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
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
This article explores the groundbreaking intersection of quantum optics and neural network models, focusing on how multiphoton quantum interference in photonic circuits can simulate associative memory akin to the human brain. Drawing from recent research, we delve into the technical mechanisms, memory limits, and phase transitions observed in these systems. We employ geometric interpretations to elucidate the underlying ideas, simulate a model demonstrating latent memory in photon-like states for AI applications, and propose avenues for future research. The discussion covers historical context, experimental implications, energy efficiency advantages, potential challenges such as scalability and noise, and broader impacts on artificial intelligence, physics simulations, and neuromorphic hardware. Mamun, SM (2026)………………………1 Quantum Photonic Associative Memory: Multiphoton Interference as a Pathway to Brain-Inspired Computing (Vol-I) Also available on Academia.edu: https://www.academia.edu/attachments/132157923/download_file?s=portfolio
This paper explores the transformative intersection of quantum computing and artificial intelligence, a synergy poised to redefine the limits of computational power. As classical deep learning approaches the "scaling wall," quantum-enhanced architectures offer a path forward through superior optimization and high-dimensional data processing. Abstract / Research Description The Quantum AI Convergence provides a comprehensive technical audit of how quantum mechanics is being integrated into modern machine learning workflows. The research transitions from theoretical foundations to practical enterprise deployment, focusing on three core pillars: 1. Hybrid Quantum-Classical Architectures The study analyzes the current state of Noisy Intermediate-Scale Quantum (NISQ) devices. It explores the efficacy of Quantum Neural Networks (QNNs) and Variational Quantum Eigensolvers (VQE), demonstrating how hybrid models utilize classical hardware for backpropagation while leveraging quantum circuits for complex feature mapping. 2. Enterprise Scaling and Application Beyond the laboratory, the paper investigates high-impact industry use cases where quantum advantage is becoming tangible: Pharmaceuticals: Accelerating molecular docking and drug discovery through quantum-enhanced simulations. Finance: Optimizing multi-variable portfolios and risk assessment models that exceed the capacity of Monte Carlo simulations. Logistics: Solving NP-hard optimization problems in global supply chain routing. 3. The Post-Quantum Paradigm As AI models become more powerful, the research addresses the critical security "double-edged sword." It evaluates the necessity of Post-Quantum Cryptography (PQC) to protect sensitive AI training data against future Shor’s algorithm-based attacks, ensuring that the evolution of intelligence does not come at the cost of absolute privacy. Key Technical Themes Quantum Supremacy vs. Quantum Advantage: Distinguishing between theoretical milestones and practical business value. Tensor Networks: Their role as a bridge between classical deep learning and quantum state representations. Algorithmic Efficiency: Comparing the complexity classes of classical vs. quantum kernels, specifically focusing on Grover’s and HHL algorithms. Research Objective: To provide a roadmap for CTOs and researchers to transition from "Quantum Curious" to "Quantum Ready," establishing a framework for integrating quantum-enhanced AI into existing enterprise tech stacks.
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 volume explores the potential of quantum technologies. The book features insightful perspectives on quantum mechanics, computational paradigms, and practical applications, and it explores key concepts such as quantum entanglement, adiabatic computing, and quantum algorithms. Readers will discover how quantum systems are revolutionizing fields like cryptography, materials science, and artificial intelligence, promising faster computations and significant advances. Through a blend of theoretical exploration and practical insights, this book navigates the complexities of quantum communication, error correction, and the development of scalable quantum architectures. Ideal for researchers, students, and enthusiasts alike, it offers a gateway to understanding the current capabilities and future directions of quantum computing, demonstrating its potential for transforming technical developments and pushing scientific frontiers.
This Volume VII explores quantum-enhanced and fractal-inspired approaches to mathematical discovery as an advanced frontier that extends beyond the classical neuro-symbolic hybrids examined in Volume III. Building on the series' commitment to preserving human insight while pursuing rigorous foundations, it examines two complementary lines of inquiry. First, it surveys quantum algorithms for search and optimisation over mathematical spaces—including Grover-type search, quantum walks, and variational quantum eigensolvers applied to proof or conjecture landscapes—and assesses their potential to accelerate exploration of vast, high-dimensional mathematical structures while identifying fundamental limitations arising from noise, decoherence, and the encoding problem. Second, it investigates fractal and self-similar architectures for conjecture generation, drawing on scale-invariant and recursive structures that appear across number theory, geometry, and dynamics, and considers how such architectures might generate conjectures with intrinsic multi-scale coherence. The volume then analyses the deeper epistemic implications of non-classical computation for notions of proof, surveyability, and understanding that have been central to the Leiden Declaration and previous volumes in this series. Also available on Academia.edu: https://www.academia.edu/attachments/133179980/download_file?s=portfolio