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Quantum computing (QC) represents a paradigm shift in computational power, offering unique capabilities for addressing complex problems that are infeasible for classical computers. This review paper provides a detailed account of the current state of QC, with a particular focus on its applications within medicine. It explores fundamental concepts such as qubits, superposition, and entanglement, as well as the evolution of QC from theoretical foundations to practical advancements. The paper covers significant milestones where QC has intersected with medical research, including breakthroughs in drug discovery, molecular modeling, genomics, and medical diagnostics. Additionally, key quantum techniques such as quantum algorithms, quantum machine learning (QML), and quantum-enhanced imaging are explained, highlighting their relevance in healthcare. The paper also addresses challenges in the field, including hardware limitations, scalability, and integration within clinical environments. Looking forward, the paper discusses the potential for quantum-classical hybrid systems and emerging innovations in quantum hardware, suggesting how these advancements may accelerate the adoption of QC in medical research and clinical practice. By synthesizing reliable knowledge and presenting it through a comprehensive lens, this paper serves as a valuable reference for researchers interested in the transformative potential of QC in medicine.
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http://dx.doi.org/10.3390/medsci12040067 | DOI Listing |
Nature
September 2025
Department of Physics, Harvard University, Cambridge, MA, USA.
Quantum simulations of many-body systems are among the most promising applications of quantum computers. In particular, models based on strongly correlated fermions are central to our understanding of quantum chemistry and materials problems, and can lead to exotic, topological phases of matter. However, owing to the non-local nature of fermions, such models are challenging to simulate with qubit devices.
View Article and Find Full Text PDFAcc Chem Res
September 2025
Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, Ave. Ferrocarril San Rafael Atlixco 186, Col. Leyes de Reforma 1A sección, Alcaldía Iztapalapa, 09310 Mexico City, Mexico.
ConspectusWhat does the word antioxidant mean? Antioxidants are supposed to be nontoxic, versatile molecules capable of counteracting the damaging effects of oxidative stress (OS). Thus, when evaluating a candidate molecule as an antioxidant, several aspects should be considered. Antioxidants are more than free radical scavengers.
View Article and Find Full Text PDFForensic Sci Int
September 2025
Department of Chemistry, Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, Ribeirão Preto, São Paulo 14040-091, Brazil; Instituto Nacional de Ciência e Tecnologia - Ciências Forenses (INCT Forense), Department of Chemistry, Faculty of Philosophy, Sciences and Letters of Ribeirão P
New psychoactive substances (NPS) present significant challenges for law enforcement and public health due to their rapid emergence and structural diversity, often outpacing the development of traditional analytical methods. This review explores using computational chemistry, particularly density functional theory (DFT), to obtain infrared spectra. This combination to characterize NPS began in the 2010s and has gained momentum across all continents in recent years.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
View Article and Find Full Text PDFPhys Rev Lett
August 2025
Southern University of Science and Technology, Department of Physics, State Key Laboratory of Quantum Functional Materials, and Guangdong Basic Research Center of Excellence for Quantum Science, Shenzhen 518055, China.
Quantum computing is expected to provide an exponential speedup in machine learning. However, optimizing the data loading process, commonly referred to as "quantum data embedding," to maximize classification performance remains a critical challenge. In this Letter, we propose a neural quantum embedding (NQE) technique based on deterministic quantum computation with one qubit (DQC1).
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