Melanie Swan

Background: Alzheimer’s disease affects one in ten people older than 65 years, with the prevalence expected to triple by 2030, together with commensurate increases in cost. A comprehensive causal model of Alzheimer’s disease etiology is lacking despite more than three decades of study. Modern computational methods provide a new approach to multiscalar biosystems by modeling neuropathology in its native three‐dimensional character in topological biophysics and quantum computational models.Method: This work describes how an emerging standard in machine learning, responsible for the AlphaFold (protein folding structure) and Gato generalist agent projects, transformer neural networks, may be applied to the study of Alzheimer’s disease per proven results in bot text chat, image recognition and creation, video generation, software programming, and autonomy in robotics and driving. The self‐attention and self‐learning mechanisms of transformer neural networks are discussed in application to multifaceted Alzheimer’s disease data sets involving neuroimaging scans, whole human genome data, and blood and CSF biomarker data.Result: A machine learning‐based topological biophysics model of Alzheimer’s disease neuropathology hypothesis‐testing (misfolded proteins, gene expression, diabetes type‐3, and peptide immune system stimulation) is described.Conclusion: There is an opportunity to apply state‐of‐the‐art transformer neural network machine learning techniques to the automated analysis of large accruing data stores of Alzheimer’s information (imaging, genomic, proteomic, and self‐reported data) towards the causal understanding of the neuropathology.

Quantum matter (novel phases of matter at zero temperature with exotic properties) is a growing field with applications in its own domain, and in providing foundational support to quantum sciences fields more generally. The ability to characterize and manipulate matter at the smallest scales continues to advance in fundamental ways. This review provides a plain-language, non-technical description of contemporary activity in quantum matter for a general science audience, and an example of these methods applied to quantum neuroscience. Quantum matter is the study of topologically governed phases of matter at absolute zero temperature that exhibit new kinds of emergent order and exotic properties related to topology and symmetry, entanglement, and electronic charge and magnetism, which may be orchestrated to create new classes of materials and computational devices (including in the areas of spintronics, valleytronics, and quantum computing). The paper is organized to discuss recent developments in quantum matter on the topics of short-range topologically protected materials (namely, topological semimetals), long-range entangled materials (quantum spin liquids and fractional quantum Hall states), and codes for characterizing and controlling quantum systems. A key finding is that a shift in the conceptualization of the field of quantum matter may be underway to expand the core focus on short-range topologically protected materials to also include geometry-based approaches and long-range entanglement as additionally important tools for the understanding, characterization, and manipulation of topological materials.

Quantum neurobiology is concerned with potential quantum effects operating in the brain and the application of quantum information science to neuroscience problems, the latter of which is the main focus of the current paper. The human brain is fundamentally a multiscalar problem, with complex behavior spanning nine orders of magnitude-scale tiers from the atomic and cellular level to brain networks and the central nervous system. In this review, we discuss a new generation of bio-inspired quantum technologies in the emerging field of quantum neurobiology and present a novel physics-inspired theory of neural signaling (AdS/Brain (anti-de Sitter space)). Three tiers of quantum information science-directed neurobiology applications can be identified. First are those that interpret empirical data from neural imaging modalities (EEG, MRI, CT, PET scans), protein folding, and genomics with wavefunctions and quantum machine learning. Second are those that develop neural dynamics as a broad approach to quantum neurobiology, consisting of superpositioned data modeling evaluated with quantum probability, neural field theories, filamentary signaling, and quantum nanoscience. Third is neuroscience physics interpretations of foundational physics findings in the context of neurobiology. The benefit of this work is the possibility of an improved understanding of the resolution of neuropathologies such as Alzheimer’s disease.

Quantum computing is implicated as a next-generation solution to supplement traditional von Neumann architectures in an era of post-Moores law computing. As classical computational infrastructure becomes more limited, quantum platforms offer expandability in terms of scale, energy-consumption, and native three-dimensional problem modeling. Quantum information science is a multidisciplinary field drawing from physics, mathematics, computer science, and photonics. Quantum systems are expressed with the properties of superposition and entanglement, evolved indirectly with operators (ladder operators, master equations, neural operators, and quantum walks), and transmitted (via quantum teleportation) with entanglement generation, operator size manipulation, and error correction protocols. This paper discusses emerging applications in quantum cryptography, quantum machine learning, quantum finance, quantum neuroscience, quantum networks, and quantum error correction.

The aim of this paper is to propose a theoretical construct, smart network field theory, for the characterization, monitoring, and control of smart network systems. Smart network systems are intelligent autonomously-operating networks, a new form of global computational infrastructure that includes blockchains, deep learning, and autonomous-strike UAVs. These kinds of large-scale networks are a contemporary reality with thousands, millions, and billions of constituent elements, and entail a foundational and theoretically-robust model for their design and operation. Hence this work proposes smart network field theory, drawing from statistical physics, effective field theories, and model systems, for criticality detection and fleet-many item orchestration in smart network systems. Smart network field theory falls within the broader concern of technophysics (the application of physics to the study of technology), in which a key objective is deriving standardized methods for assessing system criticality and phase transition, and defining interim system structure between the levels of microscopic noise and macroscopic labels. The farther implications of this work include the possibility of recasting the P/NP computational complexity schema as one no longer based on traditional time (concurrency) and space constraints, due to the availability of smart network computational resources.