Qiskit Toolkit Enables Hybrid Classical-Quantum Algorithms for Diverse Scientific Applications

Computing is rapidly advancing as a potential solution to problems currently beyond the reach of conventional computers, but realising this potential depends on accessible and scalable software. Param Pathak from QuantumAI Lab, Fractal Analytics, K Tarakeshwar and Syed Sufiyan Ali from the Department of Computer Science at Birla Institute of Technology and Science (BITS) Pilani, Dubai Campus, and colleagues, present a comprehensive review of Qiskit, IBM’s open-source toolkit, charting its evolution and impact across diverse fields. The team demonstrates how Qiskit empowers researchers to design, simulate and execute algorithms, bridging the gap between theoretical possibilities and practical implementation on real computing hardware. This survey highlights Qiskit’s contributions to areas such as cybersecurity, climate science, and finance, showcasing its ability to facilitate hybrid classical- workflows and address challenges in scalability and reproducibility, ultimately serving as a valuable resource for both newcomers and established experts in the field.

Quantum Finance Applications and Portfolio Optimisation

Research in quantum computing increasingly focuses on financial applications, alongside advancements in other scientific and engineering domains. A dominant theme is the exploration of how quantum computers can improve portfolio optimization, a crucial task in finance, by employing algorithms like QAOA, VQE, and HHL, and modelling problems using QUBO frameworks. These algorithms are being tested on actual quantum hardware and compared to traditional methods to assess their potential advantages. Quantum computing is also being investigated for risk management, specifically for more accurate Value at Risk calculations and improved credit risk analysis, and for identifying fraudulent transactions using quantum machine learning techniques.

Beyond finance, quantum computing offers potential solutions to challenges in diverse fields. In energy, researchers are exploring how quantum algorithms can optimize the integration of renewable energy sources into smart grids, improve energy management and load scheduling, and solve complex unit commitment problems in power systems. Materials science benefits from quantum algorithms designed to predict protein structures, while aerospace and environmental science are exploring applications in asteroid classification, climate modelling, and heat plume prediction using quantum convolutional neural networks. Optimization problems, such as designing efficient grid infrastructure, and data analysis tasks, like processing multispectral Earth observation data, are also benefiting from quantum approaches.

Several quantum algorithms and techniques underpin these advancements, including QAOA, VQE, HHL, and QUBO models, alongside quantum machine learning. Researchers are also focusing on developing resource-efficient algorithms that minimize the number of qubits and circuit depth required for computation. A key trend is the use of hybrid approaches, combining the strengths of both classical and quantum computing, particularly for near-term quantum devices, acknowledging the limitations of current hardware. Industry collaborations, such as those between E. ON and IBM Quantum, and Hyundai and IonQ, demonstrate growing interest in the practical application of quantum computing.

Qiskit’s Impact on Quantum Application Development

Qiskit, an open-source software toolkit, is playing a vital role in advancing quantum computing by bridging the gap between theoretical algorithms and practical implementation on real quantum hardware. Researchers are systematically reviewing applications across cryptography, image processing, climate science, and finance to assess Qiskit’s effectiveness. The toolkit’s modular architecture, comprising components like Terra, Aer, and Ignis, facilitates complex quantum workflows by allowing researchers to build and test algorithms without needing detailed knowledge of the underlying hardware. This modularity streamlines the development process and encourages experimentation.

Qiskit’s Ignis component addresses the challenges of noisy quantum systems through error mitigation techniques, crucial for obtaining reliable results from current hardware. The toolkit also supports hybrid quantum-classical algorithms, such as the Variational Quantum Eigensolver and the Quantum Approximate Optimization Algorithm, which combine the strengths of both types of computers. By examining implementations of these algorithms across different domains, researchers demonstrate how Qiskit enables practical applications even with the limitations of today’s quantum hardware. The integration of Qiskit with IBM’s quantum hardware, ranging from small prototypes to larger processors, allows for the testing and validation of algorithms in a physical environment, providing a complete workflow for developing and deploying quantum solutions.

Qiskit Advances Accessible Quantum Computing Tools

Qiskit, developed by IBM, is significantly advancing the field of computing by providing accessible tools for designing, simulating, and running quantum algorithms. The framework’s modular architecture abstracts away the complexities of quantum hardware, enabling researchers and developers to focus on algorithm creation and experimentation. At its core, Qiskit comprises Terra, Aer, and Ignis, responsible for circuit design, high-performance simulation, and error mitigation respectively, alongside specialized libraries tailored to specific applications. This layered approach allows users to seamlessly transition from theoretical concepts to practical implementation, whether utilizing simulators or accessing actual quantum hardware via the IBM Quantum cloud platform.

Qiskit’s simulation capabilities are particularly noteworthy, offering both ideal and noisy simulations that accurately reflect the behavior of real quantum devices. The Ignis component actively addresses the limitations of current, noisy intermediate-scale quantum (NISQ) devices by providing tools to characterize, measure, and reduce errors, extending qubit coherence and improving result reliability. Beyond these core components, Qiskit offers specialized libraries for fields like finance, machine learning, and materials science, simplifying complex tasks and accelerating research. The framework is driving advancements in cryptography and cybersecurity, providing tools to develop quantum-resistant protocols and assess the vulnerability of existing systems, and exploring quantum key distribution and quantum random number generation.

Beyond security, Qiskit is also proving valuable in areas like climate modelling and financial optimization. Researchers are leveraging the framework to simulate complex molecular interactions relevant to climate science and to develop more efficient algorithms for portfolio optimization and risk assessment. The ability to combine quantum workflows with domain-specific libraries allows for experimental execution of theoretical ideas, accelerating the pace of discovery and innovation. The framework’s comprehensive documentation and active community further contribute to its accessibility, making quantum computing available to a wider range of researchers and developers.

Qiskit Empowers Practical Quantum Computing Applications

This work presents a comprehensive survey of Qiskit, detailing its role in enabling practical quantum computing across diverse fields. The analysis reveals how Qiskit’s modular architecture, including components like Terra, Aer, and Ignis, supports the development and implementation of quantum algorithms for applications in cryptography, image processing, climate science, energy, and finance. By categorising existing research and examining technical workflows, the study demonstrates how Qiskit facilitates the transition from theoretical quantum models to executable circuits on both simulators and real quantum hardware, utilising hybrid quantum-classical approaches. The survey consolidates the breadth of Qiskit’s impact, providing a valuable reference point for researchers and practitioners. The authors acknowledge limitations related to scalability and reproducibility within the framework, areas requiring further development. This work aims to offer a comprehensive overview of Qiskit’s current capabilities and its significance in advancing quantum computing research, development, and education, ultimately contributing to a better understanding of the field’s potential.