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, Braulio Misael Villegas-Martínez, Jesus Escobedo-Alatorre, Sandra Gesing, N. Lozano-Crisóstomo, J. C. García-Melgarejo, Juan Carlos Sánchez Pérez, Eddie Nelson Palacios- Pérez, Omar Palillero-Sandoval
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 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.
The study delves into the revolutionary potential of quantum computing, exploring its foundational principles, software platforms, challenges, and future trajectories.The introduction highlights the profound impact anticipated in the software development realm due to quantum computing's revolutionary influence.The exploration of basic principles immerses into quantum mechanics, introducing key concepts like superposition, entanglement, and quantum parallelism, elucidating the unprecedented computational capabilities enabled by quantum bits.The subsequent section scrutinizes well-known quantum computing software platforms, offering insights into their functionalities.Platforms such as IBM Quantum Experience, D-Wave Leap, Microsoft Quantum Development Kit, Google Cirq and TensorFlow Quantum, Rigetti Forest, Qiskit, Amazon Braket, Xanadu PennyLane, and QuTiP are detailed, underscoring their pivotal role in bridging theoretical quantum principles with practical applications.The paper then tackles the challenges and future trajectories of quantum computing.Quantum decoherence, error correction, scalability, qubit stability, noise and interference, quantum software and algorithms, standardization, hybrid quantum-classical systems, quantum communication, materials science and engineering, education and workforce, and ethical and societal implications undergo critical examination.The study highlights the imperative need for collaborative efforts from academia, industry, and government to surmount these challenges and propel the progress of quantum computing.In conclusion, the study underscores the dynamic nature of the quantum computing landscape and the continuous requirement for ongoing research and technological advancements.The delineated challenges and future trajectories serve as a guide for researchers, developers, and policymakers to navigate the intricate realm of quantum computing, ensuring its responsible and impactful integration across diverse domains.
Quantum computing is being increasingly adopted for solving classically intractable problems across various domains. However, the availability of accessible and scalable software frameworks remains essential for practical experimentation and adoption. IBM's open-source quantum computing toolkit 'Qiskit' has become a key player in this space by offering tools for circuit design, simulation, hardware execution, and domain-specific applications. This survey provides a systematic review of how Qiskit has evolved and what it has contributed to several critical fields including cryptography and cybersecurity, image and signal processing, climate science and energy applications, and finance. We show how Qiskit facilitates hybrid classical-quantum workflows and enables the deployment of algorithms on physical quantum hardware through error mitigation and modular integration approaches. Our exploration covers several key applications, including quantum key distribution, climate simulation, and quantum-enhanced portfolio optimization, while providing practical insights into their implementation. This work also covers the framework's technical structure and current limitations associated with scalability and reproducibility. By bringing together developments that have been scattered across different areas, this work serves as a reference point for researchers and practitioners who want to understand or contribute to Qiskit-enabled quantum computing.
One of the greatest technological advancements in the last century lies in digital computer science. The idea of storing information and performing complicated calculations with the help of bits, i.e., 0 and 1. But due to a sudden surge in data, the classical computer system has been becoming weak in data processing. Quantum computers offer promising substantial speedup over classical computers for many applications. Quantum chip fabrication has made remarkable gains in recent years, with the number of qubits and fidelity growing. In general computing, a binary digit is the smallest unit of information or a bit. “In quantum computing, the term Qubit (Quantum Bit) serves the exact function of the term bit.” IBM Research released the IBMQ Experience in 2018, the first quantum computer that anyone can use and make accessible to a huge audience of different countries through cloud access. IBM also introduced the tool QISKIT (Quantum information software kit), which enables teachers, researchers, and developers to write coding and run their coding on quantum machines. It also includes different packages of quantum computing. In this paper, the author has discussed different steps to install qiskit. Mainly this paper focused on the “programming and application side of quantum computing.” Qiskit tools used in the python programming language. The “quantum circuits” are fabricated with the use of quantum gates and favorable algorithms with less execution time
Quantum entanglement has gained significant attention in recent years owing to its diverse application in quantum informatics. Its nonlocal nature makes it a crucial resource in quantum information, especially in quantum communication and networking. Entanglement plays a vital role in advancing quantum computation and technology.This study discusses the creation of entangled quantum states using photon pairs and the measurement of entanglement, utilizing the IBM Q Experience the first industrial initiative to develop universal quantum computers and provide widespread access to these technologies through cloud platforms. The introduction of the Qiskit tool has enabled researchers, educators, developers, and enthusiasts to write code and conduct experiments on quantum machines. In this study, we implemented and tested circuits on the IBM Q quantum computing platform using Qiskit to execute quantum computing programs such as the Bell Inequality, CHSH Inequality, and Five-Qubit GHZ States, with two successful programming presented, these tools, along with the associated Jupyter Notebook pages, serve as additional resources for users of the IBM Q Experience. We present the results which will help evaluate these prototypes’ performance. Where quantum computers are designed to process information units that go beyond mere abstract mathematical entities, as suggested by Shannon’s theory; instead, they represent real physical objects defined by one of the two fundamental physical theories—quantum mechanics.