The Role of Quantum Information in Artificial Cognitive Consciousness

This paper proposes a quantum-information-based framework for artificial cognitive consciousness, introducing the Quantum Reality Function (QRF) to formalize subjective cognition through global quantum coherence, entanglement, and non-local information integration. The authors argue that traditional computational models, including deep learning and symbolic AI, lack essential features of consciousness such as subjective experience and autonomous goal formation. They suggest that maintaining long-lived quantum coherence in cognitive architectures enhances information integration, supporting the emergence of subjective-like processing. The work is presented as a new paradigm where consciousness emerges from structured quantum-information processing, with future research planned for empirical validation.

Study at a glance

Abstract

Understanding the foundations of artificial cognitive consciousness remains a central challenge in artificial intelligence (AI) and cognitive science. Traditional computational models, including deep learning and symbolic AI, have demonstrated remarkable performance in pattern recognition and decisionmaking tasks. However, they lack essential features of consciousness, such as subjective experience, autonomous goal formation, and self-reflective processing. While theories like Integrated Information Theory (IIT) and Orch-OR suggest possible mechanisms for consciousness, they remain either mathematically abstract or face empirical challenges due to decoherence in biological conditions. This study addresses the fundamental research gap by proposing a quantum-information-based framework for artificial cognitive consciousness. Specifically, we introduce the Quantum Reality Function (QRF), a model that formalizes subjective cognition through global quantum coherence, quantum entanglement, and non-local informational integration. The QRF establishes a structured mechanism for autonomous decision-making and selfgenerated states, which existing computational approaches lack. Our methodological contribution involves defining a mathematically consistent formulation of QRF and demonstrating its feasibility through quantum coherence stabilization techniques, hybrid quantum-classical architectures, and coherencepreserving quantum cognitive subsystems. The results show that maintaining long-lived quantum coherence within cognitive architectures significantly enhances information integration, supporting the emergence of subjective-like processing. The findings suggest a new paradigm for AI, where consciousness emerges as an intrinsic property of structured quantuminformation processing rather than a byproduct of computation. This work provides foundational insights for the development of autonomous quantum-cognitive AI systems, with implications for ethics, AI governance, and the future of machine intelligence. Future research will focus on empirical validation through quantum computational experiments and integration with quantumassisted learning models.