This paper introduces an approach to quantum neural networks that combines the principles of data... more This paper introduces an approach to quantum neural networks that combines the principles of data re-uploading and entanglement. Based on the Orchestrated Objective Reduction (Orch OR) theory proposed by Roger Penrose and Stuart Hameroff, the study explores how quantum mechanical processes can improve neural network capabilities. By reuploading classical data at different stages of computation and utilizing quantum entanglement, the proposed network aims to achieve advanced information processing and learning abilities. This approach not only enhances the network's performance but also provides insights into the potential quantum basis of consciousness. The incorporation of these quantum operations within a feedback loop further enhances the learning process, potentially resulting in emergent behaviours reminiscent of consciousness.
In this paper, I introduce a novel approach to quantum neural networks, drawing inspiration from ... more In this paper, I introduce a novel approach to quantum neural networks, drawing inspiration from the Orchestrated Objective Reduction (Orch OR) theory by Roger Penrose and Stuart Hameroff. This theory, intersecting with quantum mechanics on a substantial scale, posits that consciousness arises from quantum processes within the brain's microtubules. Extending this concept, my research proposes a quantum neural network designed to emulate these processes computationally, aiming to achieve emergent properties similar to those observed in natural consciousness. The core of this research involves constructing a network that utilizes quantum operations, activation functions, and a feedback loop. These elements are meticulously coordinated to enhance the network's information-processing capabilities in ways that resonate with theoretical models of consciousness. A pivotal aspect of my exploration is the hypothesis that by scaling the network-specifically, by fostering extensive entanglement among multiple neurons equipped with quantum functions-the system evolves to manifest behaviours reminiscent of consciousness. This study is distinctive in its application of Orch OR theory to the field of artificial intelligence via quantum computing. It capitalizes on quantum mechanics' inherent properties, like superposition and entanglement, to offer a fresh viewpoint on how quantum neural networks might simulate intricate, consciousness-like phenomena. The goal of this paper is dual-purpose: to push the boundaries of quantum neural network technology and to establish a novel paradigm for comprehending the quantum mechanical facets of consciousness. Through this endeavour, it aims to bridge a significant divide between theoretical physics and computational practice, charting a new course for delving into the mysterious realm of consciousness with the aid of sophisticated quantum computing technologies. Central to this ambition is the network's scaling, where the increase in interconnected, entangled neurons crafts a fully integrated system, mirroring the complexity and integrated information processing seen in theories of consciousness emergence.
This paper introduces an approach to quantum neural networks that combines the principles of data... more This paper introduces an approach to quantum neural networks that combines the principles of data re-uploading and entanglement. Based on the Orchestrated Objective Reduction (Orch OR) theory proposed by Roger Penrose and Stuart Hameroff, the study explores how quantum mechanical processes can improve neural network capabilities. By reuploading classical data at different stages of computation and utilizing quantum entanglement, the proposed network aims to achieve advanced information processing and learning abilities. This approach not only enhances the network's performance but also provides insights into the potential quantum basis of consciousness. The incorporation of these quantum operations within a feedback loop further enhances the learning process, potentially resulting in emergent behaviours reminiscent of consciousness.
In this paper, I introduce a novel approach to quantum neural networks, drawing inspiration from ... more In this paper, I introduce a novel approach to quantum neural networks, drawing inspiration from the Orchestrated Objective Reduction (Orch OR) theory by Roger Penrose and Stuart Hameroff. This theory, intersecting with quantum mechanics on a substantial scale, posits that consciousness arises from quantum processes within the brain's microtubules. Extending this concept, my research proposes a quantum neural network designed to emulate these processes computationally, aiming to achieve emergent properties similar to those observed in natural consciousness. The core of this research involves constructing a network that utilizes quantum operations, activation functions, and a feedback loop. These elements are meticulously coordinated to enhance the network's information-processing capabilities in ways that resonate with theoretical models of consciousness. A pivotal aspect of my exploration is the hypothesis that by scaling the network-specifically, by fostering extensive entanglement among multiple neurons equipped with quantum functions-the system evolves to manifest behaviours reminiscent of consciousness. This study is distinctive in its application of Orch OR theory to the field of artificial intelligence via quantum computing. It capitalizes on quantum mechanics' inherent properties, like superposition and entanglement, to offer a fresh viewpoint on how quantum neural networks might simulate intricate, consciousness-like phenomena. The goal of this paper is dual-purpose: to push the boundaries of quantum neural network technology and to establish a novel paradigm for comprehending the quantum mechanical facets of consciousness. Through this endeavour, it aims to bridge a significant divide between theoretical physics and computational practice, charting a new course for delving into the mysterious realm of consciousness with the aid of sophisticated quantum computing technologies. Central to this ambition is the network's scaling, where the increase in interconnected, entangled neurons crafts a fully integrated system, mirroring the complexity and integrated information processing seen in theories of consciousness emergence.
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Papers by Tom McIver