Quantum-Centric Supercomputing and Photonic Bio-Quantum Architectures: An Exhaustive Evaluation of High-Fidelity Biomolecular Simulation and Mitochondrial Quantum Dynamics (Vol-II)
This paper evaluates the breakthrough in computational biophysics marked by the successful ab initio quantum simulation of protein complexes up to 12,635 atoms, achieved through a heterogeneous Quantum-Centric Supercomputing (QCSC) paradigm. By leveraging advanced quantum processors (IBM Heron r2) and classical HPC (Miyabi-G) with the novel EWF-TrimSQD algorithm, researchers surpassed previous simulation size limits by over 40-fold, enabling accurate calculation of fragment energies and ligand-binding affinities for systems like Trypsin-benzamidine. Beyond drug Mamun, SM (2026) ……………………………….1 Quantum-Centric Supercomputing and Photonic Bio-Quantum Architectures: An Exhaustive Evaluation of High-Fidelity Biomolecular Simulation and Mitochondrial Quantum Dynamics (Vol-II) K S C discovery, this capacity provides the empirical foundation for rigorous investigation into quantum biology. The paper details non-classical phenomena, including proton tunneling in ribosomal peptide bond formation and the highly efficient electron/proton tunneling within the mitochondrial electron transport chain (ETC), highlighting how oxidative damage to these quantum pathways drives neurodegenerative pathology. Also available on Academia.edu: https://www.academia.edu/attachments/132704084/download_file?s=portfolio
This paper evaluates the breakthrough in computational biophysics marked by the successful ab initio quantum simulation of protein complexes up to 12,635 atoms, achieved through a heterogeneous Quantum-Centric Supercomputing (QCSC) paradigm. By leveraging advanced quantum processors (IBM Heron r2) and classical HPC (Miyabi-G) with the novel EWF-TrimSQD algorithm, researchers surpassed previous simulation size limits by over 40-fold, enabling accurate calculation of fragment energies and ligand-binding affinities for systems like Trypsin-benzamidine. Beyond drug Mamun, SM (2026) ……………………………….1 Quantum-Centric Supercomputing and Photonic Bio-Quantum Architectures: An Exhaustive Evaluation of High-Fidelity Biomolecular Simulation and Mitochondrial Quantum Dynamics (Vol-II) K S C discovery, this capacity provides the empirical foundation for rigorous investigation into quantum biology. The paper details non-classical phenomena, including proton tunneling in ribosomal peptide bond formation and the highly efficient electron/proton tunneling within the mitochondrial electron transport chain (ETC), highlighting how oxidative damage to these quantum pathways drives neurodegenerative pathology. Also available on Academia.edu: https://www.academia.edu/attachments/132704084/download_file?s=portfolio
This academic review synthesizes the methodological breakthrough of the May 2026 quantum-centric supercomputing (QCSC) milestone, which utilized the Embedded Wave Function (EWF) and Trimmed Sample-based Quantum Diagonalization (TrimSQD) algorithmic framework to achieve coupled-cluster-level accuracy in simulating protein-ligand complexes exceeding 12,000 atoms. This unprecedented computational capability provides the rigorous analytical foundation for quantum biology, specifically validating the indispensable role of quantum tunnelling in mitochondrial bioenergetics. Atomistic calculations confirm that electron tunnelling in Respiratory Complex III is governed by highly optimized superexchange pathways and conformational gating. Concurrently, mathematical modelling using the Wentzel-Kramers-Brillouin (WKB) approximation demonstrates that proton tunnelling through Complex I substantially reduces the required Mamun, SM (2026) ……………………………….1 Quantum-Centric Supercomputing and Quantum Tunnelling in Mitochondrial Bioenergetics: An Expanded Academic Review of Biomolecular Simulation Milestones and Photonic Bio-Quantum Horizons (Vol-III) K S C thermal activation energy for chemiosmosis by up to 45%, a finding supported by real-time optical detection using bio-nanoantennae. Also available on Academia.edu: https://www.academia.edu/attachments/132704155/download_file?s=portfolio
This review critically examines the paper by Mamun (2026) on fractal-quantum architectures in exascale computational biology, which proposes the integration of the Cerebras Wafer-Scale Engine 3 (WSE-3) with non-Markovian dynamics and autopoietic cybernetics to simulate complex biological systems. Key salient points are summarized, including theoretical foundations, hardware architecture, quantum simulations, and future implications. Comparisons with existing research highlight alignments with fractal patterns in quantum biology and non-Markovian models in biochemical reactions, while contrasting the speculative nature of autopoietic AI with empirical advancements in multi-agent systems. The review evaluates strengths in innovative synthesis and weaknesses in empirical validation. Extensions are proposed into quantum error correction, drug discovery applications, ethical considerations, and hybrid hardware integrations. This coherent framework underscores the potential for transformative simulations while advocating cautious, evidence-based development. Mamun, SM & Grok xAI (2026)……………………………………..1 Advancing Exascale Computational Biology: A Critical Review and Extension of Fractal-Quantum Architectures Integrating WSE-3, Non-Markovian Dynamics, and Autopoietic Systems Also available on Academia.edu: https://www.academia.edu/attachments/131657978/download_file?s=portfolio
The escalating global demand for data storage, projected to reach 1024 bits by 2040, is rapidly outpacing the capacity of classical silicon-based memory, exposing an infrastructural crisis. Synthetic Deoxyribonucleic Acid (DNA) is the premier ultra-high-density archival candidate, offering 1018 bits per cubic centimeter, but its enterprise-scale adoption is severely limited by biochemical (imperfect synthesis, PCR biases) and computational bottlenecks (intractable encoding combinatorics, the Data Loading Problem) inherent to classical architectures. This report details how the strategic integration of Quantum-AI hybrid algorithms—specifically Variational Quantum Eigensolvers (VQE), Quantum Neural Networks (QNNs), and Artificial General Intelligence (AGI) orchestration—provides the necessary computational leverage to overcome these historic limitations. Hybrid frameworks like Quantum-DNA Image Compression (Q-DIC) utilize NISQ-compatible VQEs to enforce hardware Mamun, SM (2026) …………………………….1 Quantum-AI Hybrid Algorithms: Bridging Classical Intelligence, Quantum Acceleration, and Biological Data Storage (Vol-II) K S C synthesis constraints and achieve significant compression, while the 2026 breakthrough of Quantum Oracle Sketching bypasses the need for Quantum Random Access Memory (QRAM) by processing petabyte-scale data streams "on the fly". Also available on Academia.edu: https://www.academia.edu/attachments/132470386/download_file?s=portfolio
This analysis addresses the critical decoherence paradox in quantum biology, where observed microsecond coherence times in living cells vastly exceed the nanosecond predictions of standard open quantum systems theory. The study synthesizes theoretical and empirical advancements, establishing that the information geometry of biological macromolecules provides the mechanism for long-lived quantum states, where cellular structures function as optimized topological constraints that actively harness environmental noise. Key theoretical frameworks integrated include the Quantum Fisher Information (QFI) metric, which quantifies biological coherence and sets absolute limits on sensory precision (e.g., avian magnetoreception), and the Entanglement-Weighted Operator Geometry (EWOG). EWOG is empirically validated to resolve the decoherence discrepancy by demonstrating that quantum entanglement dynamically modifies the effective Hamiltonian, achieving a calculated 58% suppression of decoherence in proteins like MagLOV2. Furthermore, the study applies Quantum Geometrical Molecular Dynamics (QGMD) to show that the non-Abelian quantum geometric tensor drives nonadiabatic enzymatic catalysis, enabling quantum mechanical tunneling to provide profound energetic advantages, such as reducing the required energy for proton transport in mitochondrial Complex I by over 55%. Also available on Academia.edu: https://www.academia.edu/attachments/132032425/download_file?s=portfolio
We develop a workflow decomposing a molecule into fragments via quantum embedding and simulating them with a heterogeneous quantum-classical (HQC) method. We sample fragment electronic configurations on two 156-qubit quantum processors (ibm_cleveland, ibm_kobe), using up to 94 qubits, running 21,006 circuits for over 239 hours, collecting 3.0⋅109 measurement outcomes -- the most resource-intensive HQC computation for quantum chemistry to date. We compute fragment wavefunctions via optimized subspace diagonalization on supercomputers Fugaku, Miyabi-G, and ROQUO, achieving 72.5% parallel efficiency with scalable distributed linear algebra kernels. We simulate two protein-ligand complexes spanning dispersion- and electrostatics-dominated regimes (11,608, 12,635 atoms), demonstrate >40× increase in system size and up to 210× improvement in accuracy over previous state-of-the-art, with HQC matching coupled-cluster (CCSD) accuracy in fragment energies. We present the first HQC protein-ligand binding prediction using a mixed-basis set and an automated end-to-end workflow enabling practical HQC calculations of large protein systems.
This second volume systematically dissects advanced computational paradigms in quantum photonic computation, shifting the field from theoretical proofs-of-concept to complex, integrated hardware architectures. The work explores fundamental breakthroughs in scaling associative memory, including using multiphoton quantum interference to realize higher-order generalized Hopfield networks and exploiting driven-dissipative cavity quantum electrodynamics with atomic motion to achieve dynamic synaptic plasticity and surpass the theoretical Hopfield capacity limit. To address commercial viability, the volume analyzes the implementation of defect resilience through non-Abelian topological phenomena on photonic integrated circuits (PICs), as well as the development of quantum-limited stochastic optical neural networks that use intrinsic noise as regularization to achieve extreme energy efficiency. Furthermore, it details the heterogeneous integration of electronic-photonic hybrid accelerators (e.g., ADEPT and MIRAGE) for foundry-scale deployment. Finally, the volume investigates deployment in complex, Advanced Paradigms in Quantum Photonic Computation: Comprehensive Architectures, Topological Resilience, and Real-World Applications (Volume II) real-world applications, such as solving NP-hard combinatorial optimization problems in supply chain logistics and pioneering non-pharmacological neuro-lymphaphotonic interventions for Alzheimer's disease. Also available on Academia.edu: https://www.academia.edu/attachments/132159735/download_file?s=portfolio