Advances in computational intelligence and robotics book series

Quantum machine learning is strongly influenced by how classical data is embedded into quantum circuits, yet most methods rely on fixed, task-agnostic encodings that cannot adapt to problem structure. This chapter introduces the Hybrid Multi-Strategy Quantum Embedding (HMQE) framework, which integrates angle encoding, parametric entanglement, and data re-uploading into a unified, trainable archit…

Quantum-Enhanced Artificial Intelligence (QAI) integrates quantum computing with advanced AI techniques to transform computational intelligence in next-generation systems. By leveraging quantum properties such as superposition and entanglement, QAI enables faster processing, improved optimization, and enhanced learning capabilities beyond classical limits. This study highlights hybrid quantum-cla…

With high penetration of renewables, there is uncertainty, nonlinearity, and complexity of operation in modern power systems, which complicates the stability assessment and control of power systems. The chapter examines the concept of Quantum Artificial Intelligence (QAI) where quantum computing is combined with sophisticated artificial intelligence models. A hybrid scheme, Quantum-Enhanced Physi…

Quantum computing continues to make great progress, impacting not only the theory of computer science but also technological innovations. This chapter gives an overview of the subject matter, presenting the current state-of-the-art in qubit architectures, quantum algorithms, hardware achievements made since 2019 through 2024, potential applications, and difficulties ahead for wide-scale implement…

Quantum computing is transitioning from theory to engineered reality. This chapter examines key technological advances driving this shift, focusing on qubit architectures, quantum error correction (QEC), variational algorithms, and early fault-tolerant applications in drug discovery and financial optimization. Recent milestones—including error rates below the surface code threshold, high-threshol…

The potential for quantum machine learning is starting to shift from theory to well-contained industrial trials. It is most likely to be useful in situations where data for industry is not abundant, is hard to tag, high dimensional, physically structured, or is associated with challenging optimization problems. This chapter reviews some of the major architectures relevant to current industrial ap…

The convergence of quantum computing and neuromorphic engineering represents a promising frontier for next-generation artificial intelligence. Hybrid quantum-neuromorphic intelligence is emerging as an alternative to conventional von Neumann computing by combining the strengths of both paradigms. Quantum systems provide powerful capabilities for optimization, simulation, and complex problem solvi…

Superposition, entanglement, and interference are the key characteristics of quantum computing that can provide exponential speedups compared to classical computing. This chapter offers a thorough survey of the latest developments in technology that propel quantum computing from the theory to the reality. It examines prominent hardware platforms including superconducting qubits, trapped-ion syste…

To address the volatility of photovoltaic (PV) power, this study introduces the OHA-QLSTM-ATDO-SIP framework for precise solar irradiance forecasting. The system utilizes a Multi-observation Fusion Kalman Filter (MOFKF) to pre-process data from the National Solar Radiation Data Base, followed by a Quantum Long Short-Term Memory (QLSTM) network for prediction. To enhance accuracy, an Adaptive Tasm…

This chapter per the authors examines the convergence of quantum computing and artificial intelligence, focusing on the challenges and opportunities associated with their integration across modern industries. It outlines the foundational principles of quantum-enhanced computation and explores how these capabilities can extend traditional machine learning approaches. The discussion highlights key …

This study examines the transformative role of Quantum Artificial Intelligence (Quantum AI) in advancing computational intelligence through quantum-enhanced machine learning frameworks. Using a quantitative, descriptive, and analytical research design, primary data were collected from 400 respondents across academic, research, and industry domains via a structured questionnaire. The analysis appl…

Image retrieval systems rely heavily on computing similarity between high-dimensional feature vectors, an operation that grows computationally expensive as databases scale. Recent advances in quantum computing offer promising alternatives through quantum inner product algorithms. This chapter presents a comprehensive experimental comparison of two approaches: the Swap Test-based Quantum Inner Pro…

Artificial Intelligence (AI) has achieved remarkable success across diverse application domains; however, its vulnerability to adversarial attacks poses significant challenges to reliability, security, and trustworthiness. Adversarial machine learning demonstrates that even highly accurate models can be manipulated through carefully crafted perturbations, raising serious concerns in safety-critic…

The legal strategy that incorporates cutting-edge concepts like artificial intelligence and green finance with established laws like the Insolvency and Bankruptcy Code and the Real Estate (Regulation and Development) Act (RERA). It need to develop long-term solutions since real estate projects are at a standstill. Money issues, regulation infractions, or developer bankruptcies cause many projects…

Artificial intelligence is particularly causing a rapid revitalization in automation of sustainability practices, especially in the aspects of environmental, social and governance (ESG) regulation and green-finance law. Using machine learning and natural language processing and predictive analytics, AI helps to conduct the efficient aggregation of data of unstructured origins, real-time monitorin…

In light of the widespread use of AI Large Language Model chatbots such as ChatGPT-5, this article summarizes eight use cases of chatbots in the discipline of Evolutionary Culturology to address science education. The custom GPTs created in ChatGPT-4 and 5 include: (1) The Evolutionary Culturology Tutor GPT, (2) The Evolutionary Culturology Popper Quote Explainer GPT, (3) The Evolutionary Culturo…

A key pillar of green investment has become regulatory compliance, where financial institutions are required to fund a portfolio in a way that reflects a sustainability requirement, as well as exhibit a clearly proven impact on the environment. The purpose of disclosure is entailed in such elements as a global frameworks, namely a formal regulation such as the EU Sustainable Finance Disclosure Re…

The emerging breakthrough in technology in artificial intelligence and automation technologies present incredible opportunities to drive the truly green investment in India specifically in the spheres of renewable energy, sustainable finance and climate-resilient infrastructure. These tools will facilitate faster allocation of resources to environmentally friendly projects by reducing the risk of…

The real estate sector has particular challenges when someone files for bankruptcy. The process might take a long time if it is not halted immediately, and production assets gradually lose value over time. Since the cost of bankruptcy with real estate has been declining in all regions for some time and is generally agreed, it investigate the relationship between green finance and artificial intel…

The application of AI in green finance has enormous potential to tackle climate change, but its effect would be determined by the strength and integrity of governance mechanisms as well as well-designed incentives. Good policy serves to set the bar but also incentives and responsible use in finance in AI applications. In the absence of such governance, exposures including algorithmic discriminati…