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 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 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
In the present Letter, we introduce, develop, and investigate a connection between multiphoton quantum interference, a core element of emerging photonic quantum technologies, and Hopfield-like Hamiltonians of classical neural networks, the paradigmatic models for associative memory and machine learning in systems of artificial intelligence. Specifically, we show that combining a system composed of N_{ph} indistinguishable photons in superposition over M field modes, a controlled array of M binary phase shifters, and a linear-optical interferometer, yields output photon statistics described by means of a p-body Hopfield Hamiltonian of M Ising-like neurons ±1, with p=2N_{ph}. We investigate in detail the generalized four-body Hopfield model obtained through this procedure, undergoing a transition from a memory retrieval to a memory black-out regime, i.e., a spin-glass phase, as the amount of stored memory increases. The mapping enables novel routes to the realization and investigation of disordered and complex classical systems via efficient photonic quantum simulators and describes aspects of structured photonic systems in terms of classical spin Hamiltonians.
Computational fluid dynamics (CFD) has long been limited by the extreme computational cost and multiscale complexity of the Navier-Stokes equations, particularly in turbulent regimes. We propose that geometric quantum physics-informed neural networks (GQPINNs) — symmetry-embedded variational quantum circuits that enforce finite-group ((K_4)) and compact Lie-group (SO(2), (\mathbb{Z}_2)) invariances via twirling of generator sets — constitute the foundational architecture for next-generation quantum-inspired artificial intelligence in CFD. Drawing on the mechanics of data re-uploading circuits, group-equivariant trainable blocks, and physics-informed loss functions, we show how GQPINNs achieve orders-of-magnitude gains in parameter efficiency and solution accuracy on representative PDE benchmarks while naturally extending to the incompressible and compressible Navier-Stokes system. Ontologically, the framework realises an onto-dynamic structural realism in which fluid structures (vorticity fields, streamlines, conserved quantities) are relationally individuated by symmetry Mamun, SM (2026) …………………………………….1 Navier-Stokes Turbulence as a Computational Laboratory for Next-Generation Quantum-Inspired Artificial Intelligence: Mechanics, Ontology, and Epistemology of Geometric Quantum Physics-Informed Neural Networks (Vol-I) K S C groups rather than by intrinsic point-like properties. Also available on Academia.edu: https://www.academia.edu/attachments/132750817/download_file?s=portfolio
Quantum optical networks are instrumental in addressing the fundamental questions and enable applications ranging from communication to computation and, more recently, machine learning (ML). In particular, photonic artificial neural networks (ANNs) offer the opportunity to exploit the advantages of both classical and quantum optics. Photonic neuro-inspired computation and ML have been successfully demonstrated in classical settings, while quantum optical networks have triggered breakthrough applications such as teleportation, quantum key distribution and quantum computing. We present a perspective on the state of the art in quantum optical ML and the potential advantages of ANNs in circuit designs and beyond, in more general, analogue settings characterized by recurrent and coherent complex interactions. We consider two analogue neuro-inspired applications, namely quantum reservoir computing and quantum associative memories, and discuss the enhanced capabilities offered by quantum substrates, highlighting the specific role of light squeezing in this context.This article is part of the theme issue 'The quantum theory of light'.
K SC This teaching note synthesises recent advances in superconducting quantum thermodynamics—specifically the world's first cyclic superconducting quantum heat engine (Aalto University, 2026) and the associated technologies of quantum-circuit refrigerators (QCRs) and autonomous quantum heat engines—with emerging concepts of thermal connectivity in neural networks and biological mitochondrial organisation as templates for post-silicon computing architectures. It examines how engineered heat flows, dissipation control, and gradient-driven energy conversion can serve simultaneously as computational resources, interconnect modalities, and organisational principles for artificial intelligence systems. The note is intended for researchers, policy makers, and advanced students working at the intersection of quantum technology, neuromorphic computing, thermodynamic computing, and bio-inspired AI. Also available on Academia.edu: https://www.academia.edu/attachments/133844967/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