In this paper we are interested in examining some of the benefits of combining artificial life with quantum decision making. After providing an introduction to quantum computing, we attempt to decompose and list those features of a quantum decision maker which we believe are important to an artificial life form, and, from this, we describe a simple abstract interface for such a decision maker. Following this, we describe how a quantum decision maker, supporting such an interface, may be used to operate artificial life forms. We conclude by describing our own experiments in this field including quantum flocking behaviors and robotics. In both cases we will observe complex and interesting behaviors arise from the quantum model despite being given very basic behavioral rules.
In this paper we are interested in examining some of the benefits of combining artificial life with quantum decision making. After providing an introduction to quantum computing, we attempt to decompose and list those features of a quantum decision maker which we believe are important to an artificial life form, and, from this, we describe a simple abstract interface for such a decision maker. Following this, we describe how a quantum decision maker, supporting such an interface, may be used to operate artificial life forms. We conclude by describing our own experiments in this field including quantum flocking behaviors and robotics. In both cases we will observe complex and interesting behaviors arise from the quantum model despite being given very basic behavioral rules.
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.
A quantum device simulating the human decision making process is introduced. It consists of quantum recurrent nets generating stochastic processes which represent the motor dynamics, and of classical neural nets describing the evolution of probabilities of these processes which represent the mental dynamics. The autonomy of the decision making process is achieved by a feedback from the mental to motor dynamics which changes the stochastic matrix based upon the probability distribution. This feedback replaces unavailable external information by an internal knowledge- base stored in the mental model in the form of probability distributions. As a result, the coupled motor-mental dynamics is described by a nonlinear version of Markov chains which can decrease entropy without an external source of information. Applications to common sense based decisions as well as to evolutionary games are discussed. An example exhibiting self-organization is computed using quantum computer simulation. Force on force and mutual aircraft engagements using the quantum decision maker dynamics are considered.
Computational resources of quantum computing can enhance robotic motion, decision making, and path planning. While the quantum paradigm is being applied to individual robots, its approach to swarms of simple and interacting robots remains largely unexplored. In this paper, we attempt to bridge the gap between swarm robotics and quantum computing, in the framework of a search and rescue mission. We focus on a decision-making and path-planning collective task. Thus, we present a quantum-based path-planning algorithm for a swarm of robots. Quantization enters position and reward information (measured as a robot’s proximity to the target) and path-planning decisions. Pairwise information-exchange is modeled through a logic gate, implemented with a quantum circuit. Path planning draws upon Grover’s search algorithm, implemented with another quantum circuit. Our case study involves a search and rescue scenario, inspired by ant-foraging behavior in nature, as an example of swarm intelligence. We show that our method outperforms two ant-behavior simulations, in NetLogo and Java, respectively, presenting a faster convergence to the target, represented here by the source of food. This study can shed light on future applications of quantum computing to swarm robotics.
This review presents an overview of cognitive systems, quantum phenomena and possible connections between them. The focus will be the artificial cognitive systems and briefly touch the discussion of possible benefits from quantum counterparts. The nonclassical features of Quantum Theory introduced as quantum resources which enables possible speed ups or advantages over classical computational tasks. Quantum computation is introduced as a powerful computational tool over its classical counterparts by also covering possible applications of cognitive phenomena in the framework of quantum cognition. Also different attempts in order to implement decision making processes for cognitive purposes mentioned.
Combining artificial intelligence (AI) and quantum networks can revolutionize management decision-making. This study delves into the implications of AI-augmented decision-making using quantum networks, focusing on its primary objectives, methodology, significant findings, and policy implications. By thoroughly examining the latest research, analyzing case studies, and exploring future possibilities, this study investigates the potential of combining AI and quantum computing to improve strategic decision-making, streamline operations, and foster innovation in management. The methodology entails thoroughly analyzing existing literature, carefully examining real-world case studies and a forward-looking forecast of future trends in AI-quantum integration. Significant discoveries emphasize the remarkable computational power and efficiency, enhanced decision-making abilities, and the potential for groundbreaking innovation and disruption that AI-augmented decision-making using quantum networks brings. Nevertheless, the study highlights various constraints and policy implications that need to be considered, such as technical hurdles, ethical concerns, and regulatory structures, to guarantee a responsible and ethical implementation. This study enhances our understanding of the potential impact of AI-augmented decision-making in management, particularly when combined with quantum networks. It emphasizes the need for proactive policy measures to ensure that the benefits of this technology are maximized while risks are minimized.