Exploring New Chemical Paths in Quantum AI: Preliminary Accomplishments and Future Perspectives

Abstract Quantum Artificial Intelligence (QAI) refers to the integration of AI with quantum information science, with the dual objective of accelerating AI development and advancing quantum computing, sensing, and communication technologies. In this work, two innovative strategies for the development of an energetically sustainable quantum information science are proposed. Both strategies originate from the Chemical AI (CAI) research line, which aims to emulate biological intelligence by designing engineered chemical systems in liquid solutions, the characteristic phase of life. The first strategy involves implementing chemical fuzzy sets and processing fuzzy logic at the molecular level, which corresponds to manipulating thermalized quantum mixed states. Although quantum superposition, interference, entanglement, and tunneling are excluded, noteworthy advancements in quantum sensing, communicating, and computing are still within reach. The second strategy arises from neuromorphic engineering in wetware. In this context, photochromic compounds are employed as dynamic surrogates of neurons operating in the phasic excitable regime. These compounds are proposed as molecular candidates that can simulate certain features of qubits. Finally, perspectives on these two novel strategies, highlighting their potential to reshape the future of quantum information science beyond traditional qubit‐based paradigms, are outlined.