QKSA: Quantum Knowledge Seeking Agent

TL;DR

The paper introduces QKSA, a versatile reinforcement learning agent designed to model classical and quantum dynamics, integrating ideas from artificial general intelligence, constructor theory, and genetic programming for environment modeling.

Contribution

It presents a novel framework combining multiple theories to create a general learning agent capable of modeling complex physical environments, including quantum processes.

Findings

Proposes a formalism for quantum process tomography

Defines a resource-bounded computational model for environment understanding

Lays groundwork for implementing QKSA in various environments

Abstract

In this article we present the motivation and the core thesis towards the implementation of a Quantum Knowledge Seeking Agent (QKSA). QKSA is a general reinforcement learning agent that can be used to model classical and quantum dynamics. It merges ideas from universal artificial general intelligence, constructor theory and genetic programming to build a robust and general framework for testing the capabilities of the agent in a variety of environments. It takes the artificial life (or, animat) path to artificial general intelligence where a population of intelligent agents are instantiated to explore valid ways of modelling the perceptions. The multiplicity and survivability of the agents are defined by the fitness, with respect to the explainability and predictability, of a resource-bounded computational model of the environment. This general learning approach is then employed to model the physics of an environment based on subjective observer states of the agents. A specific case of quantum process tomography as a general modelling principle is presented. The various background ideas and a baseline formalism are discussed in this article which sets the groundwork for the implementations of the QKSA that are currently in active development.