This paper describes work that seeks to take the first steps towards demonstrating the application of Sci-Fi prototyping methodology to developing futuristic products. Science-Fiction Prototyping is based on an iterative interaction between science fiction and fact. We begin the paper by explaining the science-fiction prototyping process. We then identify a futuristic product to act as an exemplar of this process (free willed domestic robots) before describing virtual-reality systems and how they can provide a suitable visualisation environment for conceptualizing products. Next we identify a paper that provides a promising approach to emulating intelligent control and free-will, quantum computing. We then provide an introduction to quantum computing and argue the need for a special quantum development environment before presenting an architecture for the quantum development tools and the robot. Finally we present an evaluation methodology before summarising our work. We are in the process of implementing this system and we hope that by the time the workshop occurs, we will be able to report some initial results.
This paper describes work that seeks to take the first steps towards demonstrating the application of Sci-Fi prototyping methodology to developing futuristic products. Science-Fiction Prototyping is based on an iterative interaction between science fiction and fact. We begin the paper by explaining the science-fiction prototyping process. We then identify a futuristic product to act as an exemplar of this process (free willed domestic robots) before describing virtual-reality systems and how they can provide a suitable visualisation environment for conceptualizing products. Next we identify a paper that provides a promising approach to emulating intelligent control and free-will, quantum computing. We then provide an introduction to quantum computing and argue the need for a special quantum development environment before presenting an architecture for the quantum development tools and the robot. Finally we present an evaluation methodology before summarising our work. We are in the process of implementing this system and we hope that by the time the workshop occurs, we will be able to report some initial results.
Quantum computing establishes a completely new method for performing calculations, which relies on three fundamental principles called superposition, entanglement, and probabilistic measurement. The application of artificial intelligence (AI) with quantum computing provides the ability to develop quantum AI (QAI) agents, which function as intelligent systems using quantum-based methods for representation, optimization, and probabilistic modeling. The research study analyzed the mathematical foundations of quantum computing, together with its key quantum algorithms used in AI and the architectural designs of hybrid quantum–classical agents, alongside quantum learning models and the complete implementation framework, which includes hardware systems, programming methods, and scalability challenges. The study examined different essential obstacles, which included decoherence, data encoding bottlenecks, gradient instability, benchmarking methods, and the comparison between theoretical and practical quantum advantage. Current quantum computing hardware exists within the noisy intermediate-scale quantum computing stage, which offers scientists various ways to investigate hybrid models and variational methods. Scalable and fault-tolerant quantum computing development will enable businesses to achieve groundbreaking results across various fields, including optimization, probabilistic reasoning, and intricate decision-making. The field of AI needs hardware engineering, algorithm advancement, error correction, and assessment methodology development before it can achieve practical quantum advantage. QAI represents an emerging interdisciplinary research frontier that integrates physics, computer science, mathematics, and ethics into a coherent research agenda.
Over the last 65 years, Artificial Intelligence has successfully transformed robots from simple machines into intelligent autonomous agents. However, as robotics enters high-stakes fields like "Quick Commerce" and complex engineering, classical computing relying on binary bits is hitting a massive "computational ceiling." These traditional systems are simply too slow to process the colossal, high-dimensional data required for instant decision-making. This paper reviews the evolution of AI and identifies the specific hardware bottlenecks limiting modern autonomy. To bridge this gap, we propose "Quantum Power" leveraging qubits, superposition, and entanglement as the essential engine for the next generation of robotics. By synthesizing the interplay between AI and Quantum Computing, this research demonstrates how a hybrid approach can overcome current logistical barriers, moving us toward a future of faster, smarter, and truly symbiotic quantum-powered robots. Despite its promising advantages, the implementation of quantum-AI integration faces several challenges, including limited availability of quantum hardware, noise sensitivity, and the need for hybrid quantum-classical frameworks. The study highlights ongoing research efforts and future directions aimed at overcoming these limitations. Overall, the fusion of quantum computing and AI offers a novel and transformative approach to developing intelligent robotic systems with superior learning and decision-making capabilities.
Over the last 65 years, Artificial Intelligence has successfully transformed robots from simple machines into intelligent autonomous agents. However, as robotics enters high-stakes fields like "Quick Commerce" and complex engineering, classical computing relying on binary bits is hitting a massive "computational ceiling." These traditional systems are simply too slow to process the colossal, high-dimensional data required for instant decision-making. This paper reviews the evolution of AI and identifies the specific hardware bottlenecks limiting modern autonomy. To bridge this gap, we propose "Quantum Power" leveraging qubits, superposition, and entanglement as the essential engine for the next generation of robotics. By synthesizing the interplay between AI and Quantum Computing, this research demonstrates how a hybrid approach can overcome current logistical barriers, moving us toward a future of faster, smarter, and truly symbiotic quantum-powered robots. Despite its promising advantages, the implementation of quantum-AI integration faces several challenges, including limited availability of quantum hardware, noise sensitivity, and the need for hybrid quantum-classical frameworks. The study highlights ongoing research efforts and future directions aimed at overcoming these limitations. Overall, the fusion of quantum computing and AI offers a novel and transformative approach to developing intelligent robotic systems with superior learning and decision-making capabilities.
The possibility of integrating quantum computation in a traditional system appears to be a viable route to drastically improve the performance of systems endowed with artificial intelligence. An example of such processing consists of implementing a teleo-reactive system employing quantum computing. In this work, we considered the navigation of a robot in an environment where its decisions are drawn from a quantum algorithm. In particular, the behavior of a robot is formalized through a production system. It is used to describe the world, the actions it can perform, and the conditions of the robot’s behavior. According to the production rules, the planning of the robot activities is processed in a recognize–act cycle with a quantum rule processing algorithm. Such a system aims to achieve a significant computational speed-up.
Using the effects of quantum mechanics for computing challenges has been an often discussed topic for decades. The frequent successes and early products in this area, which we have seen in recent years, indicate that we are currently entering a new era of computing. This paradigm shift will also impact the work of robotic scientists and the applications of robotics. New possibilities as well as new approaches to known problems will enable the creation of even more powerful and intelligent robots that make use of quantum computing cloud services or co-processors. In this position paper, we discuss potential application areas and also point out open research topics in quantum computing for robotics. We go into detail on the impact of quantum computing in artificial intelligence and machine learning, sensing and perception, kinematics as well as system diagnosis. For each topic we point out where quantum computing could be applied based on results from current research.
The series «The Role of Artificial Intelligence and Distributed Computing in IoT Applications» contains publications on the theory and applications of distributed computing and artificial intelligence in the Internet of Things. Virtually all disciplines such as engineering, natural sciences, computer and information sciences, ICT, economics, business, e-commerce, environment, health and life sciences are covered. The list of topics covers all areas of modern intelligent systems and computer science: computational intelligence, soft computing including neural networks, social intelligence, ambient intelligence, self-organising and adaptive systems, human-centred and people-centred computing, recommendation systems, intelligent control, robotics and mechatronics including human-machine collaboration, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, web intelligence, and multimedia. The publications in the framework of «The Role of Artificial Intelligence and Distributed Computing in IoT Applications» are mainly the proceedings of seminars, symposia and conferences. They cover important recent developments in the field, whether of a foundational or applicable character. An important feature of the series is the short publication time. This allows for the rapid and wide dissemination of research results.