SARS coronavirus 2 (SARS-CoV-2) in the viral spike (S) encoding a SARS-COV-2 SPIKE D614G mutation protein predominate over time in locales revealing the dynamic aspects of its key viral processes where it is found, implying that this change enhances viral transmission. In this paper, we strongly combine topology geometric methods for generalized formalisms of k-nearest neighbors as a Tipping–Ogilvie and Machine Learning application within the quantum computing context targeting the atomistic

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... el of the protein apparatus of the SARS-COV-2 viral characteristics. In this effort, we propose computer-aided rational drug design strategies efficient in computing docking usage, and powerful enough to achieve very high accuracy levels for this in-silico effort for the generation of AI-Quantum designed molecules of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM ligands targeting the COVID-19-SARS-COV-2 SPIKE D614G mutation by unifying Molecular Pairs (MMP), Lindenbaum-Tarski logical spaces and Adaptive Weighted KNN Positioning for Matched Bemis and Murko (BM) driven eigenvalue statements into Shannon entropy quantities as composed by Tipping–Ogilvie driven Machine Learning potentials on a (DFT) ℓneuron(ι):=φ∘D∘R2∘S∘R1 𝑐0𝜁2(1+∑𝑖)=[A∧A'(p)] ⊗[∂vˆ∂qˆ,∂uˆ∂pˆ] − [∂vˆ⊗∂pˆ,⊗∂uˆ⊗∂qˆ]=1𝑐𝑖𝜉𝑖{−ℏ20<≡===〈ψ⁎∣∣ψ⁎〉=[A∧A'(p)] ↦(𝑐𝑜𝑠𝜙−𝑠𝑖𝑛𝜙𝑠𝑖𝑛𝜙𝑐𝑜𝑠𝜙) (𝑥𝑝),𝐷(𝛼):(𝑥𝑝)↦(𝑥+𝑅𝑒(𝛼)𝑝+𝐼𝑚(𝛼)),𝑆(𝑟): (𝑥𝑝)↦(𝑒−𝑟00𝑒𝑟)(𝑥𝑝),𝐵𝑆(𝜃) :𝑥1𝑥2𝑝1𝑝2↦𝑐𝑜𝑠𝜃𝑠𝑖𝑛𝜃−𝑠𝑖𝑛𝜃𝑐𝑜𝑠𝜃𝑐𝑜𝑠𝜃𝑠𝑖𝑛𝜃00−𝑠𝑖𝑛𝜃𝑐𝑜𝑠𝜃𝑥1𝑥2𝑝1𝑝2𝑣=0,1,2....𝜆=1/2+𝛾2+1/4𝑐1⟨ψi∣∣(𝟙ˆ−ρˆ0)∣∣ψj⟩[A∧A'(p)] ↦(𝑐𝑜𝑠𝜙−𝑠𝑖𝑛𝜙𝑠𝑖𝑛𝜙𝑐𝑜𝑠𝜙) (𝑥𝑝),𝐷(𝛼):(𝑥𝑝)↦(𝑥+𝑅𝑒(𝛼)𝑝+ j<i⟨ψi∣∣ρˆ0∣∣ψj⟩𝑎22vKS1(r)a 4cos2θ+a2r2+r4) Σ3'⟨ψi∣∣ρˆ0∣∣ψj⟩𝑎22vKS1(r)=vext(r)+∫dr′ρ(r′)∣∣r−r′∣∣+ vXC1,(r)𝑚𝑏2𝑟2𝑒𝑑2πˆ2B2mB∇ [𝑃‾‾√𝑄‾‾(𝑟𝑟𝑦𝑣Γ(𝑏+𝑣)Γ(𝑐)𝑣!𝑐0𝑎2√−‾‾√𝑄‾‾(𝑟𝑟𝑦𝑣Γ(𝑏+𝑣)Γ(𝑐)𝑣!𝑐0𝑎2𝑄‾‾√𝑃‾‾√]≡∣CmdπCdπ2∣ϕ(Wxˆ+α)⟩, 𝑑𝑦2ΣΣ¯Ǫ ⊗ ¯σ − ¯σσ¯ǫ −i_+𝑐0𝑎2[1−𝑦𝑦]2}𝜓(𝑦)𝑣) improver for Chern-Simons Topology Euclidean Geometrics.