Abstract Quantum circuits constructed from Josephson junctions and superconducting electronics are key to many quantum computing and quantum optics applications. Designing these circuits involves calculating the Hamiltonian describing their quantum behavior. Here we present QuCAT, or ‘Quantum Circuit Analyzer Tool’, an open-source framework to help in this task. This open-source Python library features an intuitive graphical or programmatical interface to create circuits, the ability to compute their Hamiltonian, and a set of complimentary functionalities such as calculating dissipation rates or visualizing current flow in the circuit.
Abstract Quantum circuits constructed from Josephson junctions and superconducting electronics are key to many quantum computing and quantum optics applications. Designing these circuits involves calculating the Hamiltonian describing their quantum behavior. Here we present QuCAT, or ‘Quantum Circuit Analyzer Tool’, an open-source framework to help in this task. This open-source Python library features an intuitive graphical or programmatical interface to create circuits, the ability to compute their Hamiltonian, and a set of complimentary functionalities such as calculating dissipation rates or visualizing current flow in the circuit.
Quantum computing (QC) is a rapidly growing field linking the disciplines of mathematics, physics, and computer science. CS students and software developers are a natural talent pool for the future QC workforce, but existing QC simulation and visualization tools are not designed with their background in mind. We present QC.py, a full-featured, open-source Python simulation and visualization tool for quantum circuits, with a feature set tailored to the background of CS students and software practitioners.
Quantum computing takes significant place among the spheres of modern information technology. Quantum computation is usually expressed by the quantum circuit with its sequences of quantum gates. One of the practical applications of quantum computation is to use it in a web-based tool for variety of purposes. Thereby, this project aimed to create an educational simulator for studying quantum circuits with different gates, functions, and transformation rules. The tool would help students to learn the principles of quantum circuit in their courses during undergraduate study. The application was created on the Python based Django framework with the help of Python library packages especially Quantum Toolbox in Python (QuTiP) package. Major steps of simulator implementation included the creation of functions able to build a quantum circuit, to add and remove of quantum gates, apply transformation rules. The challenge appeared around the frontend functions, particularly with the Drag and Drop feature. The result of entire work on this project ended up in a web-based simulator applicable for educational purposes, which allows investigation of quantum circuit work, and quantum gates influence on the quantum circuit unitary matrix
Abstract We introduce CircuitQ, an open-source toolbox for the analysis of superconducting circuits implemented in Python. It features the automated construction of a symbolic Hamiltonian of the input circuit and a dynamic numerical representation of the Hamiltonian with a variable basis choice. The software implementation is capable of choosing the basis in a fully automated fashion based on the potential energy landscape. Additional features include the estimation of the T 1 lifetimes of the circuit states under various noise mechanisms. We review previously established circuit quantization methods and formulate them in a way that facilitates the software implementation. The toolbox is then showcased by applying it to practically relevant qubit circuits and comparing it to specialized circuit solvers. Our circuit quantization is applicable to circuit inputs from a large design space, and the software is open-sourced. We thereby add an important resource for the design of new quantum circuits for quantum information processing applications.
We present Quokka#, a versatile, open-source Python library for quantum circuit analysis. Quokka# reduces various simulation, verification, and synthesis tasks to weighted model counting (#SAT). It supports universal quantum circuits and a wide variety of gates. Quokka# provides multiple encodings based on different algebraic bases and equivalence-checking methods, enabling key performance trade-offs. Moreover, the new version of Quokka# adds approximate equivalence checking, which is crucial in its synthesis algorithms, since it enables translation between arbitrary gate sets. Its synthesis engine is depth-optimal, making it well-suited to real-world quantum computing. This paper demonstrates the design, extensibility, and use of Quokka#.
With quantum computers promising advantages even in the near-term NISQ era, there is a lively community that develops software and toolkits for the design of corresponding quantum circuits. Although the underlying problems are different, expertise from the design automation community, which developed sophisticated design solutions for the conventional realm in the past decades, can help here. In this respect, decision diagrams provide a promising foundation for tackling many design tasks such as simulation, synthesis, and verification of quantum circuits. 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 this work, we first review the concepts of how decision diagrams can be employed, e.g., for the simulation and verification of quantum circuits. Afterwards, in an effort to make decision diagrams for quantum computing more accessible, we then present a visualization tool for quantum decision diagrams, which allows users to explore the behavior of decision diagrams in the design tasks mentioned above. Finally, we present decision diagram-based tools for simulation and verification of quantum circuits using the methods discussed above as part of the open-source Munich Quantum Toolkit (MQT)—a set of tools for quantum computing developed at the Technical University of Munich and the Johannes Kepler University Linz and released under the MIT license. More information about the corresponding tools is available at https://github.com/cda-tum/ddsim . By this, we provide an introduction of the concepts and tools for potential users who would like to work with them as well as potential developers aiming to extend them.
QiliSDK is an open-source Python framework for designing and executing digital, analog, and hybrid quantum algorithms. Its modular structure unifies circuit-based and Hamiltonian-based workflows within a single API. It provides high-level abstractions for gates, circuits, Hamiltonians, and optimizers, while remaining fully backend-agnostic: seamlessly switch between CPU, GPU, or QPU execution. The SDK simplifies quantum development through expressive syntax for Pauli algebra, variational programs, and built-in tools for visualization, serialization, and result analysis.