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

IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

qWard: A Unified Toolkit for Pre- and Post-Runtime Quantum Circuit Metrics

Carmen Márquez, Daniel Sierra-Sosa, Kelly Garcés

Published Oct 28, 2025• 0 citations
Publisher

Abstract

As quantum computing (QC) matures towards practical applications, effective methods to evaluate quantum algorithms, circuits, and execution quality in quantum software are gaining more relevance. To apply such methods, a set of meaningful metrics must be defined, understood, calculated, and applied. In an effort to solve that problem, this paper categorizes quantum related metrics into pre-runtime (derived from the static analysis of circuits before execution) and post-runtime (derived from the analysis of execution results). It is worthwhile to mention that while popular quantum SDKs such as Qiskit, PennyLane, and Q\# offer some circuit characteristics, there is often a gap between these native capabilities and the broader set of insightful pre- and post-runtime metrics in the literature. This paper introduces$\boldsymbol{q}$Ward, a Python library developed to bridge this gap by offering an initial implementation for the Qiskit SDK. qWard provides a toolkit designed to compute and visualize key pre-runtime and post-runtime metrics, facilitating the analysis of quantum algorithms. To illustrate its application, an experiment using a bell state circuit is conducted, which includes different backend configurations within the simulator as well as its execution on the IBM “torino” quantum processor. It is important to note that the initial version of qWard is limited to the Qiskit SDK and the Qiskit AER simulator.

DOI
10.1109/chilecon66915.2025.11476435

Related papers

Establishing a set of visualizations for post-runtime infomation in Quantum Computers.

Jaime Andrés Torres Bermejo

This bachelor thesis proposes a visualization system for qWard, a library for the measurement and display of metrics for quantum circuits, as a way to help developers and researchers to understand the characteristics of a specific quantum circuit's pre and post-runtime metrics. While qWard itself already has the capacity of capturing and showing metrics, this system makes our display of such metrics more immediately readable. This system intends to help the benchmarking process for different quantum algorithms when you vary factors such as noise, backend, or the design of the circuit itself, allowing for easier and faster benchmarking during development on quantum solutions.

•Jul 12, 2025•0 citations

QSimBench: An Execution-Level Benchmark Suite for Quantum Software Engineering

Giuseppe Bisicchia, Alessandro Bocci, José Manuel García-Alonso +2 more

Reproducibility and comparability remain fundamental challenges in the empirical evaluation of Quantum Software Engineering (QSE) tools. These challenges arise from the intrinsic probabilistic behavior of quantum programs and the variability introduced by quantum hardware and simulator backends. While existing quantum benchmarks typically offer only static circuit definitions, they often leave researchers responsible for data generation and execution, hindering the creation of standardized and reproducible experiments. In this paper, we introduce QSimBench, a novel, open-source benchmark suite and accompanying Python library designed to address these limitations. QSimBench provides over 20 million precomputed shot-level execution traces:$\mathbf{2 0, 0 0 0}$measurements each, across$\mathbf{1 4}$quantum algorithms, multiple circuit sizes ($4-15$qubits), and six Qiskit-based backends including both ideal and noise-injected simulations. Each configuration includes not only detailed measurement histories but also the original circuit in OpenQASM, the backend's noise model, and configuration metadata, ensuring full transparency and enabling in-depth analysis. QSimBench empowers researchers to conduct scalable, reproducible, and statistically rigorous evaluations of QSE tools, such as those for monitoring, orchestration, and statistical post-processing, without the need for expensive or difficult-to-reproduce quantum executions. The benchmark is distributed with a lightweight Python API that facilitates fast data retrieval, flexible sampling strategies, and seamless integration into experimental pipelines. QSimBench is publicly available, aiming to foster collaborative and reproducible research within the quantum software community.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Aug 30, 2025•1 citation

QiliSDK: Toolkit for Digital, Analog, and Hybrid Quantum Workflows

Amir Azzam, Vyron Vasileiadis

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.

•Dec 2, 2025•0 citations

Unleashing quantum algorithms with Qinterpreter: bridging the gap between theory and practice across leading quantum computing platforms

Wilmer Contreras Sepúlveda, Ángel David Torres-Palencia, José Javier Sánchez Mondragón +8 more

Quantum computing is a rapidly emerging and promising field that has the potential to revolutionize numerous research domains, including drug design, network technologies and sustainable energy. Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms, namely IBM Qiskit, Amazon Braket, Cirq, PyQuil, and PennyLane. These libraries allow for quantum simulations on classical computers and facilitate program execution on corresponding quantum hardware, e.g., Qiskit programs on IBM quantum computers. While all platforms have some differences, the main concepts are the same. QInterpreter is a tool embedded in the Quantum Science Gateway QubitHub using Jupyter Notebooks that translates seamlessly programs from one library to the other and visualizes the results. It combines the five well-known quantum libraries: into a unified framework. Designed as an educational tool for beginners, Qinterpreter enables the development and execution of quantum circuits across various platforms in a straightforward way. The work highlights the versatility and accessibility of Qinterpreter in quantum programming and underscores our ultimate goal of pervading Quantum Computing through younger, less specialized, and diverse cultural and national communities.

arXiv:2310.07173•Oct 11, 2023•0 citations

Unleashing quantum algorithms with Qinterpreter: bridging the gap between theory and practice across leading quantum computing platforms

Wilmer Contreras Sepúlveda, Braulio Misael Villegas-Martínez, Sandra Gesing +10 more

Quantum computing is a rapidly emerging and promising field with the potential to transform various research domains including drug design, network technologies, and sustainable energy solutions. Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms, namely IBM Qiskit, Amazon Braket, Cirq, PyQuil, and PennyLane. These libraries enable quantum simulations on classical computers and execution on corresponding quantum hardware, such as Qiskit programs on IBM quantum computers. Despite the variations among these platforms, the core concepts remain the same. One notable challenge is the absence of a Python-based quantum interpreter to connect these five frameworks, a gap that remains to be fully addressed. In response, our work introduces a tool called Qinterpreter, accessible through a user-friendly web interface, the Quantum Science Gateway QubitHub, which operates alongside Jupyter Notebooks. Built using the Python Object-Oriented Programming System, Qinterpreter unifies the five well-known quantum libraries into a single framework. Designed as an educational tool for students and researchers entering the quantum domain, Qinterpreter enables the straightforward development and execution of quantum circuits across such platforms. This work highlights the quantum programming versatility and accessibility of Qinterpreter and underscores our ultimate goal of pervading Quantum Computing through younger, less specialized, and diverse cultural and national communities.

PeerJ Computer Science•Oct 15, 2024•1 citation

Roadmap for Simulating Quantum Circuits Utilising IBM’s Qiskit Library: Programming Approach

Yousef M. Jaradat, Mohammad Ahmad Alia, Mohammad Z. Masoud +3 more

This paper explains the roadmap for running quantum circuit programs based on the Qiskit library in quantum simulators as well as real cloud-based IBM quantum computers. Qiskit is a free and open-source software development platform based on the Python programming language that is used in quantum programming. Qiskit acts as a link between quantum computing’s theoretical foundations and the practical aspects of programming and experimentation. It also allows users to experiment with and develop quantum algorithms, as well as simulate and execute them on simulators and real-world cloud-based quantum devices. It also simplifies the quantum programming process and allows a diverse range of people to participate in the exciting world of quantum computing. The paper, on the other hand, provides the mathematical foundation for analyzing quantum circuits and algorithms using linear algebra principles, as they provide the tools needed to describe and manipulate quantum states and operations. Furthermore, the paper shows quantum circuit design and implementation using real Qiskit codes.

The Eurasia Proceedings of Science Technology Engineering and Mathematics•Dec 30, 2023•6 citations
As quantum computing (QC) matures towards practical applications, effective methods to evaluate quantum algorithms, circuits, and execution quality in quantum software are gaining more relevance. To apply such methods, a set of meaningful metrics must be defined, understood, calculated, and applied. In an effort to solve that problem, this paper categorizes quantum related metrics into pre-runtime (derived from the static analysis of circuits before execution) and post-runtime (derived from the analysis of execution results). It is worthwhile to mention that while popular quantum SDKs such as Qiskit, PennyLane, and Q# offer some circuit characteristics, there is often a gap between these native capabilities and the broader set of insightful pre- and post-runtime metrics in the literature. This paper introducesq\boldsymbol{q}qWard, a Python library developed to bridge this gap by offering an initial implementation for the Qiskit SDK. qWard provides a toolkit designed to compute and visualize key pre-runtime and post-runtime metrics, facilitating the analysis of quantum algorithms. To illustrate its application, an experiment using a bell state circuit is conducted, which includes different backend configurations within the simulator as well as its execution on the IBM “torino” quantum processor. It is important to note that the initial version of qWard is limited to the Qiskit SDK and the Qiskit AER simulator.
This bachelor thesis proposes a visualization system for qWard, a library for the measurement and display of metrics for quantum circuits, as a way to help developers and researchers to understand the characteristics of a specific quantum circuit's pre and post-runtime metrics. While qWard itself already has the capacity of capturing and showing metrics, this system makes our display of such metrics more immediately readable. This system intends to help the benchmarking process for different quantum algorithms when you vary factors such as noise, backend, or the design of the circuit itself, allowing for easier and faster benchmarking during development on quantum solutions.
Reproducibility and comparability remain fundamental challenges in the empirical evaluation of Quantum Software Engineering (QSE) tools. These challenges arise from the intrinsic probabilistic behavior of quantum programs and the variability introduced by quantum hardware and simulator backends. While existing quantum benchmarks typically offer only static circuit definitions, they often leave researchers responsible for data generation and execution, hindering the creation of standardized and reproducible experiments. In this paper, we introduce QSimBench, a novel, open-source benchmark suite and accompanying Python library designed to address these limitations. QSimBench provides over 20 million precomputed shot-level execution traces:20,000\mathbf{2 0, 0 0 0}20,000measurements each, across14\mathbf{1 4}14quantum algorithms, multiple circuit sizes (4−154-154−15qubits), and six Qiskit-based backends including both ideal and noise-injected simulations. Each configuration includes not only detailed measurement histories but also the original circuit in OpenQASM, the backend's noise model, and configuration metadata, ensuring full transparency and enabling in-depth analysis. QSimBench empowers researchers to conduct scalable, reproducible, and statistically rigorous evaluations of QSE tools, such as those for monitoring, orchestration, and statistical post-processing, without the need for expensive or difficult-to-reproduce quantum executions. The benchmark is distributed with a lightweight Python API that facilitates fast data retrieval, flexible sampling strategies, and seamless integration into experimental pipelines. QSimBench is publicly available, aiming to foster collaborative and reproducible research within the quantum software community.
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.
Quantum computing is a rapidly emerging and promising field that has the potential to revolutionize numerous research domains, including drug design, network technologies and sustainable energy. Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms, namely IBM Qiskit, Amazon Braket, Cirq, PyQuil, and PennyLane. These libraries allow for quantum simulations on classical computers and facilitate program execution on corresponding quantum hardware, e.g., Qiskit programs on IBM quantum computers. While all platforms have some differences, the main concepts are the same. QInterpreter is a tool embedded in the Quantum Science Gateway QubitHub using Jupyter Notebooks that translates seamlessly programs from one library to the other and visualizes the results. It combines the five well-known quantum libraries: into a unified framework. Designed as an educational tool for beginners, Qinterpreter enables the development and execution of quantum circuits across various platforms in a straightforward way. The work highlights the versatility and accessibility of Qinterpreter in quantum programming and underscores our ultimate goal of pervading Quantum Computing through younger, less specialized, and diverse cultural and national communities.
Quantum computing is a rapidly emerging and promising field with the potential to transform various research domains including drug design, network technologies, and sustainable energy solutions. Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms, namely IBM Qiskit, Amazon Braket, Cirq, PyQuil, and PennyLane. These libraries enable quantum simulations on classical computers and execution on corresponding quantum hardware, such as Qiskit programs on IBM quantum computers. Despite the variations among these platforms, the core concepts remain the same. One notable challenge is the absence of a Python-based quantum interpreter to connect these five frameworks, a gap that remains to be fully addressed. In response, our work introduces a tool called Qinterpreter, accessible through a user-friendly web interface, the Quantum Science Gateway QubitHub, which operates alongside Jupyter Notebooks. Built using the Python Object-Oriented Programming System, Qinterpreter unifies the five well-known quantum libraries into a single framework. Designed as an educational tool for students and researchers entering the quantum domain, Qinterpreter enables the straightforward development and execution of quantum circuits across such platforms. This work highlights the quantum programming versatility and accessibility of Qinterpreter and underscores our ultimate goal of pervading Quantum Computing through younger, less specialized, and diverse cultural and national communities.
This paper explains the roadmap for running quantum circuit programs based on the Qiskit library in quantum simulators as well as real cloud-based IBM quantum computers. Qiskit is a free and open-source software development platform based on the Python programming language that is used in quantum programming. Qiskit acts as a link between quantum computing’s theoretical foundations and the practical aspects of programming and experimentation. It also allows users to experiment with and develop quantum algorithms, as well as simulate and execute them on simulators and real-world cloud-based quantum devices. It also simplifies the quantum programming process and allows a diverse range of people to participate in the exciting world of quantum computing. The paper, on the other hand, provides the mathematical foundation for analyzing quantum circuits and algorithms using linear algebra principles, as they provide the tools needed to describe and manipulate quantum states and operations. Furthermore, the paper shows quantum circuit design and implementation using real Qiskit codes.