A Systematic Review Of Strategic Approaches And Applications In Quantu

Quantum computing has rapidly evolved into a transformative discipline with the potential to solve complex problems beyond the capabilities of classical systems. It’s emerging applications extend across critical domains including drug discovery, logistics optimization, cryptography, healthcare, finance, and secure communications. With both open-source and commercial quantum simulators now available, researchers and enterprises are actively exploring quantum solutions. Governments and private sectors worldwide have initiated significant funding programs to accelerate quantum research and innovation. This review presents a comprehensive analysis of developments in quantum hardware, software ecosystems, and industrial applications in recent years. It highlights the growth of quantum processing capabilities, programming frameworks, and the expanding commercial interest in quantum-enabled services.

Despite these advancements, key challenges remain–including qubit stability, error correction, interoperability, and limited real-world scalability. By systematically examining the current landscape, this paper outlines major research milestones, identifies existing technological gaps, and discusses future directions that could lead to practical, large-scale quantum computing systems. This is a preview of subscription content, log in via an institution to check access. Price excludes VAT (USA) Tax calculation will be finalised during checkout. Aaronson, S.: The complexity of quantum states and transformations: from quantum money to black holes. arXiv preprint arXiv:1607.05256 (2016)

Abraham, H., et al.: Qiskit: an open-source framework for quantum computing. Zenodo (2019). https://doi.org/10.5281/zenodo.2562110 A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2026 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. How, M.-L.; Cheah, S.-M.

Forging the Future: Strategic Approaches to Quantum AI Integration for Industry Transformation. AI 2024, 5, 290-323. https://doi.org/10.3390/ai5010015 How M-L, Cheah S-M. Forging the Future: Strategic Approaches to Quantum AI Integration for Industry Transformation. AI.

2024; 5(1):290-323. https://doi.org/10.3390/ai5010015 How, Meng-Leong, and Sin-Mei Cheah. 2024. "Forging the Future: Strategic Approaches to Quantum AI Integration for Industry Transformation" AI 5, no. 1: 290-323.

https://doi.org/10.3390/ai5010015 How, M.-L., & Cheah, S.-M. (2024). Forging the Future: Strategic Approaches to Quantum AI Integration for Industry Transformation. AI, 5(1), 290-323. https://doi.org/10.3390/ai5010015

Nature Reviews Physics volume 5, pages 141–156 (2023)Cite this article The future development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation and sensing. This poses severe challenges in the efficient control, calibration and validation of quantum states and their dynamics. Although the full simulation of large-scale quantum systems may only be possible on a quantum computer, classical characterization and optimization methods still play an important role. Here, we review different approaches that use classical post-processing techniques, possibly combined with adaptive optimization, to learn quantum systems, their correlation properties, dynamics and interaction with the environment. We discuss theoretical proposals and successful implementations across different multiple-qubit architectures such as spin qubits, trapped ions, photonic and atomic systems, and superconducting circuits.

This Review provides a brief background of key concepts recurring across many of these approaches with special emphasis on the Bayesian formalism and neural networks. The complexity of quantum systems increases exponentially with their size, but in many practical contexts there are assumptions (such as low rank, sparsity, or a specific type of expected dynamics) that enable classical algorithms... Bayesian inference provides a robust approach to learning models for quantum systems, while preserving physical intuition about the processes involved. Neural networks enable the characterization of complex systems, at the expense of physical intuition.

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