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
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
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
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
K SC The convergence of quantum computing and artificial intelligence represents one of the most consequential technological developments of the twenty-first century. This fourth volume examines the distinct yet interconnected trajectories of quantum-AI integration in three leading powers: the United States, China, and Germany. Through comparative analysis of national strategies, research ecosystems, industrial policy, and geopolitical positioning, the paper identifies three distinct models of convergence: the market-driven, defence-oriented model of the United States; the state-directed, civil-military fusion model of China; and the collaborative, standards-focused, and ethically oriented model of Germany within the European Union framework. These models are not merely technical choices but reflect deeper differences in political economy, epistemic cultures, and visions of global order. The analysis concludes with reflections on the implications of this tri-polar dynamic for smaller and middle powers, including the Global South, and outlines principles for more inclusive and stable quantum-AI governance. Also available on Academia.edu: https://www.academia.edu/attachments/133209089/download_file?s=portfolio
Background: The accelerating confluence of quantum computing, big data infrastructures, and advanced artificial intelligence (AI) models is reshaping the theoretical and practical contours of financial analytics, digital marketing, supply chain optimization, and educational infrastructures. Scholarly and practitioner literatures document significant advances across each domain — quantum architectures and algorithmic paradigms, expansive big data platforms and measurement regimes, and powerful AI systems including large language models and multimodal foundation models — yet integrated frameworks that articulate the combined potential and attendant challenges remain nascent (Preskill, 2023; Tosi et al., 2024; Hong et al., 2024). Objectives: This article synthesizes the extant literature to construct a conceptual and methodological scaffold for integrating quantum-enabled computation with big data and AI in financial and economic analysis. It clarifies problem spaces, articulates methodological choices conducive to responsible adoption, analyzes potential transformative outcomes across organizational practice and education, and identifies unresolved research gaps demanding priority attention. The approach emphasizes theoretical rigor, cross-disciplinary citation, and practical interpretive analysis to serve both academic researchers and senior practitioners. Methods: We employ a critical integrative review method that draws on systematic literature syntheses, cross-disciplinary theoretical elaboration, and analytic argumentation. The methodology combines thematic synthesis of provided references, translation of technical claims into descriptive analytical narratives, and normative discussion of implementation pathways supported by cited evidence (Tosi et al., 2024; Sardi et al., 2023). Findings: Quantum computing promises algorithmic speedups for classes of problems relevant to finance (optimization, sampling, and certain linear-algebraic subroutines), yet practical advantage depends on hybrid classical-quantum architectures and careful encoding of big data into quantum-accessible representations (Preskill, 2023; Rayhan & Shahana, 2023). AI advances, particularly in foundation models and multimodal systems, provide powerful representational tools that reshape feature engineering, natural language understanding, and scenario simulation for economic forecasting and M&A diligence (Arachchige et al., 2023; Hong et al., 2024). Big data infrastructures have matured over fifteen years, yet integration challenges — governance, measurement, and sustainable performance — persist and condition how quantum and AI systems can actually be deployed (Tosi et al., 2024; Sardi et al., 2023; Rashid et al., 2024). Conclusions: The transformative potential at the intersection of quantum, big data, and AI is real but contingent. Realizing benefits in finance and related domains requires: (1) methodological advances in encoding and hybrid computation, (2) governance frameworks for data quality and ethical AI, (3) workforce re-skilling oriented to new analyst roles, and (4) empirical research focusing on scaled field deployments and measurable outcomes. We conclude with a detailed research agenda and policy recommendations designed to accelerate responsible innovation while managing risks
The integration of quantum computing and artificial intelligence (AI) constitutes a bidirectional synergy that is reshaping both disciplines. This review investigates the reciprocal relationship in which AI addresses foundational challenges in quantum computing, while quantum computing offers the potential to advance machine learning beyond classical constraints. Recent advancements exemplify this interaction: Google DeepMind’s AlphaQubit neural network decoder has achieved state-of-the-art quantum error correction, improving performance by 6% over tensor networks and 30% over correlated matching methods. Additionally, quantum neural networks have demonstrated exponential improvements in sample complexity for specific learning tasks. This review systematically examines the current landscape of quantum-AI integration across three primary dimensions. First, it addresses AI-enhanced quantum systems, such as transformer-based error correction, reinforcement learning for circuit optimization, and AI-driven hardware calibration. Second, it explores quantum-accelerated machine learning algorithms, including variational quantum neural networks, quantum generative adversarial networks, and quantum reinforcement learning. Third, it evaluates industry deployments in sectors such as life sciences, financial services, climate modeling, and pharmaceutical development. Notable examples include IonQ’s quantum chemistry simulations, which achieved 40% efficiency improvements in carbon capture material design; St. Jude’s identification of KRAS protein inhibitors using quantum machine learning with experimental validation; and JP Morgan Chase’s implementation of quantum portfolio optimization. The analysis indicates that the field has advanced from preliminary demonstrations to production-grade applications, especially in drug discovery and molecular simulation, where quantum computing offers measurable benefits. Nevertheless, several challenges remain, including barren plateaus in variational algorithms, scalability constraints in current noisy intermediate-scale quantum (NISQ) devices, requirements for real-time decoding speed, and the necessity for fault-tolerant quantum computing (FTQC) systems. IBM’s 2029 roadmap, which targets 200 logical qubits capable of executing 100 million gates, together with progress in high-rate quantum low-density parity-check codes, outlines a trajectory toward practical fault tolerance. This review makes two principal research contributions: Comprehensive Evaluation Framework for Quantum Advantage Assessment: We establish the first systematic, integrated methodology for determining quantum computing feasibility that combines problem characterization, resource estimation, quantum advantage assessment, and quantum algorithm paradigm selection. This framework consolidates criteria scattered across academic literature and industry practice into a unified decision-making tool applicable across chemistry, optimization, machine learning, and simulation domains. The framework addresses a critical industry need: enabling non-expert practitioners (chemists, financial analysts, materials scientists) to objectively assess quantum computing suitability without requiring deep quantum expertise. Novel Quantum Resource Optimization Algorithms: We present three concrete algorithmic contributions advancing quantum-AI integration: • Data Encoding Efficiency Algorithm that automatically selects optimal qubit encoding strategies (amplitude vs. angle encoding) minimizing total quantum resource consumption • Error Budget Optimization Algorithm that iteratively determines optimal quantum error correction code distance balancing logical error rate targets against physical qubit overhead • Real-time Hardware Specification Aggregation from multiple quantum cloud platforms (IBM Quantum, Amazon Braket, Google, IonQ) enabling dynamic feasibility assessment as hardware capabilities evolve Combined, these contributions establish quantum computing advantage assessment as a rigorous, data-driven discipline rather than ad-hoc expert judgment. The framework enables strategic quantum computing investment decisions, accelerates problem identification for early quantum utility, and provides a reference methodology for standardizing quantum advantage evaluation across academia and industry. This review synthesizes insights from recent academic literature, industry implementations, and expert perspectives to deliver a comprehensive assessment of quantum-AI synergies grounded in this evaluation framework. The mathematical foundations, ranging from quantum Fourier transforms to Gaussian processes, are examined. Best practices for researchers and practitioners are also outlined. The findings suggest that, although universal quantum advantage has not yet been realized, domain-specific quantum-AI applications in chemistry, optimization, and sensing are achieving practical utility in 2024-2025. This development marks a significant transition from theoretical potential to commercial realization, informed by rigorous evaluation methodologies.