The intersection of Artificial General Intelligence (AGI) and utility-scale Quantum Computing with the Mathematical and Physical Sciences (MPS) has entered a phase of radical and immediate acceleration, defying prior baseline expectations. Observational data and advanced forecasting models synthesized between late 2025 and April 2026 necessitate a recalibration, as the mass deployment of fault-tolerant quantum computing is targeted for August 2027, coinciding with the emergence of functionally agentic AGI systems (the Superhuman Coder and Superhuman AI Researcher) between late 2026 and November 2027. This report synthesizes this eighteen-month convergence (April 2026 to October 2027) using the rigorous mathematical framework of Information Geometry, which maps the evolutionary trajectory of scientific knowledge networks and parameterizes the learning dynamics Mamun, SM (2026) ……………………………………….1 Accelerating the Symbiosis: Information Geometry, Quantum Computing, and Pre-2030 AGI in the Mathematical and Physical Sciences (Vol-II) K S C of deep neural architectures. The analysis evaluates the cascading effects of policy apparatuses like the US Genesis Mission and the AI Action Plan, demonstrating how the integration of agentic AGI logic engines with quantum computational substrates will fundamentally transform scientific methodology, leading to superexponential growth in discovery. Also available on Academia.edu: https://www.academia.edu/attachments/132469996/download_file?s=portfolio
The intersection of Artificial General Intelligence (AGI) and utility-scale Quantum Computing with the Mathematical and Physical Sciences (MPS) has entered a phase of radical and immediate acceleration, defying prior baseline expectations. Observational data and advanced forecasting models synthesized between late 2025 and April 2026 necessitate a recalibration, as the mass deployment of fault-tolerant quantum computing is targeted for August 2027, coinciding with the emergence of functionally agentic AGI systems (the Superhuman Coder and Superhuman AI Researcher) between late 2026 and November 2027. This report synthesizes this eighteen-month convergence (April 2026 to October 2027) using the rigorous mathematical framework of Information Geometry, which maps the evolutionary trajectory of scientific knowledge networks and parameterizes the learning dynamics Mamun, SM (2026) ……………………………………….1 Accelerating the Symbiosis: Information Geometry, Quantum Computing, and Pre-2030 AGI in the Mathematical and Physical Sciences (Vol-II) K S C of deep neural architectures. The analysis evaluates the cascading effects of policy apparatuses like the US Genesis Mission and the AI Action Plan, demonstrating how the integration of agentic AGI logic engines with quantum computational substrates will fundamentally transform scientific methodology, leading to superexponential growth in discovery. Also available on Academia.edu: https://www.academia.edu/attachments/132469996/download_file?s=portfolio
The 2025 NSF Workshop community paper on the Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS) articulates a strategic vision for a bidirectional "two-way street" between AI and the MPS domains (astronomy, chemistry, materials research, mathematical sciences, and physics). It emphasises AI as a powerful accelerator of scientific discovery while positioning MPS insights—particularly physical laws, symmetries, sparse/noisy data handling, and rigorous uncertainty quantification—as essential for advancing robust, interpretable, and efficient AI systems (Ferguson et al., 2026). This paper redrafts and extends that community perspective under accelerated technological timelines: mass deployment of quantum computing by August 2027 and achievement of artificial general intelligence (AGI) substantially before 2030. These assumptions compress implementation windows, transforming long-term recommendations into near-term imperatives. The analysis preserves the original paper's three-pillar strategic vision (bidirectional research, interdisciplinary community-building, and education/workforce development) while introducing hybrid quantum-AI-MPS integration as a fourth critical pillar. Also available on Academia.edu: https://www.academia.edu/attachments/132469784/download_file?s=portfolio
Quantum-AI hybrid algorithms represent the most practical near-term pathway for leveraging quantum computing's probabilistic power alongside classical AI's scalability and optimization strengths. In the noisy intermediate-scale quantum (NISQ) era, these hybrids—primarily variational quantum algorithms (VQAs) such as the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA)—delegate hard subproblems (e.g., high-dimensional sampling, optimization under quantum constraints, or feature extraction in entangled spaces) to quantum processors while classical optimizers or neural networks handle parameter tuning, error mitigation, and large-scale data processing (Ferguson et al., 2026; Acampora, 2026). This exploration builds on the 2025 NSF AI+MPS community paper by integrating accelerated timelines: mass deployment of utility-scale quantum computing by August 2027 and AGI substantially before 2030. Hybrid approaches become critical enablers for MPS grand challenges in Mamun, SM (2026)…………………………………………1 Quantum-AI Hybrid Algorithms: Bridging Classical Intelligence, Quantum Acceleration, and Scientific Discovery in the MPS Domains (Vol-I) chemistry (molecular ground states), materials (inverse design), physics (many-body simulation), and astronomy (anomaly detection under uncertainty). Also available on Academia.edu: https://www.academia.edu/attachments/132469868/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 paper employs fractal mathematics principles to forecast Q-Day—the threshold at which fault-tolerant quantum computers can practically compromise public-key cryptography—over an 18-month horizon from December 2025. Synthesizing insights from recent advancements in quantum ecosystems, algorithmic hybrids for nonlinear matrix operations, and interdisciplinary frameworks integrating quantum entanglement with fractal geometries in high-dimensional computing, the analysis models technological progress as self-similar iterative processes. Utilizing iterated function systems and fractional dimensions (approximately 1.618, akin to the golden ratio), the fractal model projects accelerated convergence toward utility-scale systems, predicting Q-Day by late 2027 with a 70% probability within the specified timeframe. Key drivers include the end of the Noisy Intermediate-Scale Quantum (NISQ) era, polylogarithmic speedups in quantum algorithms, and recursive self-improvement in autopoietic multi-agent systems. Implications encompass urgent transitions to post-quantum cryptography, geopolitical divergences in investment strategies, and existential risks from quantum-AI synergies, advocating for coordinated policies in commercialization, international collaboration, and ethical regulation to mitigate vulnerabilities while harnessing transformative potential in cybersecurity, healthcare, and environmental monitoring. Also available on Academia.edu: https://www.academia.edu/attachments/125804561/download_file?s=portfolio
This paper provides a rigorous academic review of the seminal work by Mamun and Grok xAI (2025) on advancing quantum matrix computations through the integration of geometric mean subroutines and Chebyshev-accelerated chain multiplications for nonlinear and iterative challenges in machine learning and quantum information. Following a thorough summary of its salient contributions, we compare and contrast the approach with contemporary research in quantum genetic algorithms (QGAs), variational quantum eigensolvers (VQEs), and tensor network methods. Strengths in polylogarithmic complexity and hybrid fault-tolerance are highlighted, alongside limitations in NISQ-era decoherence. Extending the framework, we propose novel integrations with fractal-recursive ontologies for non-integer dimensional simulations, the Principle of Co-Adaptation for bivariational dynamics in adaptive quantum circuits, and Souriau's axiomatic geometric model for positive-type state representations in high-dimensional entanglement. These extensions unify quadratic tensor structures with emergent fractal geometries, enabling scalable applications in quantum gravity simulations and autopoietic cognitive systems. Implications for post-quantum cryptography and geopolitical quantum strategies are discussed, advocating for interdisciplinary policies to harness transformative potential while mitigating risks. Also available on Academia.edu: https://www.academia.edu/attachments/131744773/download_file?s=portfolio
As artificial intelligence compute demands push silicon CMOS architectures toward insurmountable thermodynamic limits, the search for post-silicon computational paradigms has become an existential imperative. This paper presents an exhaustive analysis of emerging computational paradigms spanning room-temperature thermodynamic sampling units, superconducting quantum circuit heat engines, and living biological organoid intelligence. We examine how non-trivial quantum biological phenomena—including environment-assisted quantum transport (ENAQT), enzymatic quantum tunneling, and open-system DNA proton dynamics—enable biological neural networks to achieve radical energy efficiency and sample efficiency. Furthermore, we deploy a closed-system game-theoretic framework to model the strategic behavior of state actors and tech conglomerates competing in the post-silicon intelligence race, highlighting the bio-quantum breakaway strategy and the geopolitical, ethical, and dual-use implications of wetware computing. KSCCN Mamun, SM (2026) …………………….…1 KSCCN The Architecture of Post-Silicon Intelligence: Quantum Thermodynamics, Biological Computing, and the Game-Theoretic Race for the Next Computational Paradigm Also available on Academia.edu: https://www.academia.edu/attachments/133849341/download_file?s=portfolio