Hamiltonian Formalism for Comparing Quantum and Classical Intelligence
This work addresses the need for a precise framework to compare quantum and classical AGI, which is foundational for understanding potential advantages in AGI development, though it appears incremental as it builds on existing Hamiltonian concepts without demonstrating new empirical results.
The authors tackled the problem of comparing quantum and classical artificial general intelligence (AGI) by introducing a Hamiltonian formalism to describe AGI tasks, decomposing dynamics into generators for functions like induction and learning, aiming to provide a mathematical language for contrasting their environmental interactions.
The prospect of AGI instantiated on quantum substrates motivates the development of mathematical frameworks that enable direct comparison of their operation in classical and quantum environments. To this end, we introduce a Hamiltonian formalism for describing classical and quantum AGI tasks as a means of contrasting their interaction with the environment. We propose a decomposition of AGI dynamics into Hamiltonian generators for core functions such as induction, reasoning, recursion, learning, measurement, and memory. This formalism aims to contribute to the development of a precise mathematical language for how quantum and classical agents differ via environmental interaction.