This project aims to investigate fundamental questions around the debate of the nature of artificial and human intelligence, specifically by asking questions around the role of quantum effects in enhancing the ability for cognition. From a purely scientific perspective, the project will be involve the study of how quantum networks scale in complexity and compute power. There are however, deeper philosophical motivations for this work related eg to the nature of consciousness in AI and natural neuronal networks. 

Despite centuries of debate, the nature and origin of consciousness remain unknown. While the discussion has long been philosophical, it is now shifting toward empirically grounded approaches, also largely motivated by the rapid development of large AI systems that prompt the question as to whether these may one day, or already, exhibit some forms of consciousness. 

We will start from the only mathematically defined framework of consciousness — Integrated Information Theory (IIT) — and apply it to artificial systems that can be simulated and physically built. Using photonic quantum reservoir computers [1,2], which can operate in both classical and quantum regimes with identical architectures, we treat IIT as a complexity metric quantifying consciousness. We will also consider more general measures of complexity that are still related to IIT but are more amenable to calculation in real-world situations [3]. 

By systematically increasing the complexity of these systems, we will compare how classical and quantum architectures differ in their complexity measures, establishing their respective scaling laws. Once characterized, these laws can be applied to biological systems such as neuron cultures and brain organoids, to test whether their scaling behaviour aligns with classical or quantum signatures of complexity by establishing if they scale within classical or quantum bounds.  

This project will have both a strong theoretical and experimental component but can also be tailored in the balance between these two components, depending on the specific skills and interests of the student.