quantum Computing, the tool can also be used as a visual quantum circuit debugging tool. In addition to QNotation being a tool that can be used by learners, it can also be used by instructors to support the teachings of quantum computing concepts and building blocks, including entanglement and Quantum Fourier Transform.
Quantum computing is an emerging field that utilizes the unique principles of quantum mechanics to offer significant advantages in algorithm execution over classical approaches. This potential is particularly promising in the domain of quantum image processing, which aims to manipulate all pixels simultaneously. However, the process of designing and verifying these algorithms remains a complex and error-prone task. To address this challenge, new methods are needed to support effective debugging of quantum circuits. The Quantum Image Visualizer is an interactive visual analysis tool that allows for the examination of quantum images and their transformation throughout quantum circuits. The framework incorporates two overview visualizations that trace image evolution across a sequence of gates based on the most probable outcomes. Interactive exploration allows users to focus on relevant gates, and select pixels of interest. Upon selection, detailed visualizations enable in-depth inspection of individual pixels and their probability distributions, revealing how specific gates influence the likelihood of pixel color values and the magnitude of these changes. An evaluation of the Quantum Image Visualizer was conducted through in-depth interviews with eight domain experts. The findings demonstrate the effectiveness and practical value of our approach in supporting visual debugging of quantum image processing circuits.
Designing and prototyping quantum circuits remains challenging for beginners and intermediate users due to steep learning curves in text-based frameworks and limited features in existing visual tools. This paper introduces QuantumFlow, a web-based platform that addresses these challenges through an accessible drag-and-drop interface for constructing and simulating quantum circuits. The platform provides realtime validation, state visualization, and interactive Bloch-sphere rendering to enable both conceptual exploration and rapid prototyping. Key features include modular subcircuit design, mid-circuit measurements, export to Qiskit, Cirq, and JSON, and hardware-aware transpilation for processors such as IBM's Falcon and Google's Sycamore. Implemented with React and Redux, the platform runs entirely client-side without installation. A structured evaluation with14\mathbf{1 4}14participants found that QuantumFlow users completed circuit construction tasks45%\mathbf{4 5 \%}45%faster and scored 38 % higher on comprehension assessments than peers relying on textbook workflows. Exported code aligns with framework standards and incorporates optimizations including gate fusion, commutation, and device-specific mapping. Current limitations involve scalability constraints and the absence of direct backend connectivity. Overall, QuantumFlow enhances accessibility to quantum computing education, supports interactive learning, and accelerates early-stage development, effectively bridging instructional platforms with advanced frameworks.
n the field of quantum computing, the properties of quantum superposition offer a promising potential for solving complex problems in a few minutes, while classical computers would take years to solve the same problem. Therefore, many institutions have introduced courses related to quantum mechanics and quantum computing into their programs. However, there is currently no known web-based tool or PC application dedicated to the study of quantum computing. Quantum computers are not available for educational purposes. In the early stages of learning, students may benefit from a way to practice quantum computing with a visual tool. Quantum computing is usually expressed by a quantum circuit with its sequences of quantum gates. The article talks about the creation of an educational simulator for the study of quantum circuits with various gates, functions and transformation rules. This quantum simulator will help students learn the principles of quantum circuitry in their undergraduate and graduate courses. This tool will offer functions that allow you to build a quantum circuit, add and remove quantum gates, apply transformation rules, and observe the operation of a quantum circuit with its unitary matrix. By practicing with the quantum circuit simulator, students can strengthen their theoretical knowledge base about quantum computing and linear algebraic functions
Quantum computing offers significant speedup compared to classical computing, which has led to a growing interest among users in learning and applying quantum computing across various applications. However, quantum circuits, which are fundamental for implementing quantum algorithms, can be challenging for users to understand due to their underlying logic, such as the temporal evolution of quantum states and the effect of quantum amplitudes on the probability of basis quantum states. To fill this research gap, we propose QuantumEyes, an interactive visual analytics system to enhance the interpretability of quantum circuits through both global and local levels. For the global-level analysis, we present three coupled visualizations to delineate the changes of quantum states and the underlying reasons: a Probability Summary View to overview the probability evolution of quantum states; a State Evolution View to enable an in-depth analysis of the influence of quantum gates on the quantum states; a Gate Explanation View to show the individual qubit states and facilitate a better understanding of the effect of quantum gates. For the local-level analysis, we design a novel geometrical visualization dandelion chart to explicitly reveal how the quantum amplitudes affect the probability of the quantum state. We thoroughly evaluated QuantumEyes as well as the novel dandelion chart integrated into it through two case studies on different types of quantum algorithms and in-depth expert interviews with 12 domain experts. The results demonstrate the effectiveness and usability of our approach in enhancing the interpretability of quantum circuits.