Avogadro Number’s-oriented HyperGeometric and ChebyshevT Functions for Black Hole Paradox Generalizations and Turing Machine Ruled Quantum Homeopathy Water Memory Entanglements for the Translation of COVID19 Homeopathy Remedies into the Neprilysin and ACE2/AT1R receptors targeted DRVYIHPFX- ligands.

Computer-aided drug discovery/design methods which are broadly classified as either structure-based or ligand-based methods have played a major role in the development of therapeutically important small molecules for over three decades. In this strategy, an innovative Quantum Homeopathy-driven ligand replacement method translated here into a Structure-based evolutionary method which is in principle analogous to high-throughput screening for delocalizing pharmacophoric reconstructions that are perfectly well substituted in a fuzzy sphere shaped druggable scaffolding via an Avogadro Number’s-oriented quantum geometry and crowding-based low mass phase protocol for a phenotypic steady-state multiple genetic parental algorithm as a Quantum Geometry-based Dynamic Homeopathic Solution and for a Modified and Restricted Ligand Selection (QuGeDoHoSoMorLiP) approach. It also visualizes an idea that has been invoked in the description of other coherent energy transportations in organic molecules in the purpose of a better Turing Machine learning exploration of the energetic hypergeometric hypersurface for the identification of multiple Quantum Phase Minima Homeopath Solutions in a single Hadamard run while preserving the chemical diversity of the generated RoccustyrnaTM structures. This could be a useful mathematical path for linking Quantum Negative Energy Harnessing from COVID19 Homeopathy remedies with Deep Geometry Sensing as described here which can be Turing Machine translated into Quantized Miasm Moiety metrics that exposed to the water interfaces and has sufficient electronic density data for the same purpose, for the designing of new crystallographic structures following the Steering criteria and conditions whose violation excludes the possibility of describing the observed measurement statistics with local hidden state (LHS) modes. In this case and in addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in this drug discovery campaign are discussed. The article combines quantum field theory with quantum homeopathy entanglement behind these most important Avogadro Number’s-oriented HyperGeometric and ChebyshevT Functions for Black Hole Paradox Generalizations where the substances which are not present, that is the homeopathic remedy, could become a kind of receptacle which ‘absorbs’ the Quantum Information exctracted from COVID19 patient’s symptoms, insofar as they are related by similarity of these Turing Machine generated patterns/features, in a Gegenbauerian model which reconfirms the feasibility of our original intention for finding a new model-building recipe in this direction, a prototype Quantum entropy circuit that clearly illustrates a Quantum Homeopathy oriented design principle for a coherent exchange of single energy quanta between electronic and vibrational degrees of freedom with the capacity of calculating the enhanced negative free energy scoring functions from the best-docked poses of the Roccustyrna ligand. Fascinating, although this water quantum memory mimicking topic is exploring the relationship between AdS5 Quantum fields Theory (QFT) Reduction, Vaidya and Kerr metrics for QFT to QM Reduction, Quantum Thermodynamics, and Turing Machine Learning Rules for Quantum Homeopathy Variables and Quantum Biology gives us a greater insight into viral transmissions and thus, SARS-CoV-2 biological complexities?

In light of the limited treatment modalities offered by conventional medicine for COVID-19, especially in more severe diseases, the results of the described homeopathic treatment in the cases previously presented are impressive because homeopathy is a well-established form of complementary and traditional medicine that has been used to treat a wide range of conditions, including respiratory illnesses [1,2,3,4,5]. Given the global impact of COVID-19 and despite the lack of a unified methodological standard, a number of healthcare organizations and ministries of health from various countries have recommended homeopathic treatments for the prevention and treatment of SARS-CoV-2 infections [1,2,3,4,5,6,7]. Inhibition of the SARS-CoV-2 Omicron variant spike RBD in complex with Fab XGv282 (PDB: 7wlc) binding interface interaction using small molecules or peptides is the most logical and straightforward strategy to block viral cellular entry. Consequently, our research team is committed to generating extended symmetries and quantum groupoid, algebroid, and functorial Avogadro Number’s representations demonstrating the efficacy of translating individualized homeopathy remedies in treating COVID-19 since homeopathic treatments have been employed successfully during major epidemics and pandemics of the 19th century, prompting numerous researchers to investigate the potential of homeopathic interventions to reduce the likelihood of SARS-CoV-2 infection or to alleviate COVID-19 symptoms [1,2,3,4,5,6,7,9,10]. Savera et al. 9 and Manchanda et al. reported that the most commonly prescribed homeopathic remedies for COVID-19 were Bryonia alba, Phosphorus, Arsenic album, Gelsemium sempervirens, and (Carboneum oxygenisatum or Pulsatilla nigricans). Clapers et al. conducted a prospective case series study of 103 mild-to-moderately ill COVID-19 patients in Spain, of whom 22 had concomitant diseases. The most frequently prescribed drugs were Bry, Ars, Phos, and Gels, whereas the drugs with the highest rate of ”good response” were Sulphur (6/6 = 100%), Pulsatilla (4/5 = 85%), and Bryonia alba (21/29 = 72%) [1,2,3,4,5,6,7,8,9,10,11,12,13]. The time to complete recovery after homeopathic treatment ranged from 3.5 to 14.4 days, and potency 200c achieved the fastest rates of complete recovery and the least need to change the remedy. [1,2,3,4,5,6,7,8,9,10,11,12,13,14] Finally, very low response rates of 20% and 0% were observed with the use of Gelsemium and China officinalis, respectively, despite the broad recommendation emphasized in several studies. [9,10,11,12,13,14,15,16] Other controlled randomized studies corroborated these encouraging early findings and facilitated evidence-based decision-making regarding the role of homeopathy in treating COVID-19, adding a potentially quick and effective treatment modality that is both safe and inexpensive. [1,2,3,4,5,6,7,8,9,10,11,12,13,17,18] Pre-hypertension (ICD-10-CM R03.0) is a sub-clinical condition, but remains a public health challenge globally. Appropriate intervention is needed to stop its progression to hypertension and other cardiovascular diseases. According to 8th Joint National Committee (JNC-8), blood pressure (BP) is classified as normotensive (≤ 120/80 mm Hg), high normal blood pressure or pre-hypertensive (120–139/80–89 mm Hg) and hypertensive (≥ 140/90 mm Hg). [1,2,3,4,5,6,7,8,9,10,11,12,13,19,20] Recent study has shown that during 2011–2012, 28.2% of the US adults (≥ 20 years old) were pre-hypertensive. In 2014, a study in West Bengal police personnel showed that around 42.9% police of various district of West Bengal have pre-hypertension with increased risk of cardiovascular disease conditions. [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21] Another study showed that pre-hypertension was present in 41.1% of Indian urban population of Belgaum city and recognized the necessity for detecting pre-hypertension and emphasizing on dietary and lifestyle modification (LSM) to prevent further progression to hypertension and its complications. [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21] Controlling pre-hypertension is basically a primary prevention managed with diet, exercise, and life style modifications. Now-a-days, complementary and alternative medicine (CAM) therapies are increasingly becoming popular in various chronic diseases including hypertension. Selective and integrative use of different CAM therapies has also been recommended in hypertension. Hypertension is a frequently encountered condition in routine homeopathy practice as well. Literature search revealed 13 published trials of homeopathy in hypertension; however, none on pre-hypertension. In the other trials, they hypothesized that there might be a significant difference between individualized homeopathic medicines (IH) and placebo in intervening with the progression of pre-hypertension to hypertension over 3 months of intervention while both groups received standardized recommendations for dietary and lifestyle modifications (LSM). [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23] Recent calorimetric studies of small molecule interactions with biomolecular targets have generated renewed interest in the phenomenon of entropy-enthalpy compensation and can be used as a lively discussion how the effect of extreme time-dilution in the case of homeopathic exercise may be guided by multistage entropy-enthalpy compensations of a chemogenomic system’s polypeptide chains and water molecules under similar solution conditions. [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23] Despite objections and controversies against homeopathy, its popularity has been increasing worldwide. It has been practiced virtually in every country in the world for the past 200 years and has been widely used by medical doctors and numerous other health and medical professionals as a complement and as an alternative to conventional medical care to treat a wide variety of physical, emotional, and mental health complaints. It is also used in self-treatment by the general public for minor, self-limiting complaints. [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,24] During the 1880s, a German pharmacologist Hugo Schultz observed a biphasic dose response characterized by a low dose stimulationand high dose inhibition, indicating that this initial dose dependent toxicity response, which is frequently observed in homoeopathy too could provoke an initial aggravation of symptoms of the patient followed by a compensatory/rebound response. [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25] In the beginning, he diluted his drugs simply up to the level that ended their toxicity, and indications to administer them according to the principle of similars were not known enough except fortoxicity they cause when using plant substances and liquids, Hahnemann diluted the solution with water or a mixture of alcohol and water. He then used accordingly the centesimal (1:99) scale for standardization. After each dilution, he vigorously shaked (he used the term “succuss” which is a type of more forceful shaking) the mixture at least 40 times. When he used mineral agents and various chemicals that were insoluble in water/alcohol, he diluted them with lactose (milk sugar) and triturated (i.e., grinded) the mixture since in the 1960s, Cyrus Levinthal pointed out that the apparent contradiction between the astronomical number of possible conformations for a protein chain and the fact that proteins can fold quickly into their native structures should be regarded as a paradox, known as Levinthal’s paradox [2,3,4,5,6,7,8,9,22,23,24,25,26]. Calabrese states it more accurately as, “It is a dose-time-response relationship in which there is such that at low doses the response becomes greater than the original background state or control group value in an initial dose-dependent toxicity response”. Given the tremendous impact of Quantum Mechanics (QM) in this research area of generalizing Protein-Ligand complexes which are the building blocks of life on Earth and they perform a vast array of biological functions within organisms and have revolutionized more innovative Quantum Functions have to be introduced in order to overcome the main drawback of this understanding of the structure and reactivity of small molecular system approaches which is the large memory footprint of these numerical representations of the molecular orbitals when are much larger in terms of the number of coefficients, with a significant computational overhead [16,17,18,19,20,21,22,23,24,25]. Hormesis and homeopathy are interrelated in many ways and since the robust research methodology developed in hormesis can help in carrying out research work in homeopathy toxic substances that do not exhibit hormesis can do so after their potentization, at least those with which life evolved. [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,24,25] While these methods can be less expensive than classical approaches, they make up for this deficiency by the more realistic modeling of the electronic nature of biological systems and in their ability to be broadly applied. Advent of nanoscience and new understanding about water can help to answer this question. Schultz claimed that the phenomenon of biphasic dose response explained the principle of homeopathy assertions which could also be brought into the biological realm where quantum systems of a few thousand atoms and beyond are routine. [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,26] Since this was his important discovery’s marginalization and has characterized as a schism developed in medicine after the advent of homeopathy, Calabrese and colleagues noted the problem, this intrinsic homeopathy phenomenon of biological functions of a nascent protein that exists as an unfolded polypeptide when translated from a sequence of mRNA to a polypeptide chain in a ribosome could be determined by its native three-dimensional (3D) structure derived from the physical process of protein folding [1,2,26,27], by which means a polypeptide folds into its native characteristic and functional 3D structure in an spontaneous manner when applying QM methods to biological problems. Such Protein folding which can thus be considered as the most important mechanism, principle, and motivation for biological existence, fictionalization, diversity, and evolution [27,28,29] brings an improved representation to these LegendreP Quantum Function Systems by the direct inclusion of inherently QM effects such as polarization and charge transfer. The QM effects will generate a homeopathic pharmaceutical drug designs when translating the dilution, succussion or trituration processes, the “potentization” phenomenon in which the greater number of potentizations led to longer and deeper effects of the drugs, and the need for reduced repetition of them once a medicinal agent is diluted in the ratio of 1:99 12 times (i.e., in its12C potency) or in the ratio of 1:9 24 times (i.e., in its 24X potency). There would be in high probability no remaining molecules of the original medicinal agent (i.e., source-drug) in the solution. These improved representations and novel insights of ultra low dilutions of nanoparticles can cause a hormetic dose-response that can be gleaned from the application of QM tools to biomacromolecules in ultra low aqueous solutions indicating that these ultrahigh dilutions of drugs/toxic substances used in homeopathy as medicines, such nanoparticles are present as an evidence confirming the hormesis as a generalized adaptive response, which may be further generalized to nanoscale druggable scaffolding, drug design, and chemical geometric challenges. Herein, through the examples chosen, I show the power of QM to provide novel insights into SARS-COV-2 biological systems, while also impacting practical hermetic applications such as structure refinement for providing drug designing examples of QM studies on biological systems that focus on ACE2 protein solvation as viewed by QM, QM enabled structure-based drug design, and NMR and X-ray biological structure refinements using QM derived restraints. These QM and QM enabled structure-based drug designs are based on Turing Machine Learning Relationships by translating quantum entanglement negativities occurred in homeopathic practices where far higher dilutions than N number are used regularly in daily practice. Discussions on how these translational effects can happen will be discussed later. Of the tools and applications discussed in this paper, X-ray structure refinements from many different research groups have shown that various chemicals/drugs diluted homeopathically even beyond Avogadro’s limit can stimulate hormetic responses in living cells from medicinal effects of serially diluted solutions that are not confined to substances conventionally used in homeopathy as source-drugs. These hormetic responses are exctracted from Chemicals like histamine, thyroxin, and aspirin that are diluted homeopathically, even far beyond Avogadro’s limit showing opposite effects to those from their pharmacological doses when using QM models. In particular, QM biological models of quantum entanglement are borrowed from physics and developed into an algebraic argument to explain how double-blinding randomized controlled trials could lead to equivocal evidence for the efficacy of homeopathy by proving these quantum homeopathic calculations as a protein-ligand folding problem which was brought to light over 60 years ago. These quantum entanglements distanced hormesis from homeopathy in an effort to establish hormesis well in modern science which is based on Calabrese along with Jona’s debate on possible links between these 2 systems when studying hormesis and the dose is conventionally kept just below the toxic threshold, while in homeopathic practice it is usually lower than this and can even be beyond the physical existence of the original agent if prepared in a typical way. These quantum entanglements accurately and efficiently include electron correlation effects and will facilitate our AT1R drug design modeling of new ligands and dispersive interactions, which is also a major hurdle that a broad range of groups are addressing. By analogy with the famous double-slit experiment of quantum physics, and more modern notions of quantum information processing, these failings are understood as blinding causing information loss resulting from a kind of quantum superposition between the remedy and placebo in these Quantum Homeopathy experimental methods that will currently be used to determine the structure of an annotated drug design. Entangled entities are generally applied to static structures, so ways in which to include sampling is an ongoing challenge because they behave as one inseparable holistic unit, whose totality cannot be deduced from any of its parts of QM models in biology is in its infancy, leading to the expectation that the most significant use of these tools to address biological problems will be seen in the coming years. It is hoped that while this account summarizes where I have been, it will also help set the stage for future research directions at the interface of quantum mechanics and biology. In the beginning I consider non-locality which has been defined as ‘the mysterious ability of nature to enforce protein folding correlations between separated but entangled parts of a Quantum Homeopathy system that are out of speed-of-light contact to reach instantaneously across vast spatial distances, or even across time itself, and to ensure that the parts of a quantum system are made to match [19,20,21,22,23,24,25,26,27,28] of chemical spaces since protein folding is assumed to be one of the miracles of nature that human technology finds quite difficult to follow, due to the very large number of degrees of rotational freedom in an unfolded polypeptide chain. Commenting on the work of Chikramane et al., Calabrese suggested it as a step forward toward unification of hormesis and homeopathy in the early treatments of Hahnemann which can now be identified as hormetic in nature of atom-centered Gaussians which have traditionally been the most common and widespread choice of basis set for molecules [1,2,3,4,5,6,7,8,9,10]. This means that observation of one part of an entangled system instantaneously provides information about the rest, provided one knows how the system is entangled. However, attempts to observe the parts of an entangled system as separate entities, destroys the whole despite a lot of progress being made in the prediction of protein native structures through the use of artificial intelligence [2,3,4,5,6,7,8,9,10,11], understanding the physical folding mechanisms and laws still remains the most fundamental task in molecular biology and biophysics. Levinthal also pointed out there should be pathways for protein folding [4,5,6,7,8,9,11,12]. Several strong arguments are in favor of such a choice: the compactness of the representation which is defined by a handful of coefficients, the ability to represent atomic orbitals well (Slater functions are in theory superior due to the cusp at the nuclear position and the correct asymptotic), the simplification in the computation of molecular integrals which are often obtained analytically (this is the weak point of Slater orbitals which require expensive numerical evaluations) [1,2,3,4,5,6,7,8,9,10,11,13] while non-locality and entanglement have been experimentally demonstrated at the nanoscopic level of particles, atoms and even molecules [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] where macroentanglement between ultra small doses in water solutions and water memory is still a matter of Protein folding conjecture which can therefore be considered as an organized reaction as stated in Anfinsen’s Dogma, the “thermodynamic hypothesis”. That means that the three-dimensional structure of a native protein in its normal physiological milieu (solvent, pH, ionic strength, presence of other components such as metal ions or prosthetic groups, temperature, and other) which is the one in which the Gibbs free energy of the whole system is lowest; that is, that the native conformation is determined by the totality of interatomic interactions and hence by the amino acid sequence, in a given environment [6,12,14]. Indeed, the non-commuting algebraic formulation of orthodox quantum theory which contains the extremely small number called Planck's constant, h = 6.626 * 10⁻³⁴ J s⁻¹ would appear to preclude such quantum effects between macroscopic objects/entities where their main disadvantage is the non-orthogonality of the basis which can become a severe problem especially for large bases leading to a computational bottleneck when orthonormalization is required or worse numerical instabilities due to near linear-dependency in the basis [2,3,4,5,6,7,8,9,10,11,12,13,14] of well-defined native 3D structures of small globular proteins and are uniquely encoded in their primary structures (i.e., the amino acid sequences), and are kinetically reproducible and stable under a range of physiological conditions that must there be physical mechanisms and allow polypeptide mimicking small molecules to find the native states encoded in their sequence. Nevertheless, it is proving increasingly possible to conceive non-locality and entanglement in a similar but less restricting algebraic [21,22] or, indeed non-algebraic context [23], and usefully apply these concepts to phenomenological problems arising out of CAM research of the spectrum of the plane waves (PWs) that are ideally suited for periodic systems and are orthonormal by construction. However a very large number of them needs to be employed in order to achieve good precision, especially if one is interested in high resolution in the nuclear-core regions [3,4,5,6,7,8,9,10,11,12,15]. Lately, the use of projector augmented wave (PAW) [5,6,7,8,9,10,11,12,13,14,15,16] and linearized augmented plane wave (LAPW) [6,7,8,9,10,11,12,13,14,15,16,17] techniques, has made this issue less critical for PW calculations. Another challenge for PWs is constituted by non-periodic systems, which can only be dealt with by using a supercell approach [7,8,9,10,11,12,13,14,15,16,17,18]. Thus, Gernert [24] defines a ‘common pre-arranged context’ which he suggests characterizes the preparation to be made in advance (or naturally given conditions) in order to enable entanglement for Quantum Chemical modeling which is constantly expanding its horizons: cutting edge research is focused on achieving good accuracy (either in energetics or molecular properties) on large non-periodic systems such as large biomolecules or molecular nanosystems. The present paper explores this possibility along with the relationship between hormesis, homeopathy, Entanglement for Quantum Chemical modeling as a popular choice to circumvent the problem of using pseudopotentials [4,5,6,7,8,9,10,11,12,16] in the core region by reducing the number of electrons to be treated and at the same time removing the need for very high-frequency components. The black hole solution of the four-dimensional spacetime Einstein–Maxwell functions of classical general relativity has the following physical characteristics: mass (M), electric charge (Q), and angular momentum (J) supporting the idea that lead us to ways of considering macroentanglement as a possible explanation of significant correlations found between carefully performed Quantum Homeopathy Function Experiments on spatially separated pairs of small molecules [25,26] which is essential for an all-electron description where varying resolution is a prerequisite for efficiency. The price to pay, to provide a representation with a given number of vanishing moments, is a basis consisting of several wavelet functions per node as the most common choice of basic functions in the Euclid Space framework which is a generic orthonormal polynomial basis of order k, providing a second possibility to increase the resolution of the representation alongside the adaptive grid refinement [15,16,17,18,19]. Weather ley-Jones et al. had already suggested the reason DBRCTs apparently fail to unequivocally demonstrate the efficacy of homeopathy, is because specific and non-specific effects of the therapeutic process are actually interdependent and mutually correlated with the static spherically symmetric solution as a Schwarzschild metric with mass as its only physical characteristic [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]. The static spherically symmetry solution with electric charge is the Reissner–Nördstrom metric [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,21,22], and the axisymmetric generalization of the Schwarzschild metric with angular momentum is known as the Kerr metric [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24] and localizes the orthonormal basis as an ideal match for modern massively-parallel architectures [18,19,20,21,22,23,24]. I am confident that this static spherically symmetric solution as a Schwarzschild metric with mass as its only physical characteristic will become competitive with or even superior to traditional ones as an axisymmetric solution of the Reissner–Nördstrom metric has been generalized by incorporating angular momentum to the Kerr–Newman metric [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]. Thus, according to this holistic paradigm, the methodologies used in this axisymmetric generalization of the Schwarzschild metric with angular momentum, and the loss of information must necessarily destroy the very thing when they are trying to investigate the basis functions that are localized as Gaussian-type orbitals yet orthonormal as plane waves. One crucial property of Euclid Spaces is the disjoint support (zero overlap) between basis functions in adjacent nodes [14,15,16,17,18,19,20,21,22,23,24,25,26,27], paving the way for adaptive refinement of the mesh, tailored to each given function. Considering these four metrics that are often referred to as the “black hole” exact solutions of general relativity in 1943, Vaidya proposed a radiating spherically symmetric solution. [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28] These are the solutions where I am developing further in this theme into a critique of cryptomethodology [27] of quantum function series couched in terms of a developing algebraic metaphors of the homeopathic therapeutic process called PPR entanglement [22] in order to achieve high precision and keep the memory footprint at a manageable level. This is an adaptive strategy which refines grids only if needed is necessary [19,20,21,22,23,24,25,26,27,28,29] and is referred to a choice that has a profound impact on the minimization strategies that can be adopted in order to solve SCF problems such as the Roothaan–Hall functions of the Hartree–Fock (HF) method. In other words, these strategies which rely upon having a fixed basis, such as the most common atomic orbital based methods [20,21,22,23,24,25,26,27,28,29] are excluded beyond the Vaidya metric, which was originally applied to radiating stars, and can be regarded as the simplest generalization of the Schwarzschild metric. In 1974, Hawking applied Quantum theory to determine that black holes emitted heat radiation [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] only for the occupied molecular orbitals are needed both in HF and DFT to describe the wavefunction/electronic density. It is the Quantum theory which predicts that black-hole mass will gradually evaporate through radiation and therefore, the black hole solutions in this project may have a fourth physical characteristic in this macroentanglement as inputs for our innovative Quantum Functions and Turing Machine Learning Rule Relationships. [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] This macroentanglement interpretation between the patient, practitioner, and homeopathy remedies can facilitate the design of new druggable scaffolds and chemicalized structures by translating these therapeutic processes into a direct minimization of the orbitals without requiring a fixed basis representation which also must be considered. Additionally, using Euclid Spaces on an adaptive grid generates representations with discontinuities at the nodal surfaces, that poses a challenge when differential operators are considered the Vaidya solution which have been already applied to black holes when studying the Hawking radiation phenomenon. [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33] As will be shown in the paper, if the Hartree–Fock functions are reformulated as coupled integral functions, it becomes possible to minimize the occupied orbitals, without ever recurring to differential operators like celestial bodies that are present in the nature and require the Vaidya–Kerr solution which always have rotational angular momentum and radiation, including radiating rotating stars and black holes. [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34] Einstein's field functions are nonlinear differential functions and can obtain accurate analytical solutions by using hypergeometric and symmetry mimicking functions that are envisaged as expressible in terms of wave functions; ψ_Px, ψ_Pr, ψ_Rx, each expressing a multitude states and corresponding to the macroentanglement states from patient (Px) and practitioner (Pr) quantum systems, and remedy (Rx) atomic orbitals. [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,35,36] I further define the biological counterparts of this evolutionary drug design macroentanglement as the measure of stochasticity of an evolutionary process of chemical potential (evolutionary potential) as the amount of evolutionary work required to add a new trainable variable (such as an additional atomic orbits and atomic charges) [Supplementary material (PLIP Reports1-8)] into these Quantum Electrodynamics Subsystems from Quantized Water Memory and Hormetic Networks. Second, the Papapetrou gauge and Ernst functions can also be applied to solve the axisymmetric Einstein's field functions [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,37,38], [13-30,39,40] with wave function ψ_n, in any potentially macroentanglenment state that corresponds to the quantum subsystems of each therapeutic situation whereas Px may be considered in a state of wellness (|Px↑〉) or unwellness (|Px↓〉); Pr may be helpful (|Pr↑〉) or unhelpful (|Pr↓〉); and the remedy may be curative (|Rx↑〉) or non-curative (|Rx↓〉). [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41] In addition, previously conducted studies have demonstrated that the Kerr and Kerr–Newman metric can be derived from the orthogonal ansatz by applying an ellipsoidal-coordinate transformation [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30], [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42] for the development of a phenomenological approach and for the description of ChebyshevT-adS5 Quantum fields theory (QFT) Reductions, which involves modeling of intregralized chemical Block Systems based on Black Hole Paradox Generalizations and Supersymmetry breaking foundations for Turing Machine Learning Ruled Calculations as a grand potential and as a function of an evolutionary macroentanglement potential, a Hidden MacroEntanglement Evolutionary Potential. [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40] The World Health Organization considers homeopathy to be a part of “traditional medicine.” How can ultra-high dilutions, as used in homeopathy, be effectively translated into methods known for solving axisymmetric problems in the literature are as follows while also exhibiting hormesis at least mathematecically?

We employ the conceptual apparatus of Quantum Thermodynamics to develop a Quantum Hormesis Phenomenological Translational Theory of evolution and of the origin of life that incorporates both equilibrium and nonequilibrium evolutionary processes within a Avogadro Number’s based mathematical framework of the theory of learning based on Turing Machine Learning Relationships that takes account of Quantum Mechanics which is a basic theory of physics [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41]. The threefold correspondence is traced between the fundamental quantities of quantum thermodynamics, the theory of learning, and the theory of evolution. Under this theory, and since in quantum mechanics, physical states of some objects are changed by observations and major transitions in evolution, including the origin of life, represent specific types of physical phase transitions[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45]. The observer can only know the states after the observations but can not know the states before the observations from [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46] the COVID-19 viral infectious which emerged in China at the end of 2019 and was accountable for many fatal cases. The physical states correspond to configurations in these Turing Machine Ruled computations [2,4,35] refer to what the WHO committee declared a global health emergency [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,46,48] and are based on the rate of the increasing spread of the infection [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37] in the range 2.0-6.0 with a reproductive number (rN) 5,4 higher than SARS and Middle East respiratory Syndrome Coronavirus (MERS) respectively, and [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47] with fatality rate of about 4% that also will be referred to the all observed configurations and inputs in our Turing Machine Ruled experiment. In order to introduce quantum mechanics into the computation theory, and since Deutsch [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48] has proposed the quantum computer studies in the quantum computers, as in quantum mechanics, a configuration is defined as a base vector in Hilbert space and a transition is described by a unitary matrix. [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49] A computation is here defined to operate a unitary matrix to vectors of atomic charges from targeted antiviral drugs against the viral main protease Mpro (or 3CL protease), a cysteine protease which cleaves the two polyproteins (pp1a and pp1ab) of SARS-CoV-2 at multiple locations, resulting in the various non-structural proteins, which are the key for viral replication. Nirmatrelvir (PF-07321332), is an irreversible inhibitor of SARS-CoV-2 Mpro that is co-formulated with ritonavir allowing an oral route of administration (known as PAXLOVID). [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50] When treatment is initiated during the first days after symptom onset, it results in roughly a 90% protection against severe COVID-19 and hospitalization (Owen et al.,2021). Even though the Mpro-gene can be slightly affected by evolutionary mutations, the antiviral potency does not seem to be compromised (Sven Ullrich et al., 2021), [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59] in terms of matrices and vectors is less comprehensible than that in terms of transition functions. Bernstein & Vazirani [I] have formulated the quantum computers called Quantum Turing Machines in terms of the transition functions for Genomic characterization by using a simple DNA cryptography genetic code according to the main dogma of biology through a process of DNA, RNA, Amino Acid are underway from scientists worldwide to understand the rapid spread of the novel coronavirus (CoVs), and to develop effective coding drug-protein-gene personalized intervention options. The definition of computations by their Quantum Turing Machines is exactly the same as by quantum mechanics, so that they do not make full use of their own definition on the Quantum Turing Machines so far published make use of vectors and matrices [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68]. [4,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69] However, a condition for the transition functions is still written in terms of vectors and it is hard to check, given a transition function, whether it satisfies the condition or not. [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70] The Quantum Turing Machine defined by Bernstein & Vazirani behaves according to a probabilistic distribution. Hence it is natural to take a Quantum Turing Machine as an extended probabilistic Turing Machine Learing relationship in a major part of the biological and chemical biodiversity in Brazil’s natural products remains unexplored [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70] by removing these restrictions by modifying the Quantum Turing Machines so that they can the.mol2 files from Brazil’s natural products natural products and their computations. In order to show the power of the new Quantum Turing Machines, I prove that a class of.mol2 and SMILES accepted by the new machines in polynomial time includes both NP and co-NP. In Section 2, I introduce the concepts of superpositions of chemical configurations and biological observations into the probabilistic Turing Machines. [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74] The SARS-CoV-2 virus enters the airway and binds, by means of the S (Spike) protein on its surface (after whose image the term coronavirus is coined), to the membrane protein ACE2 in type 2 alveolar cells since these S protein-ACE2 complexes are internalized by endocytosis and facilitates the entry of each virion into the cytoplasm by initiating an inflammatory process with release of proinflammatory cytokines, and establishing a self-powered cascade event which were inserted as inputs into a Quantum Turing Machine, an extension of a probabilistic Turing Machine showing a difference between Bernstein & Vazirani's definition and ours. In Section 1, I point out a difficulty which lies in both types of the Quantum Turing Machines, and solve it by introducing new machines called halting Quantum Turing Machines. In Sections 2 & 3, I show the power of the halting Quantum Turing Machines [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74] which could be beneficial for the generation of COVID-19 new chemical geometrics. [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77] NEP activity is elevated in acute respiratory distress syndrome and it is conceivable that it is also high in COVID-19. Multiple clinical studies are in progress to determine the effect of ARB therapy in COVID-19 patients Controlled evaluation of Angiotensin Receptor Blockers for COVID-19 respIraTorY Disease-Full Text View-ClinicalTrials.gov and preliminary evidence indicates a reduction of critical prognosis and a lower death rate in ARB-treated COVID-19 patients (4). [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80] I then define Quantum Turing Machines by extending their probabilistic Turing Machine Rules and show that the proposed Quantum Turing Machines have the same powers as those by Bernstein & Vazirani. Both Bernstein & Vazirani's machines. Since Quantum physics and biology over the past decades have long been regarded as unrelated disciplines, describing nature at the inanimate micro-level on the one hand and living species on the other that are required to be time-bounded we get explicitly results from the machines by using QFT to QM methods, vertex prizes, and edge costs including ab initio Density Field Theories (DFT) [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83] incorporated in a common Quantum-many body premium technique and a semi-empirical computational scheme for translating in-vitro data from SARS-CoV-2 biological systems in an attempt to control, characterize, and prevent this devastating viral disease. Density Field Theories (DFT) are implemented here as more systematic and less expensive methods [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85] to describe the rich complexity of SARS-CoV-2 variant biosystems that were strikingly correlating with important risk factors for COVID-19, not only in the brain but also in peripheral tissues. [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87] Protein–ligand binding affinity calculation is crucial to the discovery and optimization of lead compounds based on the protein structure. [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90] With the recent advancements in data storage and computing power, various computational methods have been developed to calculate the binding affinity of a given protein–ligand complex,1 including molecular docking, molecular dynamics simulation and molecular mechanics/quantum mechanics particularly when focusing on the COVID19 disease since the behavior of SARS-CoV-2 biological complexities do not differ essentially from the behavior of the mechanical systems of rigid bodies, gas molecules, and fluids similar. [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94] These combinations of Quantum Mechanical and Quantum Homeopathy mathematical methods are preferably implemented in this project for translating COVID-19 biosystems quantitative observables and peculiarities of theirs behavior in an attempt to generate the Quantum Information required for the entanglement between two non complementary pharmacophoric parts A1 and A2. It is widely known that molecular dynamics simulation methods and molecular mechanics/quantum mechanics methods can calculate the protein–ligand binding affinity with high accuracy [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49] where A1 refers to the electron states involved in the mechanisms of injury common to age-related disorders and A2 to those present after SARS-CoV-2 infection, both in the brain and the periphery. Nevertheless, the extensive computational resources required by these methods greatly limit the ligand search space in the process of drug design based on Bohmer and Hogan proposed method for obtaining the Vaidya–Kerr spacetime [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]. They utilized a retarded u, t−r coordinate transformation to rewrite the non-static mass function and rotating parameter as m (u) and a (u), respectively in terms of the Kerr–Schild coordinates, which express the Kerr solution using Cartesian coordinates [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96]. All results of these previously conducted studies involve radiating rotating black hole solution. My derivation is a nonstationary generalization of previous researches [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98] ; however, this paper presents an improved version of the orthogonal ansatz as well as a more concise and intuitive derivation method. Until the 1950s, the common opinion was that quantum fields theory (QFT) was just quantum mechanics (QM) plus special relativity. But that is not the whole story, as is described in [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99]. There, the authors mainly say that the fact that QFT was “discovered” in an attempt to extend QM to the relativistic regime is only historically accidental. Indeed, QFT is necessary and applied also in the study of condensed matter, e.g., in the case of superconductivity, superfluidity, ferromagnetism, etc. The substantial difference between QM and QFT was understood around the 1950s [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100], the Haag’s theorem [4,[4,–101], the breaking of spontaneous symmetry, the dynamic generation of collective modes (long range correlations). In QM the Stone-von Neumann theorem [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104] holds: for systems with a finite number of degrees of freedom the representations of the CCRs are “unitarily equivalent”, and therefore physically equivalent. The theorem of Stone-von Neumann does not apply to QFT because the fields by their nature introduce an infinite number of degrees of freedom, and so therefore the hypothesis on which the theorem of Stone-von Neumann is based fails: in QFT there are infinitely many unitarily inequivalent representations. There are physically different “stages” or dynamic regimes. For a review on these topics, see, for example, foundational works in [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105]. Another common opinion is that QFT does not have quantum information content, differing from QM (for example, in the case of a two-levels quantum system). This is a very important point, of great interest to us. In fact, Ι don’t fully agree with this common opinion, in the sense that I believe that there is hidden quantum information in QFT. I instead agree that the quantum information content is not explicit in QFT, and, put in these terms, this can be taken as one of the main differences between QFT and QM. Then, Ι maintain the following main differences between QFT and QM: (1). The number of degrees of freedom is infinite in QFT and finite in QM. (2) The representations of the canonical commutation relation (CCR) are all unitarily equivalent in QM (for the Stone-von Neumann’s theorem). Instead, QFT admits unitarily inequivalent representations (uir) of the CCR. In the case of QFT with interactions, Haag’s theorem states that the representation of the interacting fields is unitarily inequivalent to that of the free fields. (3) QFT does not seem to have explicit quantum information content, differing from QM. (4) From point 3 it follows that, while QM can be simulated directly by a quantum computer (QC), QFT cannot. The QFT must first be adjusted before being simulated, and the regulator is typically a lattice, which, however, breaks Lorentz’s invariance. For the topic of quantum simulation of QFT, see, for example [13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106]. The various stages of the reduction mechanism are illustrated below, which is quite complex and requires sophisticated mathematics. I considered a boson field operator, over which I performed an ansatz that admits an attractor in whose basin there is a flow of spatial degrees of freedom, an analogous ansatz was used for an SU (2) gauge field in [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109]). The ansatz I perform in this work corresponds to a boson translation in terms of the annihilation operator in momentum space. This defines a new vacuum and, in the case of infinite volume, the two Quantum Homeopathy Representations are unitarily inequivalent. However, in the finite volume of the basin of the attractor, the two Avogadro Number’s representations become equivalent, indicating that the bosonic Quantum fields Theory has been reduced to Quantum Mechanics. Within this attractor basin it is possible to define a new metric, quantized in Planck units, that undergoes quantum fluctuations, the quantum foam [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50], which induce uncertainties in the position states. The latter can be interpreted as maximally entangled qubits on the surfaces of the spheres centered at the attractor point. This is possible if an adequate non-commutative space, which is a generalization of the fuzzy sphere [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,110,111,112,113,114,115,116,117,118,119,120] is taken as the geometrical representation of the state space of the maximally entangled n-qubits states. Within these fuzzy spheres, instead, the position states are fully mixed states, and represent the spatial degrees of freedom which have been released. The entanglement entropy of the maximally entangled states equals the total quantum entropy (the von Neumann entropy) of the fully mixed states, so that quantum information is conserved. The study of representing qubit states by non-commutative geometry started a few years ago. In [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122] I looked for a quantum system, on a quantum (non-commutative) space, which could mimic (simulate) space-time at the Planck scale. For a review on quantum spaces, see, for example [20], and for the relation with QFT, see [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,123,124,125,126,127,128,129,130]. In contrast to what happens in QM, the von Neumann theorem does not hold in QFT, and the choice of a particular representation of the field algebra can have a physical meaning. From a mathematical point of view, this fact is due to the existence in QFT of unitarily inequivalent representations (uir) of the CCR (in the infinite volume or thermodynamic limit). The two particularly important cases of linear transformations are the boson translation [2,9,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131] and the Velatin-Bogoliubov transformation (for bosons) [26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132]. In this conventional approach of Quantum fields Theory, where I try to explain the interacting theories, more than one class of representations is needed. The said phenomena was observed bay Haag and sometimes is called the Haag’s no-go theorem, which states that free and interacting fields must necessarily be defined on different unitarily inequivalent Hilbert spaces. According to Einstein, “It is the only physical theory of universal content, which I am convinced, that within the framework of applicability of its basic concepts will never be overthrown” [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133]. The main problem is that when I try to construct a physical theory, by considering, e.g., the Poincare symmetry, Ι select just one of these classes and simply forget about the existence of others. This causes some problems, such as Haag’s no-go theorem. On the other hand, the formulation of S-Matrix is such that one can find the final state by operating S-Matrix on the initial state without taking into account the moment of interaction, regarding it as a black box. But it is the moment of interaction that all of these classes may become equally important with the basic laws of thermodynamics which were established at a time when the atomistic theory of matter was only in its infancy, and even the existence of atoms has not yet been demonstrated unequivocally. The fact of ignoring the moment of interaction derives from the common attitude of the practitioners to adopt ontology of events, instead of ontology of processes since in many domains of Physics, this ontology of events seems to be the only possible one, or at least the most convenient. Nevertheless, these laws remained untouched by all subsequent developments in physics, with the important qualifier “within the framework of applicability of its basic concepts.” This framework of applicability is known as the “quantum thermodynamic limit, ” the limit of a large number of particles when fluctuations are assumed to be small [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135] as a process of a temporal sequence of events that is ruled by some dynamical law which characterizes the process itself. This is exactly the structural content of QFT, as stressed and explained in [1,2,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,136,137,138], where it is clarified that the dynamics, expressed by the functions for the interacting fields also called Heisenberg fields, that defines and characterizes the theory under this study and manifests itself in the observable physical fields at the level of these Quantum Homeopathy translations. Events are thus the manifestations of the underlying Turing Machine Learning Ruled dynamics (the process). Moreover, this concept of entropy negativity that is central to quantum thermodynamics was further generalized to become the cornerstone of information theory [Shannon’s entropy [25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,139,140] and is currently considered to be one of the most important concepts in all of science, reaching far beyond physics [26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141]. For example, Black Hole Paradox Generalizations and Supersymmetric breaking foundations for Turing Machine Learning Ruled Calculations are ruled on by these implemented algorithms. Ontology of Quantum Homeopathy processes does not deny that Hidden Entanglement observations are about events, but hold that events are explained only in terms of the underlying process, and that the descriptions of these events and processes are somehow inseparable. The expression “ontology of processes” has been borrowed from information science, where it has been introduced within the context of space-temporal databases (see, for instance, Kuhn [28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144]). The conventional presentation of Quantum Thermodynamics starts with the analysis of thermal machines. However, a more recently promoted and apparently much deeper approach is based on the understanding of entropy as a measure of our knowledge (or, more accurately, our ignorance) of a quantized subsystem [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145]. A theoretical construction of cluster of Hypergeometric Functions to integralize chemical Block Systems developed here, where I found a model for quantum-computational gravity on a quantum space background; namely, the fuzzy sphere. A few words should also be spared for the important role played in this paper by the uir of the CCR in QFT. Thus, the choice of a particular representation in which to work reduces to a pure matter of convenience. The situation changes drastically when I consider systems with an infinite number of degrees of freedom. This is the case of QFT, where systems with a very large number of degrees of freedom are considered. In a sense, there is no entropy other than information entropy, and the loss of information resulting from summation over a subset of the degrees of freedom is the only necessary condition to derive the Gibbs distribution and hence all the laws of quantum thermodynamics [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,146,147,148,149,150,151,152,153,154]. In QM, i.e., for systems with a finite number of degrees of freedom, the choice of representation is inessential to the physics, since all the irreducible representations of the canonical commutation relations (CCR) are each unitarily equivalent: this is the content of the Stone-von Neumann theorem. Ludwig Boltzmann prophetically espoused the connection between entropy and biological evolution: “If you ask me about my inner most conviction whether our century will be called the century of iron or the century of steamor electricity, I answer without hesitation: it will be called the century of the mechanical view of nature, the century of Darwin” [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157]. In particular, in this reduction mechanism of QFT to QM illustrated in this paper, it is extremely important to take due account of the moment of interaction; that is, to assume an ontology of processes, as I will see in the Conclusions. More specifically, the link between thermodynamics and evolution of biological populations was clearly formulated for the first time by Ronald Fisher, the principal founder of theoretical population genetics [60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158]. Subsequently, extended efforts aimed at establishing detailed mapping between the principal quantities analyzed by thermodynamics, such as entropy, temperature, and free energy, and those central to population genetics, such as effective population size and fitness, were undertaken by Sellaand Hirsh [13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,159,160,161] and elaborated by Barton and Coe [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162]. Avoiding doing so would lead to an internal classical computational structure of QFT, which is itself of no real help in the simulation of the latter. It is therefore no surprise that many attempts have been made to apply concepts of thermodynamics to problems of biology, especially to population genetics and the theory of evolution. Theoretical research on quantum simulation of QFT is very urgent nowadays, because it should support important experimental applications, mainly in high energy physics (HEP), and also for setting up the fundaments of theoretical computer science (TCS) [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163]. The parallel is indeed clear: The smaller the ultra low drug doses the stronger are the effects of random processes (genetic drift), which in physics associates naturally with temperature increase. Among other attempts to conceptualize the relationships between quantum thermodynamics and evolution, of special notices the work of Frank [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165] on applications of the maximum entropy principle, according to which the distribution of any quantity in a large ensemble of entities tends to the highest entropy distribution subject to the relevant constraints (6). [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166] The mutual information SA1, SA2 − SA1 ∪ A2 measures the correlations between the two parts, but gives only an upper bound on the entanglement between them as a more useful measure of entanglement in the recent Transcriptome analysis of samples from COVID-19 patients [2,3,4,5,6,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167] providing an opportunity to correlate their findings with those obtained earlier when reporting ARBs anti-inflammatory and anti-aging protective effects [4,5,6,7,8,9,10,11,12,13,14,15,16,17,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168]. [2,3,4,5,6,7,8,9,10,11,12,13,17,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,64] Molecular docking uses a scoring function to evaluate the binding conformation obtained by each docking to predict binding affinity and uses significantly fewer computational resources. One corresponds to the pure electronic states of the interacted viral proteins involved in the Transcriptomic analysis of samples from COVID-19 patients and SARS-CoV-2-infected primary human alveolar cells where revealed unique inflammatory profiles with excessive inflammatory cytokine release [2,3,4,5,6,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169] and the other to the ones reported in the neuronal cultures injured by excitotoxic glutamate concentrations, a major injury factor in the brain that normalized by treatment with the ARB Candesartan [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170]. Quantum Mechanics (QM) has revolutionized the understanding of the structure and reactivity of small molecular systems in a large ensemble of ultra low drug dose entities that tend to the highest of negative entropy distribution subject to the relevant constraints. Molecular docking can thereby quickly calculate the binding affinity between multiple ligands and proteins and is commonly used for high-throughput virtual screening of drug molecules based on the protein structure. Nonetheless, the computational efficiency of molecular docking comes at the expense of accuracy. For example, QM based methods provide structural information in excellent agreement with experiment, match experimental barrier heights for chemical reactions, and provide chemically accurate interaction energies for hydrogen-bonded or dispersive systems.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66] Given the tremendous impact QM has had for ultra low drug dose systems I then hypothesized the partial transpose of HermiteH [n, x] HeunT [q, α, γ, δ, ϵ, z] LaguerreL [n, x] HeunT [q, α, γ, δ, ϵ, z] and then the logarithmic negativity as (1) where he refers to the trace norm ||ρT2|| of the pure electron transition states exctracted from the translation of the cytokine storm signal transduction of the SARS that follows pneumonia and other coronavirus infections. The attention mechanism [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,171] is one of the most powerful approaches to solve this problem. By introducing an attention mechanism, this prediction model can focus on features that are most relevant to the prediction target and at the same time improve the accuracy and interpretability of this drug designing model. [2,3,4,5,6,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172] The key Hypergeometric Functions refer to the geometry shaping of the atom orbitals and atomic charges during the signal transduction of these effects in cardiovascular and metabolic disorders that are frequently comorbid with COVID-19 including alterations in interferon responses, macrophage and neutrophil infiltration, increased apoptosis, and p53 signaling associated with lymphopenia. [13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173] In this study, in order to predict protein–ligand binding affinities and structures of proteins and ligands, Ι developed a Quantum Turing Machine Learning Deep Neural Network SEGSA_DTA, a SuperEdge Graph convolution-based and Supervised Attention-based Drug–Target Affinity prediction method. Super edge graph convolution was adopted to learn feature representations of both nodes and edges from graph structures of proteins and ligands. The sum E, ln||E (y) ρT2∇ ⊗ (Rx (π), [H] ⨶ [Ho] ⨚I) ⊕ CZeiπ/2 refers to pure Quantum states of the Angiotensin Receptor Blockers (ARBs) where ρT2 corresponds to the discrete particles that are bound in finite systems such as the protein-drug systems involved in the excessive activation of Angiotensin II AT1 receptors (AT1R). However, because of the small size of the structure dataset and the lack of detailed knowledge concerning protein–ligand interactions, most of the existing methods are not yet able to effectively learn the attention distribution and accurately capture the true interaction information between proteins and ligands, limiting the predictive performance.[16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,70] Several studies in the fields of visual question answering [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174] and natural language processing [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175] have demonstrated that training attention mechanism in a supervised manner can result in more effective attention distribution and improve model performance significantly, but its effectiveness in building a better protein–ligand binding affinity prediction model remains unclear. [16,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176] These E (y) Quantum Homeopathy wave effects will allow tunneling through an energy barrier for the generation of chemical mappings against the (AT1R) receptor which is a major injury factor and plays a significant role in the development of disorders of the brain, the cardiovascular system, the kidney, lipid and glucose metabolism and the immune system that are linearly associated with viral load and lung injury in COVID-19 patients [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177]. In recent years, Quantum Turing Machine Deep Learning has achieved great success in many fields such as computer vision, speech recognition and natural language processing. Deep learning is also widely used in numerous aspects of drug design,3 including de novo molecule design and chemical synthesis. Randomness is often an important resource in information processing. This is true even in the quantum regime, where quantum randomness is often represented by a random state, a quantum pure state that is drawn uniformly at random from a Hilbert space. Currently, most deep learning-based protein–ligand binding affinity prediction models, such as DeepDTA,4 DeepAffinity5 and DeepDTAF, only use protein and ligand sequence information to predict protein–ligand binding affinity. It is known that protein structure information is essential for protein-ligand binding, yet these sequence-based methods lack sufficient structure information. Alternatively, several studies have utilized co-crystal structures of protein–ligand complexes to predict binding affinity. Nevertheless, obtaining co-crystal structure in protein–ligand complexes is a time-consuming practice, and thus the protein structure-based model is still needed. Moreover, with the recent development of Quantum Homeopathy Neural Networks, the advantages of using Quantum Functions to represent proteins and ligands are gradually being highlighted in deep Turing Machine learning-based models. Accordingly, it makes sense to investigate the extent to which peculiarly quantum effects such as coherence and entanglement play an important role in living systems. [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180] The Turing Machine learning representation retains the structure information of proteins and ligands, [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,181] and quantum hypergeometric functions convolution neural network (GCN) are commonly used to learn the structure pattern. Before turning to these drug designing efforts, I will review the evidence for quantum effects in these hypertension biological mechanisms as [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,182] [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183] similar mathematical formalisms of QFT to QM Quantum Mechanics for Quantum Homeopathy reduction methodologies for the modeling biosystems of the molecular structures that are associated in these inflammations and viral injuries in the lung will be inserted as a protein code (cipher-protein), and be determined, and be prepared in heterodox interpretations [22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184] when solving the time-independent [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185] Schrödinger function over a Boson Field Operator for a Poly-Triangle Shaped Fragmentation Scheme. [26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186] However, these Schrödinger functions in Markovian and [27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187] non-Markovian scenarios cannot be solved for any but a one-data-driven [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188] electron system method (the hydrogen atom) a family of solutions of [28,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189] quantum functions and approximations need to be made [33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190] since most applications of these methods in protein systems have been largely limited to semi-empirical, HF, and DFT calculations. Among these approaches, FMO has been applied to higher level ab initio calculations such as second-order Møller−Plesset perturbation theory (MP2) and coupled cluster theory (CC) as Nakai and co-workers have described D&C-MP222, [25,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191] and D&C-CCSD38 approaches and applied them to linear or near-linear systems. QM excels at the static modeling of a molecular system however, in chemistry and biology, fluctuations are important to describe a broad range of effects. Thus, the ensemble picture of a molecular system is more appropriate, and while molecular dynamics (MD) methods are beginning to address dynamical issues using classical potentials, how to address this “sampling” issue when using more expensive QM based approaches which will need to be addressed in this Quantum Homeopathy approximation, as it is assumed that the atoms that are far away from the region of interest regarding only weakly influence local regions of a protein.