Quantum Science Beyond the Hype: Facts, Myths, and Realistic Progress in Physics, Chemistry, and Computing

Quantum science underpins many of the most significant advances in modern physics, chemistry, and information technology. Quantum physics provides the fundamental laws governing matter and energy at microscopic scales, quantum chemistry applies these principles to explain molecular structure, bonding, and reactivity, and quantum computing seeks to exploit quantum phenomena such as superposition and entanglement to enable new computational paradigms. Despite their long-standing theoretical foundations and experimental successes, all three domains remain surrounded by persistent misconceptions, often amplified by abstract formalisms, popular science narratives, and rapid technological developments. This contemporary review critically examines the established facts and common myths associated with quantum physics, quantum chemistry, and quantum computing. Emphasis is placed on experimentally verified principles, computational feasibility, and realistic assessments of current capabilities. Recent advances reported between 2020 and 2025 are reviewed to highlight genuine progress in areas such as quantum simulations, molecular modeling, and noisy intermediate-scale quantum (NISQ) devices, while also addressing their practical limitations. By distinguishing validated achievements from speculative claims, this review provides a balanced, evidence-based perspective on modern quantum science. The article aims to clarify enduring misconceptions, promote conceptual clarity across disciplines, and outline realistic future directions for research and applications in quantum technologies.

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Publication Details

Journal
International Journal of Computational and Theoretical Chemistry
Published
2026-02-25
DOI
https://doi.org/10.11648/j.ijctc.20261401.11
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Quantum Science Beyond the Hype: Facts, Myths, and Realistic Progress in Physics, Chemistry, and Computing

Ravuri Krishna
International Journal of Computational and Theoretical Chemistry
Machine Learning in Materials Science
article

Quantum Science Beyond the Hype: Facts, Myths, and Realistic Progress in Physics, Chemistry, and Computing

Ravuri Krishna
article en

Abstract

Quantum science underpins many of the most significant advances in modern physics, chemistry, and information technology. Quantum physics provides the fundamental laws governing matter and energy at microscopic scales, quantum chemistry applies these principles to explain molecular structure, bonding, and reactivity, and quantum computing seeks to exploit quantum phenomena such as superposition and entanglement to enable new computational paradigms. Despite their long-standing theoretical foundations and experimental successes, all three domains remain surrounded by persistent misconceptions, often amplified by abstract formalisms, popular science narratives, and rapid technological developments. This contemporary review critically examines the established facts and common myths associated with quantum physics, quantum chemistry, and quantum computing. Emphasis is placed on experimentally verified principles, computational feasibility, and realistic assessments of current capabilities. Recent advances reported between 2020 and 2025 are reviewed to highlight genuine progress in areas such as quantum simulations, molecular modeling, and noisy intermediate-scale quantum (NISQ) devices, while also addressing their practical limitations. By distinguishing validated achievements from speculative claims, this review provides a balanced, evidence-based perspective on modern quantum science. The article aims to clarify enduring misconceptions, promote conceptual clarity across disciplines, and outline realistic future directions for research and applications in quantum technologies.

International Journal of Computational and Theoretical ChemistryVol. 14(1)
Institute of Engineering (NP)
Openalex Percentile: Top 42%
Machine Learning in Materials Science
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