An algebraic correspondence between an imaging model and a quantum state does not specify a computational method: input access, measurement and decoding determine what can be returned. This exposition develops four distinctions for computational-imaging and engineering readers. It separates amplitude encodings of input values from encodings of complete candidate labelings, explaining why amplitude storage does not provide full-array readout. Complex-field interference motivates unknown-phase inference: complementary measurements resolve ambiguities that local Fisher information alone does not detect. Reshaping joint amplitudes into a matrix connects singular-value decomposition to pure-state entanglement, while distinguishing Schmidt rank from conditions for efficient classical simulation. In a four-pixel segmentation, an objective becomes a cost operator and a sampling circuit. One layer of the quantum approximate optimization algorithm (QAOA) lowers mean energy from $1.675$ to $0.686$ relative to uniform sampling, yet makes a nonoptimal labeling more probable than the optimum 0.484 versus 0.365. Archived IBM and IQM measurements illustrate a separate six-variable local boundary decision. Low-rank state reconstruction and selected quantum algorithms connect these distinctions to an end-to-end resource account. The derivations and reproducible numerical references assume complex linear algebra and probability, without prior quantum mechanics. The accompanying Seeing Amplitudes browser laboratory supports interactive exploration; the article remains self-contained and connects each representation to the estimate or decision it permits and the resources that output requires.
MPC (Medicinal Plant Classification) has significance in drug discovery, herbal and biodiversity conservation. Conventional ways of identification tend to be sluggish and inaccurate, novel AI-powered systems are required. This survey presents hybrid deep models that use CNNs including methodologies, like metaheuristic optimization and score voting of ensemble-forming. Different deep learning models are studied in performance of feature extraction and classification, i.e., ResNet, DenseNet, EfficientNet, and Vision Transformers (ViT). These papers have utilized metaheuristic e.g. Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Grey Wolf Optimiser (GWO) etc., as an aid to optimising the performance of a model by using hyperparameter and feature selection methods. Some of the most applicable techniques are ensemble methods and they combine two or more classifiers to enhance the reliability of decisions with the most popular one being score voting. Arguments on the most critical issues in applying these algorithms (i.e. computational complexity, overfitting, data availability, etc.) are presented relative to the provision of future research directions like quantum/computing, federated learning, and multi-modal classification. It illustrates how hybrid AI systems can be used to identify medicinal plants with a power that has never been explored before to use in botany and health care with efficient, accurate and scalable classification tools.
We present a scheme for implementing a high-fidelity non-linear phase shift on a photonic state. The scheme is based on repeated scattering off a two-level quantum emitter embedded in a chiral or one-sided waveguide. The waveguide is equipped with elements inducing second-order dispersion and temporal phase shifts, which effectively form a harmonic trap and confine the photon pulses to a Gaussian shape. The same quantum emitter can be used for each scattering, and thus, only one quantum emitter is needed in this scheme. To illustrate the application of our scheme for photonic quantum computing and quantum communication, we analyze the implementation of a control-Z gate and a deterministic Bell-state analyzer for photonic qubits. Through numerical optimization, we show that we can reach a control-Z gate fidelity of $\mathcal{F} \sim 99.2\%$ ($\mathcal{F} \sim 96\%$) and a success probability of $P_s \sim 99.6 \%$ ($P_s\sim 98 \%$) for a Bell-state measurement with $N=17$ ($N=5$) scatterings.
The recent innovation in the military technology has changed the face of the contemporary warfare greatly to make it more intelligent, autonomous, and data-driven systems as opposed to the human-oriented and mechanically controlled ones. In this regard, Quantum Computing and Artificial Intelligence intersecting to create what is alternatively known as Quantum Artificial Intelligence (Quantum AI) has become a mighty facilitator of next- generation military autonomous robotics and advanced mechanical systems. It is a chapter that provides an in-depth discussion of how Quantum AI can contribute to the improvement of intelligence, changing capabilities, and the efficiency of autonomous military capability. The chapter starts with a history of military robotics and mechanical systems, noting that the early systems were controlled through rigid mechanical means and autonomous control on predetermined rules, but nowadays, autonomous systems are driven by AI. It then presents the basic concepts of quantum computing including superposition, entanglement, and quantum interference and how it applies to military decision-making and optimization issues. It is based on this that the chapter describes the fundamentals, models, and architectures of Quantum Artificial Intelligence, such as quantum machine learning, quantum optimization, and hybrid quantum-classical systems. One of the key aspects that have been emphasized in the chapter is the combination of Quantum AI and autonomous robotics and sophisticated mechanical systems. It addresses the role of quantum- enhanced perception, decision-making, path planning, and swarm coordination in enhancing the performance and resiliency of unmanned aerial and ground systems, as well as the role of unmanned naval systems. Moreover, the chapter emphasizes on the applications of Quantum AI in adaptive mechanical control, predictive maintenance, and fault-tolerant operations in hard battlefield conditions. Some of the main military use- cases and system-level architectures are briefly discussed to illustrate their practical applicability. The chapter is also concerned with critical issues and gaps in research on the topic of quantum hardware constraints, scalability in real time, cybersecurity concerns, and ethical governance of autonomous military systems. Lastly, it gives an outlook of the future where the autonomous robotics and mechanical systems with Quantum AI become the pillars of intelligence- centric warfare. Altogether, this chapter defines Quantum AI as a strategic and technological requirement towards making superior decisions, delivering resilient operations, and next-generation military capabilities.
HR7/PFA Prospective Validation Series II — M3: 90-Day Frozen Validation Report This report presents the third prospective validation checkpoint (M3) of the HR7 Historical Reference Framework and Predictive Feasibility Assessment (PFA), developed by independent researcher Jos Aben. The study follows eleven previously frozen assessments across artificial intelligence, biomedical research, robotics, cybersecurity, quantum computing, genomics, and scientific machine learning. The objective is to examine whether technological developments observed over 90 days remain consistent with the original feasibility classifications, identified uncertainties, and predefined evidence requirements. Key findings: All eleven original feasibility classifications remained unchanged in the provisional assessment. Four cases demonstrated material new intermediate evidence, two showed supporting infrastructure or accessibility developments, one produced mixed findings, and four remained consistent without decisive new validation. Fourteen previously frozen structural forecasts were evaluated: four provisionally confirmed, eight partially confirmed, two unresolved, and none contradicted. Technological and scientific progress frequently preceded independent clinical, industrial, economic, or operational validation. The findings provide qualitative prospective support for HR7/PFA as a structured framework for distinguishing technological capability, evidence maturity, and deployment readiness. However, the study does not yet establish statistical predictive superiority over conventional expert assessment or alternative technology-readiness methodologies. The current assessment remains subject to final source-cutoff and original forecast-register verification. A further independent observation checkpoint is proposed for 25 December 2026, extending the longitudinal evaluation to 150 days. Author: Jos Aben — Independent Researcher, TARH Energy. Previous frozen records: Original T0 Assessment: DOI 10.5281/zenodo.21610724 M1 30-Day Validation: DOI 10.5281/zenodo.22113487
