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BACKGROUND: African swine fever virus (ASFV) and porcine epidemic diarrhea virus (PEDV) differ in viral biology and cellular tropism, yet both pathogens suppress macrophage-mediated immune responses in pigs. OBJECTIVE: To identify a conserved macrophage suppression module shared by ASFV and PEDV and evaluate quantum computing as an independent framework for biological network validation. METHODS: Integrated analysis of publicly available GEO datasets (GSE231435 for ASFV and GSE306895) identified 471 shared downregulated genes. A network- and multi-omics-informed 20-gene core was selected and encoded as a 20-qubit modularity-based Quadratic Unconstrained Binary Optimization (QUBO) problem. Community detection was benchmarked using the Quantum Approximate Optimization Algorithm (QAOA) on both the IBM Quantum Aer simulator and the 156-qubit IBM Fez (Heron r2) quantum processor and compared with brute-force enumeration and simulated annealing. RESULTS: A conserved macrophage suppression module shared by ASFV and PEDV was identified. For the STRING protein-protein interaction network, QAOA at circuit depth p = 3 reproduced the brute-force optimum with an approximation ratio of 1.000. In contrast, performance progressively declined in the denser co-expression network with increasing circuit depth, consistent with noise accumulation under current Noisy Intermediate-Scale Quantum (NISQ) conditions. Multi-run consensus analysis identified stable hub genes, including MMP9 and SLA-DOA, as well as genes exhibiting variable community assignments. CONCLUSION: These findings reveal a conserved macrophage suppression module shared between ASFV and PEDV and demonstrate that quantum computing can serve as an independent validation framework for biologically meaningful host-response networks. Network topology emerged as a key determinant of QAOA performance on real NISQ hardware.
OBJECTIVE: The authors evaluated how quantum computing threatens the cryptographic primitives used in Bitcoin and other blockchain systems. These findings were translated into a standards-aligned post-quantum migration profile for healthcare ledgers, including consent, identity, provenance, audit, and encrypted off-chain data exchange. METHODOLOGY: Narrative analysis, theoretical security analysis, and healthcare-oriented deployment mapping of classical public-key and hash primitives used in blockchain protocols were paired with an implementation-oriented migration profile based on finalized National Institute of Standards and Technology (NIST) post-quantum standards. We summarize the mathematical assumptions underlying RSA, Elliptic Curve Cryptography/Elliptic Curve Digital Signature Algorithm (ECC/ECDSA), and Secure Hash Algorithm (SHA-2/SHA-3-family) hash functions; analyze their susceptibility to Shor's and Grover's quantum algorithms; compare Federal Information Processing Standards (FIPS) 203 Module-Lattice-Based Key-Encapsulation Mechanism Standard (ML-KEM), FIPS 204 Module-Lattice-Based Digital Signature Standard (ML-DSA), and FIPS 205 Stateless Hash-Based Digital Signature Standard (SLH-DSA); and map their distinct roles to healthcare-ledger authorization, auditability, identity, and encrypted off-chain exchange. The term Advanced Hybrid Module-LWE & Code-Based (AHMC) is used only as shorthand for a standards-aligned hybrid post-quantum cryptography (PQC) migration profile and does not denote a proprietary product, a novel algorithm, or a new cryptographic primitive. Proposed adoption of hybrid, quantum-resistant cryptographic primitives for cryptocurrency wallets, transaction signatures, and ledger security during an interim migration period (hybrid classical + PQC, followed by PQC-only). Qualitative and implementation-oriented assessment of (1) break feasibility of RSA/ECC under Shor's algorithm, (2) effective security reduction for hash functions under Grover's algorithm, (3) security assumptions and composition requirements of a standards-aligned hybrid migration profile, (4) transaction-size and verification-cost impact, and (5) healthcare-specific implications for long-retention consent, identity, provenance, and audit records. RESULTS: Shor's algorithm reduces integer factorization and discrete logarithms to polynomial time, directly compromising Rivest-Shamir-Adleman (RSA) and ECC/ECDSA once fault-tolerant, large-scale quantum computers exist. Grover's algorithm yields a quadratic speedup for brute-force search, effectively halving the security margin of symmetric keys and hash functions at fixed output sizes. The AHMC-L1/L3/L5 profiles use ML-KEM-512/768/1024 for key establishment and ML-DSA-44/65/87 for transaction authentication, with SLH-DSA as a hash-based fallback. During a hybrid ECDSA+PQC migration, verification requires one classical and one PQC verification per authorization; transaction-size overhead is dominated by PQC signatures, approximately 2.4 KB for ML-DSA-44, 3.3 KB for ML-DSA-65, and 4.6 KB for ML-DSA-87 before script and encoding overhead. For healthcare ledgers, these findings support selective use of post-quantum signatures for:high-value state transitions,one-time public-key registration where possible, andcontinued off-chain storage of protected health information.Deployment suitability remains contingent on workflow-specific latency, storage, availability, key lifecycle, and side-channel testing. CONCLUSIONS: Quantum risk to blockchain signatures has direct implications for healthcare systems that depend on long-lived consent, identity, provenance, and audit records. A staged, standards-aligned migration profile can preserve authorization and ledger verifiability while keeping protected health information off-chain. The AHMC label refers only to this migration profile, not to a new cryptographic primitive; healthcare adoption requires open implementations, empirical benchmarking, crypto-agile key governance, and side-channel-resistant engineering.
Cyclic nucleotide injection in neurons shows that cAMP controls a new type of membrane permeability. The neuron response to cAMP has a short delay, unusual bioenergetics and is blocked by drugs binding with the regulatory subunit of protein kinase. These data are interpreted in terms of the hypothesis that the controlling system of the living cell is a molecular (DNA, RNA, protein operators with complementary addresses), holographic (quick changeable lattice--cytoskeleton), quantum (each phonon examines whole lattice), hypersound (with wave length 100-10,000 A that does not destroy molecules) system with an inner point of view (molecular coding of questions and answers about quantum processing). Neither an electron, nor a macroscopic computer has an inner point of view.
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Chromosome conformation capture experiments have revealed the underlying spatial interactions that govern three-dimensional (3D) genome organization and topology. Detecting 3D contacts between genomic loci considerably enhances our understanding of fundamental regulatory processes. Modeling 3D structures from experimental contact matrices can further contextualize the relationship between 3D genome organization and regulation. While classical algorithms have been successful in reconstructing genomic conformations, we investigate the prospect of quantum computation to aid in modeling the conformational space. In this context, we propose a novel variational quantum algorithm (VQA) to model the distribution of 3D genomic structures from experimental contact data. Through rigorous evaluations, we demonstrate the capability of our algorithm to sample ensembles of viable 3D conformations that agree well with experimental and simulated contact data. Furthermore, we extend our methodology to model the conformational space of a single cell or a population of cells. In the advent of sufficient quantum utility, the insights gained from this study can serve as a foundation for investigating high-resolution, large-scale ensembles of genomic conformations through generative VQAs.
The use of computer simulations to study the internal dynamics of globular proteins and nucleic acids has attracted a great deal of attention in recent years. These simulations constitute the most detailed theoretical approach available for studying internal motions and structural flexibility of biological macromolecules. In this paper we review recent work concerned with the use of computer simulations for the interpretation of, and comparison with, experimental probes of molecular dynamics. New methods for calculating vibrational spectroscopic lineshapes from computer simulations are discussed. A quasiharmonic approximation is described by which classical computer simulations on multidimensional potential surfaces can be used to estimate the effects of anharmonicity on vibrational spectra. A novel aspect of the method is the use of normal-mode eigenvectors as the independent coordinates for Monte Carlo sampling. Results for isolated small molecules and liquid water are reviewed. The construction of vibrational lineshapes from quantum computer simulations using path integral methods with new (quasiharmonic and variable quadratic) reference systems are discussed. Results for small model systems are presented and extension of the methods to large molecules is discussed. The recent use of molecular dynamics simulations to analyze X-ray refinement models for proteins and nucleic acids is also reviewed.
The need for faster and more informative data processing for better decision-making is driving the adoption of artificial intelligence (AI) in the agricultural sector. Thanks to recent advancements in computer science and the increase in computational powers of modern computers, AI is not only augmenting traditional solutions, but also helping in developing novel solutions to existing challenging matters. AI-driven models have an exceptional ability to identify patterns and combine a diverse collection of data together and make inference. The increasing pressure on farmlands posed by the growing global population and climate change is lessening growth, yield, and productivity ultimately posing risk to food security worldwide. Incorporation of AI in agriculture has the potential to drive farming efficiency to new heights. This comprehensive review critically evaluates the evolution of AI in agricultural biotechnology from a theoretical concept to a global phenomenon. A comprehensive literature search was performed using major scientific databases, including PubMed, Web of Science, Embase, Scopus, Lens and the Cochrane Library. In this review, we empirically demonstrate the fields advancement toward more capable AI systems and discuss the current applications of AI across crop improvement and precision agriculture such as crop improvement and genetic engineering, genomic selection and plant breeding, pest and disease detection, precision agriculture and smart farming, soil health and nutrient management, climate resilient crop development, livestock biotechnology, challenges and ethical considerations in AI based agricultural biotechnology. Furthermore, this review addresses the exponential growth of commercial intellectual property in the field and contrast it with academic publication outputs. Finally, we critically assess the ethical challenges impeding equitable adoption of AI including data sovereignty and digital divide, while projecting future frontiers involving quantum computing. This review will help build sustainable agricultural systems capable of adapting to climate change, contribute to the development of climate-resilient and high-yielding crops, and address global food security challenges.
BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.
It is proposed that "Quantum Molecular" computer of a neuron consists of the cell cytoskeleton serving as calculating media and input ionic channel sending a hypersound signal to observe these media. The sound spreads through the media travelling along microtubules and microfilaments and switching between those via molecular bridges which serve as elementary switches. The whole system works like a wave guiding net connecting input ionic channels (which generate different sound signals) and output ionic channels (which are controlled by the processed sound signals). Thus the output of such systems depends on the input (controlled by synaptic activity) and on the construction and state of these calculating media. We think that the sound waves spreading through different calculating media solve different physical problems. The construction of the calculating part of the cytoskeleton, according to the hypothesis, is different in different neurons. It is defined by special protein which is produced by DNA, RNA and protein molecular word processor (during brain development and, may be, education). We comment on how the existence of an extremal computer produces an impact on physics and mathematics exemplified by the optimality principle as substitution of physical relativity principle for a complex problem.
In a fluorescent donor group and a nonfluorescent acceptor group are incorporated into a biopolymer, so that radiationless energy transfer occurs between the two groups, the apparent separation of the groups, as determined by energy transfer, will be influenced by the existence of a distribution of separations. This might arise from the presence of significant localized flexibility at the sites of attachment of the two groups, or from internal flexibility involving the biopolymer itself. If a Gaussian form is assumed for the distribution of separations of the donor and acceptor groups, the efficiency of transfer is dependent upon the width of the distribution, as well as the average distance between the groups. Significant differences may thereby arise between the true average separation and the separation computed from transfer efficiencies by the usual procedures. The deviations are different for transfer efficiencies computed from quantum yields and from decay times. They become more important with increasing width of the distribution of separations and increasing efficiency of transfer. In general, if a distribution of separations is present, the average separation is most reliably computed by procedures which take into account the effects of this distribution.
We review our research on triose-phosphate isomerase and bacteriophage T4 lysozyme. In our studies over the last ten years we have used electrostatic potentials, computer graphics, quantum mechanics, molecular mechanics, molecular dynamics and free energy calculations to try to understand why triose-phosphate isomerase is such an efficient enzyme and why its efficiency is dramatically decreased by several site-specific mutations. For T4 lysozyme we have used free energy methods to analyse and try to understand why Thr-157----Val and Thr-157----Ala mutations decrease protein stability by about 1-2 kcal/mol.
Quantum-chemical computations using the Hückel method have been performed on quinoline derivatives that exhibit antibacterial activity against E. coli, in order to obtain quantitative structure-activity relationship and drug-receptor interaction data.
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Quantum-mechanical computations performed by the ab initio self-consistent field molecular orbital method have been used for the determination of the principal hydration sites of acetylcholine in its gauche et trans conformations. The hydration destabilizes the gauche form with respect to the trans one by about 2 kcal/mole. Taking into account, however, that the gauche form is 3 kcal/mole intrinsically more stable, this form still remains the most stable one of acetylcholine in solution.
Equilibrium geometries and binding energies of model "salt" or "ion" bridge systems have been computed by ab initio quantum chemistry techniques (GAUSSIAN82) and by empirical force field techniques (AMBER2.0). Formate and dimethyl phosphate served as anions in the model compounds while interacting with several organic cations, including methyl ammonium, methyl guanidinium, and divalent metal ion (either Mg2+ or Ca2+) without and with an additional chloride; and a divalent metal ion (either Mg2+ or Ca2+), chloride, and four water molecules of hydration about the metal ion. The majority of the quantum chemical computations were performed using a split-valence basis set. For the model compounds studied we find that the ab initio optimized geometries are in remarkably good agreement with the molecular mechanics geometries. Several calculations were also performed using diffuse fractions. The formate anion binds these model cations more strongly than does dimethyl phosphate, while the organic cation methyl ammonium binds model anions more strongly than does methyl guanidinium. Finally, in model compounds including organic anions, Mg2+ or Ca2+ and four molecules of water, and a chloride anion, we find that the equilibrium structure of the magnesium complex involves a solvent separated ion pair (the magnesium ion is six coordinate), whereas the calcium ion complex remains seven coordinate. Molecular mechanics overestimates binding energies, but the estimates may be close enough to actual binding energies to give useful insight into the details of salt bridges in biological systems.
The optimized geometry of the conformation of atoms constituting the 6-pyruvoyl tetrahydropterin molecule, the labile key intermediate of tetrahydrobiopterin biosynthesis, was obtained by molecular orbital calculations within the MINDO/3 framework. The stereostructure of the molecule showing the preferred mode for binding to sepiapterin reductase or pyruvoyl tetrahydropterin reductase was drawn in perspective. The resulting structure with the equatorial staggered configuration of the 6-1',2'-dioxopropyl (pyruvoyl) side chain indicated that O(1') and H(6) were located in the trans position around the C(6)-C(1') bond and that the two vicinal carbonyls in the side chain were fixed in the incomplete trans form. The calculation of atomic charges and LUMO coefficients of these carbonyls suggests that the C2'-carbonyl may be more reactive toward NADPH than the C1'-carbonyl in the enzymatic reaction.