We introduce an open-source web-based platform that integrated multiple methods for visualizing Shor's algorithm. We mainly focus on three different approaches which are widely used in the field of visualizing qubit and quantum algorithms. These methods include Bloch sphere, quantum circuit and probability distribution map. We combine these geometrical methods and abstract the level of quantum circuit in order to introduce the well-known Shor's algorithm more explicitly. Our platform provides a direct and comprehensible perspective for better understanding the basic principles of quantum computation and how the features of quantum algorithms reduce the time complexity of certain problems. It also provides an interactive way for users to easily test the Shor's factoring algorithm. With further improvement and development, potential capacity can be proved in the field of visualization of quantum computation.
We introduce an open-source web-based platform that integrated multiple methods for visualizing Shor's algorithm. We mainly focus on three different approaches which are widely used in the field of visualizing qubit and quantum algorithms. These methods include Bloch sphere, quantum circuit and probability distribution map. We combine these geometrical methods and abstract the level of quantum circuit in order to introduce the well-known Shor's algorithm more explicitly. Our platform provides a direct and comprehensible perspective for better understanding the basic principles of quantum computation and how the features of quantum algorithms reduce the time complexity of certain problems. It also provides an interactive way for users to easily test the Shor's factoring algorithm. With further improvement and development, potential capacity can be proved in the field of visualization of quantum computation.
Quantum algorithms which are performed on quantum computers are hardly developed, because the principle of quantum computers are totally different from classical computers. We would like to discuss the way of visualization to assist understanding quantum algorithms. As the first step, we have developed visualization system of the Bloch sphere, which is the popular way to show the status of one quantum bit. In the system, we can see the Bloch sphere as 3DCG and interact with it. Next, we discuss the problems of visualizing multiple quantum bits and quantum algorithms.
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.
Quantum state visualisation remains a significant challenge in quantum computing education and research due to the abstract mathematical properties of quantum mechanics. This paper presents a new Quantum State Visualiser, an interactive web app that tries to bridge the gap between theoretical quantum mechanics and real-world understanding. The proposed solution operates with multi-qubit quantum circuits, employing partial trace operations to derive single-qubit reduced density matrices, which are then depicted as dynamic 3D Bloch sphere representations. The tool helps you understand abstract ideas like superposition, entanglement, and quantum interference by turning hard math into easy-to-understand pictures. This is useful for students, teachers, researchers, and people who work on quantum computers.The prototype uses Qiskit for quantum Traditional quantum co simulation, Python for computational backends, and modern web technologies for interactive visualisation. The system can accurately show quantum states in real time, which means that circuit changes get instant visual feedback. The new thing is that it combines quantum simulation in real time with 3D visualisation that you can interact with. This creates a fun place to learn that helps people understand quantum phenomena faster. This tool fills a big gap in education and could be used in schools, to fix problems with quantum algorithms, and to make quantum circuits work better. In the future, it will be possible to work with bigger qubit systems, connect it to quantum hardware platforms, and add features for augmented reality visualisation
Shor’s algorithm stands as a breakthrough in quantum computing due to its potential to factor large integers exponentially quicker than classical algorithms. However, implementing and evaluating this algorithm on real quantum computer hardware remains exciting due to qubit limitations, gate noise, and hardware constraints. This research presents a comprehensive performance evaluation of Shor’s algorithm using simulated quantum backends provided by Qiskit. A flexible and generic implementation is proposed, allowing dynamic input of integers to be factored, with randomized co-prime selection and automated circuit generation. The algorithm is tested on various semiprime numbers, such as 15, 21, and 35, using IBM’s Aer simulator. A major contribution of this work is the circuit-level analysis conducted both before and after transpilation. Metrics such as gate counts, circuit depth, and simulator runtime are extracted to assess scalability and resource requirements. High-resolution plots of the pre-transpiled circuits are saved to visualize algorithmic complexity, while post-transpilation metrics inform future quantum hardware feasibility. The output measurement distributions are analyzed to estimate periodicity and derive correct factors. The proposed implementation is compared with existing fixed-instance Shor demonstrations to highlight its flexibility and extensibility. Experimental results show consistent success in factor retrieval and provide valuable insight into circuit growth and complexity under realistic constraints. This analysis lays the groundwork for future adaptation to NISQ hardware and contributes to understanding Shor’s algorithm from both computational and architectural perspectives.
Quantum computing introduces new paradigms for solving complex computational problems. In this paper, we present the development of a full-stack quantum computing simulator that enables users to design, simulate, and visualize quantum circuits, while also integrating classical NP problem solvers. Built using Python, Qiskit, React, and Flask/FastAPI, the simulator supports up to 20 qubits and includes features like quantum gate editing, Bloch sphere visualization, and classical algorithms for 3-SAT and Knapsack. The project aims to bridge accessibility and functionality in quantum computing education and experimentation.
This article presents a detailed implementation of Shor’s quantum factoring algorithm, which served as the cornerstone for a pilot project at Jamk University of Applied Sciences. The primary goal was to leverage this significant algorithm to establish and validate a reusable quantum simulation environment on a high-performance computing (HPC) platform. We report the successful factorization of the integer 15, executed on the Finnish CSC’s MAHTI supercomputer using a 9-qubit quantum circuit developed in the Qiskit framework. This work provides a comprehensive overview of the principles behind Shor’s algorithm and a methodical breakdown of its simulation. Furthermore, it documents the practical software challenges faced during the environment’s configuration, focusing on library versioning and dependencies. The purpose of this work is to provide a systematic introduction to implement Shor’s algorithm at computer science or applied mathematics classes in institutions of higher education such as universities of applied sciences. The successful factorization provides an initial validation of the simulation workflow for this benchmark case, while the detailed account of implementing Shor’s algorithm offers a practical guide and valuable insights for future quantum computing initiatives at educational institutions with a focus on computer science or scientific computing.