Quantum-inspired optimization (QIO) has emerged as a promising paradigm for solving complex combinatorial problems in industrial scheduling. While most existing studies focus on algorithmic developments and classical implementations, recent advances in nanoscale computing, including memristor-based Ising machines, photonic computing systems, and oscillatory neural networks, have created new opportunities for implementing QUBO- and Ising-based optimization through physical hardware dynamics. This survey provides a comprehensive review of QIO techniques for industrial scheduling, with a particular emphasis on their compatibility with emerging nanoscale computing platforms. We first introduce how representative scheduling problems, including job shop, flexible job shop, and resource-constrained scheduling, can be reformulated into quadratic unconstrained binary optimization (QUBO) models. We then categorize QIO methods into annealing- and Ising-based methods, evolutionary and population-based methods, hybrid quantum-classical and decomposition methods, and emerging learning-augmented approaches, and examine their computational characteristics and compatibility with QUBO or Ising hardware. Beyond algorithmic perspectives, this work highlights the potential role of specialized Ising hardware in enabling highly parallel and potentially energy-efficient optimization. We discuss how QUBO and Ising formulations can be mapped to physical energy-minimization processes in nanoscale devices, thereby providing a computational framework that connects classical optimization, quantum-inspired algorithms, and nanoscale hardware systems. Finally, we identify key challenges, including scalability, penalty calibration, and system integration, and outline future research directions toward hybrid quantum-classical computing ecosystems incorporating nanoscale optimization hardware. This survey positions QIO as a promising paradigm for next-generation intelligent manufacturing, in which optimization algorithms and nanoscale hardware are jointly developed to support low-latency and energy-efficient decision-making.
Vasculature-linked diseases (VLDs) alter vascular geometry, flow, and blood composition, motivating sensing strategies that can interrogate vascular conditions beyond passive contrast enhancement. This perspective article presents nanoparticle tomography (NPT) as a general sensing framework with the Internet of Bio-Nano Things (IoBNT), which interprets nanoparticle (NP) assisted vascular examination as an inverse problem. In this regard, NPs are treated as transport probes, and disease-related changes are inferred from particle-level or ensemble-level observables, such as arrival time, dispersion, and spatial distribution. Subsequently, various representative NPbased vascular sensing techniques are critically reviewed under the same umbrella of NPT. Furthermore, a benchtop proof-ofconcept is presented, which is based on controlled NP injection, optical tracking, and simplified vascular phantoms, together with simulation-assisted NP signature analysis for stenosis inference. It represents an early-stage NPT prototype under controlled experimental conditions, rather than a fully developed clinical imaging system. By looking into both the potentials and constraints of transport-based vascular inference, this perspective highlights directions for future NP-enabled diagnosis and sensing.
Molecular communication is redefining information transfer across nano-, micro-, and macroscale regimes by integrating principles from biology and engineering to enable programmable, biologically compatible signaling in environments inaccessible to conventional electromagnetic systems. This survey adopts a device-level perspective by integrating three core dimensions that shape MC research. First, we summarize advances in theoretical foundations, including molecular channel modeling, emerging modulation schemes, and detection strategies. Second, we review experimental testbeds across air-based, liquid-based, and biological platforms, emphasizing practical constraints and the limited experimental validation of existing models. Third, we examine recent biomedical applications of MC, such as neuromodulation, programmable therapeutic delivery, and in-vivo monitoring. By integrating insights across these dimensions, we identify key limitations, knowledge gaps, and open challenges that hinder the realization of deployable, device-level MC systems. Finally, we outline future research directions toward integrated, energy-efficient, biocompatible, and experimentally validated MC devices.
This mini-review covers experimental work mainly carried out by our group aiming to the engineering of chemical communication between micro/nanoparticles and between micro/nanoparticles and cells. We exploit two main types of particle platforms – gated mesoporous silica nanoparticles and giant unilamellar vesicles – which are equipped with information processing capabilities. On the one hand, this work provides fundamental insights and experimental platforms for assembling and testing communication models. Moreover, proof-of-concept applications in the biomedical context are highlighted. We believe that the development of particles with communication capabilities holds great potential for the development of applications at the interface of different disciplines – including information and communications technologies, nanotechnology, biotechnology, and biomedicine.
